HUNGARIAN JOURNAL OF INDUSTRY AND CHEMISTRY Vol. 48(2) pp. 1–64 (2020) hjic.mk.uni-pannon.hu Contents Combination of Chemical and Biological Methods for Effective Plant Protection DÁVID VOZIK AND KATALIN BÉLAFI-BAKÓ 1–4 Enhancement of Oxygen Transfer Through Membranes in Bioprocesses PÉTER KOMÁROMY, KATALIN BÉLAFI-BAKÓ, ÉVA HÜLBER-BEYER, AND NÁNDOR NEMESTÓTHY 5–8 The Role of Water Activity in Terms of Enzyme Activity and Enantioselectivity During Enzymatic Esterification in Non-Conventional Media PIROSKA LAJTAI-SZABÓ, NÁNDOR NEMESTÓTHY, AND LÁSZLÓ GUBICZA 9–12 Follow-up Control of a Second Order System in Sliding Mode MÁRK DOMONKOS AND NÁNDOR FINK 13–21 Informative Environment Qualifying Index ANETT UTASI, VIKTOR SEBESTYÉN, AND ÁKOS RÉDEY 23–36 The Effect of pH on Biosurfactant Production by Bacillus Subtilis DSM10 RÉKA CZINKÓCZKY AND ÁRON NÉMETH 37–43 Adsorption of Nickel Ions From Petroleum Wastewater Onto Calcined Kaolin Clay: Isotherm, Kinetic and Thermodynamic Studies ALEXANDER ASANJA JOCK, ANIETIE NDARAKE OKON, UCHECHUKWU HERBERT OFFOR, FESTUS THOMAS, AND EDMOND OKWUDILICHUKWU AGBANAJE 45–49 Considerations to Approach Membrane Biofouling in Microbial Fuel Cells SZABOLCS SZAKÁCS AND PÉTER BAKONYI 51–53 New Chaining Criteria in Dipolar Fluids Based on Monte Carlo Simulations SÁNDOR NAGY 55–58 Temperature and Electric Field Dependence of the Viscosity of Electrorheological (ER) Fluids: Warming Up of an Electrorheological Clutch SÁNDOR MESTER AND ISTVÁN SZALAI 59–64 HUNGARIAN JOURNAL OF INDUSTRY AND CHEMISTRY Vol. 48(2) pp. 1–4 (2020) hjic.mk.uni-pannon.hu DOI: 10.33927/hjic-2020-20 COMBINATION OF CHEMICAL AND BIOLOGICAL METHODS FOR EFFECTIVE PLANT PROTECTION DÁVID VOZIK 1 AND KATALIN BÉLAFI-BAKÓ*1 1Research Institute on Bioengineering, Membrane Technology and Energetics, University of Pannonia, Egyetem u. 10, Veszprém, 8200, HUNGARY The application of combined biological and chemical techniques to control undesirable processes in plant cultivation is be- coming ever more important to maintain sustainable and environmentally friendly agricultural systems. Entomopathogenic nematodes and bacteria can provide an effective and special technology in the field of plant protection. Keywords: antimicrobial, entomopathogenic nematodes, bacteria 1. Introduction Nowadays, one of the most important challenges world- wide is the sufficient cultivation of edible plants and crops due to the rising trend in population growth and food demand. Moreover, it is important to solve this problem by implementing sustainable agriculture which prefers natural protection and tries to minimize the inter- vention of radical chemicals. It was confirmed recently that some synthetic pesticides are environmental hazards. Since the biological degradation of certain pesticides is slow, their bioaccumulation might cause significant dam- age to ecosystems, soil, natural waters, etc. Therefore, the idea of protecting crops has been extended and a new con- cept, “Integrated Pest Management” (IPM), introduced which involves chemical, biological and biotechnologi- cal methods together with modern cropping, cultivation and breeding technologies [1] in a way which minimizes any risk of environmental damage. To apply this concept in practice requires compre- hensive knowledge of the crops, fitopathogenic microor- ganisms as well as their enemies, and the behaviour of chemicals (e.g. pesticides) that may be used. A field that has hardly been researched are the so-called ento- mopathogenic nematodes and bacteria which have been studied in Hungary for a significant period of time [2]. 2. Entomopathogenic nematodes and bacteria Entomopathogenic nematodes are a group of thread worms that kill certain insects. They enter – in the form of infective juveniles – into the insects, live as parasites *Correspondence: bako@almos.uni-pannon.hu inside them and cause host mortality within 1-2 days. It has turned out, however, that this is not only caused by the nematodes themselves but certain bacteria play an impor- tant role as well. Entomopathogenic bacteria live in symbiosis with en- tomopathogenic nematodes [3,4]. The nematodes provide shelter for the bacteria, an area of the interior part of the intestine of the infective juveniles is transformed into a bacterial chamber where cells of symbiotic bacteria are located. The relationship is highly specific: e.g. the nema- todes of Steinernema usually carry species of Xenorhab- dus bacteria, while Heterorhabditis carry Photorhabdus bacteria. When entering an insect, infective juveniles release the bacteria, which start multiplying rapidly in the haemolymph. It has been proven that although the bacte- ria are primarily responsible for the mortality of the insect host, the nematodes also produce a toxin which is lethal to the insect. These nematode-bacteria complexes can be applied successfully as biological control agents against insect pests in agriculture [5]. Some of these agents are available commercially, as listed in Table 1, where the target insects, habitats and places of usage are presented [6–8]. 3. Antimicrobial compounds Entomopathogenic bacteria contribute not only to the successful activity of entomopathogenic nematodes but can produce special compounds with an antimicrobial ef- fect as well. The main purpose of producing these com- pounds is to protect the colonized cadaver in the soil [9]. The antimicrobial compounds of entomopathogenic bac- teria have been tested and it would seem that these natural agents show a wide range of bioactivities of medical and https://doi.org/10.33927/hjic-2020-20 mailto:bako@almos.uni-pannon.hu 2 VOZIK AND BÉLAFI-BAKÓ Table 1: Examples of the successful application of nematode-bacteria complexes [6–8] Target Habitat Where Japanese beetle Popillia japonica subterranean lawn, turf black vine weevil Otiorhynchus sulcatus subterranean strawberry plants fungus gnats Lycoriella species, Bradysia species subterranean mushroom production diaprepes root weevil Diaprepes abbreviatus epigeal citrus invasive mole cricket Scapteriscus vicinus epigeal lawn, turf codling moth Cydia pomonella cryptic apple, pome fruit Table 2: Plant pathogens tested Pathogen Effect Ref. Phytophthora nicotianae root rot disease of tobacco [13] Erwinia amylovora fire blight disease of several plants that belong to Rosaceae, e.g. apple, pear, etc. [14] Ralstonia solanacearum brown rot disease of potato [15] agricultural interest, e.g. antibiotic, antimycotic and in- secticidal effects [10,11]. For analytical purposes, Fourier Transform Infrared spectrometry (FTIR) was used to identify these compounds [12]. In our laboratories, secondary metabolites of Xenorhabdus budapestiensis (isolated in Hungary) have been investigated [13]. The bacterium was maintained on a Luria Agar (LA) medium and freshly subcultured. The cells were cultured in 1000 mL flasks and incu- bated on a gyrorotary shaker at 25 ◦C . Then the cells were removed by centrifugation and the supernatant extracted. The cell-free filtrate was further purified by chemical methods (adsorption and filtration) to obtain a peptide-rich fraction. This fraction was applied to test for various plant pathogens, as listed in Table 2, including pathogens of tobacco, apple and potato (the latter of which is widespread in Hungary, causing serious damage to inland agriculture). The investigations involved in vitro bioassays, where the antibacterial activity of the biofraction was deter- mined on solid media. In an agar diffusion test, the tested bacterium was mixed with soft agar poured onto LA plates, then a small hole was made in the centre and the biopreparation of the filtrate added [13]. For the so-called overlay test [13], the antimicrobial preparation was incu- bated on the solid Luria Broth Agar (LA) plate then the pathogenic microorganism in soft agar was spread onto the surface. In both cases, an inhibition zone can be iden- tified and determined, if the biopreparation was effective against the pathogenic strain. An example of the agar dif- fusion bioassay is given in Fig. 1. A picture taken from a successful overlay bioassay test is shown in Fig. 2. In our laboratory, in vitro bioassays proved that the bioprepara- tions containing the antimicrobial compounds could be successfully used against these pathogens. Moreover, in planta (in field trials), a bioassay was conducted using infected apple blossom (Erwinia amylovora) which was treated with the biopreparation. It was concluded to be suitable to reduce the symptoms of the infected plants, thus it can be considered as a promis- Figure 1: Antibacterial effect of the preparation in the agar diffusion test. Figure 2: The antagonistic effect of X. budapestensis against E. amylovora EA1 bacteria in an overlay test. Hungarian Journal of Industry and Chemistry EFFECTIVE PLANT PROTECTION 3 Figure 3: Infected apple blossom treated with the bio- preparation. ing biological agent [14,15]. This successful treatment is illustrated in Fig. 3. Other than plant pathogens, the peptide-rich fraction of Xenorhabdus budapestiensis has recently been tested against species of fungi in vitro [16] in clinical samples. Candida albicans, Candida lusitaniae, Candida krusei, Candida kefyr, Candida tropicalis and Candida glabrata were used in the research by applying the agar diffusion method. The results proved that every Candida species is sensitive to the preparation, thus it would seem that the fraction has a fungicide impact as well. 4. Conclusion In summary, the symbiotic complex of entomopathogenic nematodes and bacteria can be considered as efficient bi- ological agents to control some insect pests, but the sec- ondary metabolites of the bacteria can be effectively used against other pathogenic bacteria and fungi. As a result, their applications can contribute significantly to success- ful plant protection by providing numerous environmen- tal benefits and can be considered as an important com- ponent of “Integrated Pest Management” (IPM). REFERENCES [1] Act XXXV on plant protection (2000, Hungary) http://www.kvvm.hu/szakmai/hulladekgazd/jogszabalyok/2000 _XXXV_tv.htm [2] Lengyel, K.; Lang, E.; Fodor, A.; Szállás, E.; Schu- mann, P.; Stackebrandt, E.: Description of four novel species of Xenorhabdus, family Enterobac- teriaceae: Xenorhabdus budapestensis sp. Nov., Xenorhabdus ehlersii sp. nov., Xenorhabdus innexi sp. nov., and Xenorhabdus szentirmaii sp. nov., Syst. Appl. Microbiol. 2005, 28(2), 115–122 DOI: 10.1016/j.syapm.2004.10.004 [3] Boemare, N. E.; Akhurst, R. J.; Mourant, R. G.: DNA relatedness between Xenorhabdus spp. (Enterobacteriaceae), symbiotic bacteria of ento- mopathogenic nematodes, and a Proposal to trans- fer Xenorhabdus luminescens to a new genus, Pho- torhabdus gen. nov., Int. J. Syst. Bacteriol. 1993, 43(2), 249–255 DOI: 10.1099/00207713-43-2-249 [4] Thomas, G. M.; Poinar, G. O.: Xenorhabdus gen. nov., a genus of entomrnopathogenic, ne- matophilic bacteria of the family Enterobacteri- aceae, Int. J. Syst. Bacteriol. 1979, 29(4), 352–360 DOI: 10.1099/00207713-29-4-352 [5] Smart, G. C.: Entomopathogenic nema- todes for the biological control of in- sects, J. Nematology 1995, 27(4S), 529–553 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2619649/pdf/ 529.pdf [6] Koppenhöfer, A. M.: Nematodes, in Lacey, L.A.; Kaya, H.K. (Eds.): Field manual of techniques in in- vertebrate pathology. Application and evaluation of pathogens for control of insects and other inverte- brate pests (Springer, Dordrecht, The Netherlands) 2007, ISBN: 978-1-4020-5933-9 [7] Georgis, R.; Koppenhöfer, A. M.; Lacey, L. A.; Bélair, G.; Duncan, L. W.; Grewal, P. S.; Samish, M.; Tan, L.; Torr, P.; van Tol, R. W. H. M.: Suc- cesses and failures in the use of parasitic nematodes for pest control, Biol. Control 2006, 38(1), 103–123 DOI: 10.1016/j.biocontrol.2005.11.005 [8] Lacey, L. A.; Georgis, R.: Entomopathogenic nematodes for control of insect pests above and below ground with comments on commercial production, J. Nematology 2012, 44(2), 218–225 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3578470/pdf/ 218.pdf [9] Akhurst, R. J.: Antibiotic activity of Xenorhabdus ssp., bacteria symbiotically associated with insect pathogenic nematodes of the families Heterorhab- ditidae and Steinernematidae, J. Gen. Microbiol. 1982, 128(12), 3061–3065 DOI: 10.1099/00221287-128- 12-3061 [10] McInerney, B. V.; Gregson, R. P.; Lacey, M. J.; Akhurst, R. J.; Lyons, G. R.; Rhodes, S. H.; Smith, D. R.; Engelhardt, L. M.; White, A. H.: Biolog- ically active metabolites from Xenorhabdus spp., Part 1. Dithiolopyrrolone derivatives with antibiotic activity, J. Nat. Prod. 1991, 54(3), 774–784 DOI: 10.1021/np50075a005 [11] Webster, J. M.; Chen, G.; Hun, K.; Li, J.: Bac- terial metabolites, In: Gaugler, R. (Ed.) Ento- mopathogenic Nematology (CABI Publishing, New York, USA) 2002, 145–168 ISBN: 08-5199-567-5 [12] Vozik, D.; Madarász, J.; Csanádi, Zs.; Fodor, A.; Dublecz, K.; Bélafi-Bakó, K.: Study on analysis of antibiotic compounds from en- tomopathogenic bacteria by FT-IR, Hung. J. Ind. Chem. 2012, 40(2), 83–86 https://mk.uni- pannon.hu/hjic/index.php/hjic/article/download/346/316 [13] Böszörményi, E.; Érsek, T.; Fodor, A.; Földes, L. Sz.; Hevesi, M.; Hogan, J. S.; Katona, Z.; Klein, M. G.; Kormány, A.; Pekár, Sz.; Szentirmai, A.; Sztar- icskai, F.; Taylor, R. A. J.: Isolation and activity of 48(2) pp. 1–4 (2020) http://www.kvvm.hu/szakmai/hulladekgazd/jogszabalyok/2000_XXXV_tv.htm http://www.kvvm.hu/szakmai/hulladekgazd/jogszabalyok/2000_XXXV_tv.htm https://doi.org/10.1016/j.syapm.2004.10.004 https://doi.org/10.1016/j.syapm.2004.10.004 https://doi.org/10.1099/00207713-43-2-249 https://doi.org/10.1099/00207713-29-4-352 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2619649/pdf/529.pdf https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2619649/pdf/529.pdf https://doi.org/10.1016/j.biocontrol.2005.11.005 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3578470/pdf/218.pdf https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3578470/pdf/218.pdf https://doi.org/10.1099/00221287-128-12-3061 https://doi.org/10.1099/00221287-128-12-3061 https://doi.org/10.1021/np50075a005 https://doi.org/10.1021/np50075a005 https://mk.uni-pannon.hu/hjic/index.php/hjic/article/download/346/316 https://mk.uni-pannon.hu/hjic/index.php/hjic/article/download/346/316 4 VOZIK AND BÉLAFI-BAKÓ Xenorhabdus antimicrobial compounds against the plant pathogens Erwinia amylovora and Phytoph- tora nicotianae, J. Appl. Microbiol. 2009, 107(3), 746–759 DOI: 10.1111/j.1365-2672.2009.04249.x [14] Vozik, D.; Bélafi-Bakó, K.; Hevesi, M.; Böször- ményi, E.; Fodor, A.: Effectiveness of a peptide- rich fraction from Xenorhabdus budapestensis cul- ture against fire blight disease on apple blossoms, Not. Bot. Horti Agrobot. Cluj-Napoca 2015, 43(2), 547–553 DOI: 10.15835/nbha4329997 [15] Vozik, D.; Bélafi-Bakó, K.; Hevesi, M.; Böször- ményi, E.; Polgár, Zs.; Fodor, A.: Effectiveness of antimicrobial compounds produced by ento- mopathogenic nematode symbiotic bacteria to con- trol pests and bacterial plant diseases, Interna- tional Organization For Biological Control - West Palaearctic Regional Section Bulletin 2016, 113 17–21 ISBN: 978-92-9067-296-8 [16] Burgetti-Böszörményi, E.; Németh, S.; Bélafi- Bakó, K.; Vozik, D.; Barcs, I.; Csima, Z.: The antifungal effect of Xenorhabdus budapestiensis bacteria biopreparatum on some Candida species in vitro, Dev. Health Sci. 2018, 1(7), 57–62 DOI: 10.1556/2066.2.2018.17 Hungarian Journal of Industry and Chemistry https://doi.org/10.1111/j.1365-2672.2009.04249.x https://doi.org/10.15835/nbha4329997 https://doi.org/10.1556/2066.2.2018.17 https://doi.org/10.1556/2066.2.2018.17 HUNGARIAN JOURNAL OF INDUSTRY AND CHEMISTRY Vol. 48(2) pp. 5–8 (2020) hjic.mk.uni-pannon.hu DOI: 10.33927/hjic-2020-21 ENHANCEMENT OF OXYGEN TRANSFER THROUGH MEMBRANES IN BIOPROCESSES PÉTER KOMÁROMY *1, KATALIN BÉLAFI-BAKÓ1, ÉVA HÜLBER-BEYER1, AND NÁNDOR NEMESTÓTHY1 1Research Institute on Bioengineering, Membrane Technology and Energetics, University of Pannonia, Egyetem u. 10, Veszprém, 8200, HUNGARY In several aerobic bioprocesses, it is extremely important to provide a sufficient amount of oxygen, which is a difficult task since the solubility of oxygen in aqueous systems, namely in water, is low. Thus oxygen transfer techniques should be designed, built, studied and operated very carefully in biosystems. Moreover, in certain cases, special techniques, e.g. involving membranes, must be applied. In this paper, three different bioprocesses are presented where oxygen was supplied by membrane aeration: wastewater treatment by microbial consortia (i), itaconic acid fermentation by a single microorganism (ii) and an oxidative enzyme catalytic process for the elimination of glucose (iii). Keywords: biotechnology, aerobic microbes, silicone membrane, enzyme 1. Introduction At the dawn of applied and industrial biotechnology, the use of engineering tools to establish reliable bioprocesses for manufacturing certain bioproducts was inevitable [1– 4]. For the effective production of acetic acid, ethanol and later citric acid, etc. by biological processes, the technical skills and experience of chemical engineering were used together with microbiological knowledge and practices to establish bioengineering and bioprocess engineering. Several bioprocesses require oxygen for various purposes, e.g., respiration, certain energy-producing metabolic pathways, direct oxidation, the oxidative degradation of some substrates, etc. [1–3]. Oxygen is usu- ally supplied by surface aeration, bubble aeration (sparg- ing), by entering oxygen or air directly, aeration com- bined with agitation or in airlift bioreactors. Regardless, oxygen is always transported from a gas stream to an un- saturated liquid [5]. The steps of this pathway are as fol- lows and are outlined in Fig. 1: • from the inside of the bubble to the surface (gas side) (1); • through the boundary layer of the bubble (liquid side) (2); • from the surface to the bulk liquid (broth) (3); • from the bulk liquid to the liquid film layer of the cell (4); *Correspondence: komaromy.peter@mk.uni-pannon.hu • into the microbial cells (through the liquid film layer) (5). Moreover, it is important to take into consideration that the oxygen demand changes as a function of time and according to the characteristics of the particular form of cell. In the liquid film layers, the transfer occurs by diffu- sion, while in the bulk phase, it always takes place via convective transport (which can be enhanced by mixing). The rate-limiting step is diffusion through the boundary film layer of the bubble on the liquid side, which is highly influenced by its surface area. On the other hand, membranes have been used in bio- processes for an extensive period of time [6–9]. How- ever, firstly they were exclusively applied for separations [6–8]. Membranes that are distinctly selective towards certain gases were used to develop gas separation tech- niques. Some of these membranes are permeable to oxy- gen molecules in the gaseous phase, therefore, it was a breakthrough to apply them for oxygen transfer into the aqueous (liquid) phase [9]. One of the most important benefits of this kind of aeration is the amplified surface area between the gaseous (air or pure oxygen) and liquid phases. In this work, three different bioprocesses are pre- sented where oxygen was supplied by membrane aera- tion: • wastewater treatment by microbial consortia; • fermentation by a single microorganism (under ster- ile conditions) for the manufacture of an organic https://doi.org/10.33927/hjic-2020-21 mailto:komaromy.peter@mk.uni-pannon.hu 6 KOMÁROMY, BÉLAFI-BAKÓ, HÜLBER-BEYER, AND NEMESTÓTHY Figure 1: The pathway of oxygen transfer. acid; • an enzymatic oxidation for the elimination of glu- cose. 2. Membrane-aerated bioreactors for the treatment of wastewater The combination of membrane technology with the bio- logical treatment of municipal and industrial wastewater has been investigated and applied for an extensive period of time. Coupling the two systems resulted in a special, new type of reactor called a membrane bioreactor (MBR). The roles of membranes in these biological systems in- clude: • separation of solids; • aeration; • extraction of special pollutants. The supply of oxygen to microbes through a mem- brane is carried out by a membrane-aerated bioreactor (MABR). This type of bioreactor was developed in 1978 and has been studied ever since. The membrane itself has to exhibit a high degree of oxygen permeability. Among gas-separation membranes, silicone (polydimethylsilox- ane) was used for oxygen enrichment (from air), thus seemed suitable for this purpose. Oxygen mass transfer through a silicone membrane was closely examined [9] and it was determined that the membrane aeration sys- tem was approximately 7 times more effective in terms of oxygen supply than the standard activated sludge pro- cess, hence it was experimentally proven to have strong potential. In different wastewater treatment processes, various gas-permeable membranes were tested with regard to oxygen supply systems [11–14]: • polyethylene; • ethyl cellulose; • polystyrene; • polytetrafluorethylene; • polypropylene; HO C O CH2 C CH2 C O OH Figure 2: Chemical structure of itaconic acid. • polyetherimide; • silicone. In terms of configuration, mainly hollow fibre mod- ules were successfully applied (though plate-and-frame modules can also be used). The membrane was placed into the liquid waste (submerged system) and oxygen from the air was pumped inside the lumen. Oxygen was transferred by diffusion through the membrane directly into the biomass that formed on the shell side of the mem- brane. The main benefits of the system are that no bubbles are formed and bubble-to-liquid transfer (diffusion limi- tation) could be avoided. The membrane aerated bioreactor system is contin- uously being developed and finally a truly reliable, ro- bust and viable system was established [15], namely the ZeeLung system. It consists of a silicone, non-porous polymer membrane with an extremely small outer diam- eter (50-70 µm) and wall thickness (5-20 µm). Thus the module has a very high surface area, low packing den- sity and an efficient rate of oxygen transfer can be main- tained under low pressure. The new system (technology) resulted in an energy efficiency four times greater than that of conventional bubble aeration. By comparing membrane aeration with traditional bubble aeration, another benefit can be recognized. A homogeneous oxygen concentration can be achieved throughout the reactor by pumping through air or oxygen bubbles. When membrane aeration is applied, it is possi- ble to establish different oxygen levels within the reactor: several zones with various oxygen levels (as required for certain biological processes) can be created. 3. Fermentation of itaconic acid Itaconic acid is an unsaturated five-carbon dicarboxylic acid that is considered to be an important platform molecule of high potential (bio-based building block) (Fig. 2). It was specified by the United States Department of Energy as one of the 12 most promising chemicals ob- tained from biomass. Itaconic acid is mainly produced biologically by fila- mentous fungi, e.g. Aspergillus terreus [16–20]. The fermentation of itaconic acid is a quite sensitive process that requires a high initial substrate concentration of glucose, precise control of operational parameters (e.g. pH, temperature, presence/deficiency of certain ions, etc.) and a high oxygen level since a high level of oxygen ten- sion in the fermenter enhances the formation of itaconic Hungarian Journal of Industry and Chemistry ENHANCEMENT OF OXYGEN TRANSFER THROUGH MEMBRANES 7 acid. A sufficient oxygen supply can be ensured by in- creasing the mixing rate, however, filamentous fungi are generally very sensitive to high shear rates. Moreover, in- tensive foaming may result which must be avoided. As the fermentation progresses, ever more biomass and pro- tein is produced in the broth, hence these phenomena may occur more acutely. In addition, the oxygen demand con- tinuously increases. Oxygen can be supplied by conventional techniques, e.g. airlift bioreactors, air bubbling equipped with pro- peller agitators, etc. For example, an airlift bioreactor was combined with a modified draft tube to achieve a high (2,430 1/h) volumetric oxygen transfer coefficient [21]. In our laboratory, membrane aeration was applied by pumping 600 l/h of air at a pressure of 1.1 bar into the broth by using a special PermSelect hydrofluoric acid (HF) silicone membrane which was immersed into the fermenter. This membrane was originally an oxygen- selective gas separation membrane, consisting of 10 thou- sand fibres (OD = 150 µm) with a surface area of 1 m2. Since oxygen permeated faster than other compounds, it resulted in a high oxygen supply. Our successful experi- ments have proven that by applying membrane aeration, enhanced oxygen transport and a higher yield of itaconic acid are achieved. 4. Enzymatic removal of glucose In the food industry, some processes require the removal of glucose [22]. For example, in the manufacture of pow- dered egg white, the reaction between glucose and pro- tein results in the Maillard browning reaction during the spray-drying step, leading to an undesirable brown dis- colouration and poor quality [23]. Another example is the manufacture of low-calorie, low-alcohol beverages from fruit juices [24]. The content of fermentable sugar can be reduced significantly by converting it into other com- pounds which cannot be metabolised into ethanol (less alcohol forms). Glucose (or dextrose, its chemical name is D- glucopyranose) is the most abundant monosaccharide, a six-carbon sugar (hexose) with five hydroxyl groups. Its name is derived from the Greek word “glukos” which means “sweet”. The extraction of glucose from a mixture containing compounds of similar molecular size is generally dif- ficult as well-known separation techniques are unsuit- able [6–8]. Therefore, a special enzymatic method has been developed where glucose is oxidised. The enzyme catalysing the reaction is called glucose oxidase (GOD), E.C. 1.1.3.4 [25,26]. During the reaction, glucose is con- verted into D-glucono-1,5-lactone, which spontaneously hydrolyses non-enzymatically into gluconic acid: β-D – glucose + O2 −−→ D – gluconic acid + H2O2 The reaction uses oxygen and releases hydrogen per- oxide as a by-product, which has a strong inactivation effect on GOD itself, therefore, it must be eliminated from the mixture. H2O2 can be converted into water and molecular oxygen by another enzyme called catalase, E.C. 1.11.1.6.: H2O2 −−→ H2O + 1 2 O2 The two enzymes can be applied together, such an en- zyme mixture can be manufactured by various moulds, e.g., species of Aspergillus. For the successful operation of the enzyme, a suffi- cient amount of oxygen should be supplied. In the case of the production of powdered egg white, traditional aera- tion methods (e.g. bubble aeration) cause a high degree of foaming, thus another technique should be implemented. In our laboratory, membrane aeration was studied and ap- plied [27]. The enzyme complex was immobilized on a resin and a packed column reactor set up for the reaction. Then a perforated silicone tube membrane was placed in- side the bioreactor in a spiral configuration to provide oxygen directly to the site of the enzymatic oxidation re- action. 5. Conclusion In summary, three different bioprocesses were presented in this paper where oxygen was supplied using a special method, namely membrane aeration. Firstly, the treat- ment of wastewater by microbial consortia (i), secondly, the fermentation of itaconic acid by a single microorgan- ism (ii) and finally, an oxidative enzymatic process (iii) for the elimination of glucose were examined in detail. Acknowledgement The research work was supported by the National Re- search, Development and Innovation Fund Project K 119940 entitled “Study on electrochemical effects on bio- product separation by electrodialysis” and project EFOP- 3.6.1-16-2016-00016. REFERENCES [1] Doran, P. M.: Bioprocess Engineering Principles (Academic Press, Waltham, Massachusetts, USA) 2013, ISBN: 978-00-8091-770-2 [2] Ratledge, C.; Kristiansen, B. (Eds.): Basic Biotech- nology (Cambridge University Press, Cambridge, UK) 2006, ISBN: 978-05-2154-958-5 [3] Bailey, J. E.; Ollis, D. F.: Biochemical Engineer- ing Fundamentals (McGraw Hill, New York, USA) 1986, ISBN: 978-00-7003-212-5 [4] Atkinson, B.; Mavituna, F.: Biochemical Engi- neering and Biotechnology Handbook (The Nature Press, London, UK) 1983, ISBN: 0-333-33274-1 [5] Komaromy, P., Sisak, Cs.: Investigation of gas- liquid oxygen transport in three-phase bioreactor, Hung. J. Ind. Chem. 1994, 22(2), 147–151 48(2) pp. 5–8 (2020) 8 KOMÁROMY, BÉLAFI-BAKÓ, HÜLBER-BEYER, AND NEMESTÓTHY [6] Mulder, M.: Basic principles of membrane technol- ogy (Kluwer Academic Publisher, Dordrecht, The Netherlands) 1996, ISBN: 978-94-009-1766-8 [7] Baker, R. W.: Membrane Technology and Applica- tions (Wiley, New York, USA) 2012, ISBN: 978-04- 708-5445-7 [8] van Reis, R.; Zydney, A.: Membrane separations in biotechnology, Current Opin. Biotechnology 2001, 12(2) 208–211 DOI: 10.1016/S0958-1669(00)00201-9 [9] Charcosset, C.: Membrane Processes in Biotechnol- ogy and Pharmaceutics (Elsevier, Amsterdam, The Netherlands) 2012, ISBN: 978-04-445-6334-7 [10] Hirasa, O.; Ichijo, H.; Yamauchi, A.: Oxygen trans- fer from silicone hollow fiber membrane to wa- ter, J. Ferm. Bioeng. 1991, 71(3), 206–207 DOI: 10.1016/0922-338X(91)90113-U [11] Irvine, R.; Sikdar, S. H.: Fundamentals and Applica- tion of Bioremediation: Principles (CRC Press, New York, USA) 1997, ISBN: 978-15-667-6308-0 [12] Stephenson, T.; Brindle, K.; Judd, S.; Jefferson, B.: Membrane Bioreactors for Wastewater Treatment (IWA Publishing, London, UK) 2000, ISBN: 978-19- 002-2207-5 [13] Brindle, K.; Stephenson, T.: The application of membrane biological reactors for the treat- ment of wastewaters, Biotechnol. Bioeng. 1996, 49(6), 601–610 DOI: 10.1002/(SICI)1097- 0290(19960320)49:6<601::AID-BIT1>3.0.CO;2-S [14] Brepols, C.: Operating large scale membrane biore- actors for municipal wastewater treatment (IWA Publishing, London, UK) 2010, ISBN: 978-18-433- 9305-4 [15] Stricker, A. E.; Lossing, H.; Gibson, J. H.; Hong, Y.; Urbanic, J. C.: Pilot scale testing of a new configuration of the membrane aerated biofilm reactor (MABR) to treat high-strength industrial sewage, Water Env. Res. 2011, 83(1), 3–14 DOI: 10.2175/106143009X12487095236991 [16] Jang, Y. S.; Kim, B.; Shin, J. H.; Choi, Y. J.; Song, C. W.; Lee, J., Park, H. G.; Lee, S. Y.: Bio-based production of C2-C6 platform chemicals, Biotechnol. Bioeng. 2012, 109(10), 2437–2459 DOI: 10.1002/bit.24599 [17] da Cruz, J. C.; Camporese Servulo, E. F.; de Cas- tro, A. M.: Microbial production of itaconic acid, in Microbial production of food ingredients and addi- tives in Handbook of Food Bioengineering Holban, A. M., Grumuzescu, A.M. (Eds.) (Academic Press, Elsevier, Amsterdam, The Netherlands) 2017, pp. 291–316, DOI: 10.1016/B978-0-12-811520-6.00010-6 [18] Varga, V.; Bélafi-Bakó, K.; Vozik, D.; Nemestóthy, N.: Recovery of itaconic acid by electrodialy- sis, Hung. J. Ind. Chem. 2018, 46(2), 43–46 DOI: 10.1515/hjic-2018-0017 [19] Karaffa, L.; Diaz, R.; Papp, B.; Fekete, E.; Sandor, E.; Kubicek, C. P.: A deficiency of manganese ions in the presence of high sugar concentrations is the critical parameter for achieving high yields of ita- conic acid by Aspergillus terreus, Appl. Microbiol. Biotechnol. 2015, 99, 7937–7944 DOI: 10.1007/s00253- 015-6735-6 [20] Komáromy, P.; Bakonyi, P.; Kucska, A.; Tóth, G.; Gubicza, L.; Bélafi-Bakó, K.; Nemestóthy, N.: Op- timized pH and its control strategy lead to enhanced itaconic acid fermentation by Aspergillus terreus on glucose substrate, Fermentation, 2019, 5(2), 31–39 DOI: 10.3390/fermentation5020031 [21] Okabe, M.; Ohta, N.; Park, Y. S.: Itaconic acid pro- duction in an air-lift bioreactor using a modified draft tube, J. Ferm. Bioeng. 1993, 76(2), 117–122 DOI: 10.1016/0922-338X(93)90067-I [22] Varzakas, T.; Tzia, C.: Food Engineering Hand- book, Food Process Engineering (CRC Press, Am- sterdam, The Netherlands) 2014, ISBN: 978-14-665- 8226-2 [23] Kilara, A.; Shahani, K. M.: Removal of glucose from eggs: A review, J. Milk Food Technol. 1973, 36(10), 509–514 DOI: 10.4315/0022-2747-36.10.509 [24] Heresztyn, T.: Conversion of glucose to gluconic acid by glucose oxidase enzyme in Muscat Gordo juice, The Australian Grapegrower and Winemaker, 1987, 4, 25–27 [25] Sisak, Cs.; Csanádi, Z.; Rónay, E.; Szajáni, B.: Elimination of glucose in egg white us- ing immobilized glucose oxidase, Enzyme Mi- crob. Technol., 2006, 39(5), 1002–1007 DOI: 10.1016/j.enzmictec.2006.02.010 [26] Pečar, D.; Vasić-Rački, Ð.; Presečki, V.: Immo- bilization of glucose oxidase on Eupergit C: Im- pact of aeration, kinetic and operational stability studies of free and immobilized enzyme, Chem. Biochem. Eng. Q. 2018, 32(4), 511–522 DOI: 10.15255/CABEQ.2018.1391 [27] Bélafi-Bakó, K.: Possibilities for removal of glu- cose from various foodstuffs and food bioprocesses, in New Topics in Food Engineering, Siegler, B.C. (Ed.) (Nova Science Publishers, New York, USA) 2010, pp. 289–299 ISBN: 978-161209599-8 Hungarian Journal of Industry and Chemistry https://doi.org/10.1016/S0958-1669(00)00201-9 https://doi.org/10.1016/0922-338X(91)90113-U https://doi.org/10.1016/0922-338X(91)90113-U https://doi.org/10.1002/(SICI)1097-0290(19960320)49:6<601::AID-BIT1>3.0.CO;2-S https://doi.org/10.1002/(SICI)1097-0290(19960320)49:6<601::AID-BIT1>3.0.CO;2-S https://doi.org/10.2175/106143009X12487095236991 https://doi.org/10.2175/106143009X12487095236991 https://doi.org/10.1002/bit.24599 https://doi.org/10.1002/bit.24599 https://doi.org/10.1016/B978-0-12-811520-6.00010-6 https://doi.org/10.1515/hjic-2018-0017 https://doi.org/10.1515/hjic-2018-0017 https://doi.org/10.1007/s00253-015-6735-6 https://doi.org/10.1007/s00253-015-6735-6 https://doi.org/10.3390/fermentation5020031 https://doi.org/10.1016/0922-338X(93)90067-I https://doi.org/10.4315/0022-2747-36.10.509 https://doi.org/10.1016/j.enzmictec.2006.02.010 https://doi.org/10.1016/j.enzmictec.2006.02.010 https://doi.org/10.15255/CABEQ.2018.1391 https://doi.org/10.15255/CABEQ.2018.1391 HUNGARIAN JOURNAL OF INDUSTRY AND CHEMISTRY Vol. 48(2) pp. 9–12 (2020) hjic.mk.uni-pannon.hu DOI: 10.33927/hjic-2020-22 THE ROLE OF WATER ACTIVITY IN TERMS OF ENZYME ACTIVITY AND ENANTIOSELECTIVITY DURING ENZYMATIC ESTERIFICATION IN NON- CONVENTIONAL MEDIA PIROSKA LAJTAI-SZABÓ1, NÁNDOR NEMESTÓTHY1, AND LÁSZLÓ GUBICZA *1 1Research Institute on Bioengineering, Membrane Technology and Energetics, University of Pannonia, Egyetem u. 10, Veszprém, 8200, HUNGARY During enzymatic esterification in non-conventional media, the activity and enantioselectivity of the enzyme is significantly influenced by the water content of the reaction medium, which continuously changes as water is produced during the esterification. To provide constant reaction parameters, water activity should be kept constant. The commonly used salt hydrate pairs may be difficult to apply and often hinder enzyme activity. During the enantioselective esterification of racemic 2-bromopropanoic acid in various solvents (organic solvents, ionic liquids), it was proven that the conditions related to the optimal water content required for kinetic examinations can be provided without using any salt or salt hydrate pairs. This conclusion is based on the realization that the optimal water activity can be set by first determining the initial water content that is necessary to achieve the maximum reaction rate in the given solvent. Keywords: enzymatic enantioselective esterification, non-conventional media, ionic liquid, racemic acid, effect of water content 1. Introduction The history of the intentional use of enzymes as biocat- alysts stretches back several decades. In recent years, in- tensified interest has been shown in applying enzymes in chemical reactions. Enzyme technology as an important discipline of biotechnology has begun to develop rapidly. Nowadays, it is of great importance in the pharmaceutical and pesticide industries where, in many cases, it has pro- duced intermediates and active substances with enzymes or microorganisms more easily than by chemical synthe- sis and usually with a high degree of (enantio)selectivity [1–3]. The known advantages of these reactions are the mild reaction conditions, pure products, high yield and, in many cases, environmentally friendly by-products. At first, enzymes were only applied in aqueous media as they exert their catalytic activity under such conditions in various organisms. Later, the recognition that enzymes can exert their catalytic activity in organic solvents pro- moted intensive investigation among organic chemists who tested more and more enzymes from various classes as catalysts of organic chemical reactions [1, 4–7]. Contradictory results concerning how the activity and enantioselectivity of a lipase depend on the physical and chemical properties of the solvents can be found in the lit- erature. In some cases, the different enzyme activities and *Correspondence: gubiczal@almos.uni-pannon.hu enantioselectivities observed in various organic solvents are not only affected by the solvents, e.g., when solvents with very different polarities are compared without fix- ing the water activity. In these experiments, the variation in the water activity probably contributes to the change in enzyme activity [6, 8–10]. In terms of the reaction, the water content of the reac- tion medium is twice as significant. To keep the enzyme activity and enantioselectivity of the lipase constant, a constant amount of water should be provided. As the con- tent of the reaction medium changes as the conversion proceeds, the water adsorption capacity of the reaction medium does not remain constant during the reaction ei- ther. Water activity is an index which indicates how much water is accessible to the enzyme. Polar solvents can ad- sorb more water, while in non-polar solvents less water is required to achieve saturation and form a new aqueous phase where the water activity of the organic solvent is equal to one (aw = 1). Therefore, two solvents of differ- ent polarities but equal water activities may contain sig- nificantly different amounts of water [11–13]. Based on the aforementioned considerations, in the literature a constant water activity is sought instead of a constant concentration of water during enzymatic reac- tions, e.g. by pervaporation of salt hydrates [14–16]. In reactions where water is not produced, the initial water activity of the reaction medium is fixed and the change in water activity, which is caused by changes in the polarity https://doi.org/10.33927/hjic-2020-22 mailto:gubiczal@almos.uni-pannon.hu 10 LAJTAI-SZABÓ, NEMESTÓTHY AND GUBICZA of the reaction medium, is neglected. In the case of esteri- fications, where the water activity of the reaction medium significantly increases as the reaction proceeds, the water activity must be constantly controlled [16–19]. In this study, the resolution of (R,S)-2- bromopropanoic acid was analyzed. Changes in the activity and enantioselectivity of the enzyme Candida rugosa lipase, which is suitable when the water content of the reaction medium is changed during the afore- mentioned reaction, were investigated. Identification of the simplest method to sustain a constant water activity required for kinetic examinations was also sought. 2. Experimental 2.1 Samples and Measurements All chemicals were commercially available and used without further purification. Candida rugosa lipase (EC 3.1.1.3) (nominal activ- ity: 920 Umg−1 enzyme) was obtained from Sigma-Aldrich (St. Louis, USA). Racemic 2-bromopropanoic acid and the ionic liquids used, namely [BMIM]PF6 (1-Butyl-3- methylimidazolium hexafluorophosphate), [NMIM]PF6 (1-Methyl-3-nonylimidazolium hexafluorophosphate) and [BMIM]BF4 (1-Butyl-3-methylimidazolium tetraflu- oroborate), were obtained from Merck KGaA (Darm- stadt, Germany). Butan-1-ol as well as all the other organic solvents and salts used were manufactured by Reanal Laboratory Chemicals Ltd. (Budapest, Hungary). In a typical experiment, 2 mmol of racemic 2- bromopropanoic acid and 12 mmol of butan-1-ol were added to 5 ml of solvent. The water concentration of the reaction mixture was measured using a Mettler DL35 Karl Fischer titrator. The water activity was adjusted us- ing salts of different aw values (LiCl (aw = 0.11), MgCl2 (aw = 0.33), NaBr (aw = 0.57), KI (aw = 0.69), KCl (aw = 0.84) and K2SO4 (aw = 0.97)). The reaction was started by adding 0.1 g of enzyme and the closed flasks were shaken in a New Brunswick G-24 horizontal shaker incubator. 2.2 Analysis The (R)- and (S)-esters produced were analyzed by an HP 5890A GC (gas chromatograph) using a 25 m FS-LIPODEX E chiral capillary GC column from MACHEREY-NAGEL (Aachen, Germany). The samples from organic solvents were directly injected into the GC after being extracted from ionic liquids using n- hexane. The activity of the lipase was characterised by the amounts of (R)- and (S)-esters produced. 3. Results and Analysis 3.1 Experiments To investigate the actual effect of solvents on the en- zyme’s activity, the same water activity should be pro- vided in the reaction media to avoid differences in en- zyme activity originating from variations in water activ- ity. By choosing the most suitable method, the applica- tion of [BMIM]BF4 must be considered, which is a polar solvent and miscible with water. The setting of the water activity with salt hydrate pairs and saturated salt solutions can be hindered as salts dissolve in ionic liquids [19]. As an alternative, the fact that the maximum enzyme activity of Candida rugosa lipase is achieved at the same water activity in any solvent was exploited [16]. According to the polarity of the solvents, the same water activity results in different water contents in various solvents, namely the reaction rate or conversion that indicate enzyme activity will be at their maxima with different water contents. An opportunity arises from the inversion of these consider- ations: the optimal initial water activity, required for ki- netic examinations, can be set by determining the initial water content that provides the maximum reaction rate for each solvent. Naturally, to precisely set a given water activity, the reaction rate should be measured by monitoring the water content infinite times. Instead of this, at least five differ- ent initial concentrations of water were set for each sol- vent. To calculate the reaction rates, the amounts of (R)- and (S)-2-Bromopropanoic acid butyl esters produced un- til 10% conversion was achieved or over two hours were considered. To determine the equilibrium constant nec- essary for calculating the enantiomeric ratio, quick re- actions with (R)-2-bromopropanoic acid butyl ester were studied in organic solvents until the equilibrium concen- tration was reached. In contrast, with ionic liquids numer- ous reaction media had to be prepared as many samples were taken because for gas chromatography (R)- and (S)- 2-bromopropanoic acid butyl esters had to be extracted from the ionic liquids using n-hexane and the subsequent extracts analysed. As a result, 15 reaction media were prepared simultaneously and the products extracted from them when samples were taken. 3.2 Effect of water content The reaction rate altered as a function of water content according to optimum curves (Fig. 1). The highest re- action rate was obtained in n-hexane and the ionic liq- uid [BMIM]PF6 when the concentrations of water were 0.15 mol dm−3 (8.9× 10−3 mol h−1 g−1) and 0.38 mol dm−3, respectively. The esterification reaction rate was very similar in toluene and [BMIM]PF6 (3.2× 10−3 mol h−1 g −1 and 3.0 × 10−3 mol h−1 g−1, respectively), while in the more polar tetrahydrofuran, [BMIM]BF4 es- ters were produced at a very low reaction rate (10−3 mol h−1 g−1) which was observed to be only slightly depen- dent on the concentration of water. The enantioselectivity of Candida rugosa lipase also varied as a function of water content according to opti- mum curves, however, was shown to be less dependent on the concentration of water than the reaction rate (Fig. 2). Hungarian Journal of Industry and Chemistry ENZYME ACTIVITY IN NON-CONVENTIONAL MEDIA 11 Figure 1: Dependence of the reaction rate on the initial concentration of water The enantioselectivities in [NMIM]PF6 and [BMIM]PF6 were modest (25 and 19, respectively), while were significantly lower in the other solvents. The highest enantioselectivity (E = 10) was obtained in n-hexane among other classic organic solvents. 3.3 Discussion In Table 1, the concentrations of water are summarized. It is shown that the maximum reaction rate (cw(conv)) or enantioselectivity (E) (cw(E)) can be achieved at 30 ◦C after 2 hours. The enzyme activity and enantioselectivity of Candida rugosa lipase varied according to optimum curves that plots water activity, moreover, the maximum reaction rate and enantioselectivity can be obtained at the same water activity regardless of the solvent used. Based on these observations, it is probable that the water activity will be the same in reaction media if a suitable cw(conv) concentration of water is set in every solvent. Similarly, the water activity of reaction media will be approximately the same by setting the suitable cw(E) concentration of water. It is possible to set approximately the same water activity in reaction media indirectly, even in the absence of salt pairs or salt hydrates. However, it should be mentioned that the cw values required to reach the maximum conversion or enantiose- lectivity are not necessarily the same. As can be observed in the case of the ionic liquid [BMIM]PF6, a maximum conversion of 29.0% was achieved when cw(conv)= 0.38 mol dm−3, while only a conversion of 26.2% was ob- Figure 2: Dependence of the enantioselectivity on the ini- tial concentration of water tained when cw(E)= 0.31 mol dm−3 required for the maximum enantioselectivity of just 20.0. Similar obser- vations may be seen in the case of organic solvents, e.g. n-hexane in which a maximum conversion (36.1%) was achieved when E = 8, while maximum enantioselec- tivity (E = 10) resulted when the conversion was only 31.7%. Therefore, it must be decided whether reaching the maximum conversion or maximum enantioselectivity is the aim. The determination of the maximum conver- sion as a function of the required enantioselectivity will be the subject of future research. 4. Conclusion In this study, it has been proven by the esterification of 2-bromopropanoic acid in the presence of Candida ru- gosa lipase that during reactions conducted in such non- conventional media, the determination of the optimal wa- ter content can be significantly simplified by setting the optimal concentration of water instead of the water ac- tivity, which is difficult to accomplish. For this purpose, the initial water content required to achieve the maximum reaction rate must be determined. As the water contents needed to achieve the maximum conversion or maximum enantioselectivity are usually different, further optimiza- tion tasks are necessary. Acknowledgement The financial support of Széchenyi 2020 under the project EFOP-3.6.1-16-2016-00015 is acknowledged. Table 1: Conversion and enantioselectivity during the enantioselective esterification reaction at the optimum cw(conv) and cw(E) values in different solvents (T =30 ◦C, t = 2 h). Solvent logP cw (conv) Conversion E cw(E) Conversion E - mol dm−3 % - mol dm−3 % - [BMIM]BF4 -2.44 0.54 4.6 5 0.46 4.0 6 [BMIM]PF6 -2.38 0.38 29.0 18 0.31 26.2 20 [NMIM]PF6 -2.19 0.31 13.4 24 0.23 8.9 25 Tetrahydrofuran 0.50 0.38 4.3 4 0.31 3.2 5 Toluene 2.50 0.23 13.9 3 0.15 10.1 5 n-Hexane 3.50 0.15 36.1 8 0.08 31.7 10 48(2) pp. 9–12 (2020) 12 LAJTAI-SZABÓ, NEMESTÓTHY AND GUBICZA REFERENCES [1] Carrea, G.; Riva, S.: Organic synthesis with en- zymes in non-aqueous media (Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, Germany) 2008, pp. 169-190 ISBN: 978-3-527-31846-9 [2] Bommarius, A. S.; Riebel, B. R.: Biocataly- sis (Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, Germany) 2004, pp. 339–372 DOI: 10.1002/3527602364.ch12 [3] Gholivand, S.; Lasekan, O.; Tan, C. P.; Abas, F.; Wei, L. S.: Optimization of enzymatic esterification of dihydrocaffeic acid with hexanol in ionic liquid using response surface methodology, Chem. Cent. J., 2017, 11(44), 1–10 DOI: 10.1186/s13065-017-0276-2 [4] Ghaffari-Moghaddam, M.; Eslahi, H.; Aydin, Y. A.; Saloglu, D.: Enzymatic processes in alternative re- action media: a mini review, J. Biol. Met., 2015, 2(3), 25–35 DOI: 10.14440/jbm.2015.60 [5] Hobbs, H. R.; Thomas, N. R.: Biocatalysis in su- percritical fluids, in fluorous solvents, and under solvent-free conditions, Chem. Rev., 2007, 107(6), 2786–2820 DOI: 10.1021/cr0683820 [6] Naushad, M.; ALOthman, Z. A.; Khan, A. B.; Ali, M.: Effect of ionic liquid on activity, sta- bility, and structure of enzymes: A review, Int. J. Biol. Macromol., 2012, 51(4), 555–560 DOI: 10.1016/j.ijbiomac.2012.06.020 [7] Ulbert, O.; Fráter, T.; Bélafi-Bakó, K.; Gubicza, L.: Enhanced enantioselectivity of Candida rugosa li- pase in ionic liquids as compared to organic sol- vents, J. Mol. Catal. B: Enzym., 2004, 31(1-3), 39– 45 DOI: 10.1016/j.molcatb.2004.07.003 [8] Wu, B.-P.; Wen, Q.; Xu, H.; Yang, Z.: Insights into the impact of deep eutectic solvents on horseradish peroxidase: Activity, stability and structure, J. Mol. Catal. B: Enzym., 2014, 101, 101–107 DOI: 10.1016/j.molcatb.2014.01.001 [9] Ghaffari-Moghaddam, M.; Ahmad, F. B. H.; Basri, M.; Abdul Rahman, M. B.: Lipase-catalyzed es- terification of betulinic acid using phthalic anhy- dride in organic solvent media: Study of reaction parameters, J. Appl. Sci., 2010, 10(4), 337–342 DOI: 10.3923/jas.2010.337.342 [10] Peres, C.; Gomes da Silva, M. D. R.; Barreiros, S.: Water activity effects on geranyl acetate synthesis catalyzed by Novozym in supercritical ethane and in supercritical carbon dioxide, J. Agric. Food Chem., 2003, 51(7), 1884–1888 DOI: 10.1021/jf026071u [11] Svensson, I.; Wehtje, E.; Adlercreutz, P.; Mattias- son, B.: Effects of water activity on reaction rates and equilibrium positions in enzymatic esterifica- tions, Biotechnol. Bioeng., 1994, 44(5), 549–556 DOI: 10.1002/bit.260440502 [12] Páez, B. C.; Medina, A. R.; Rubio, F. C.; Moreno, P. G.; Grima, E. M.: Modeling the effect of free water on enzyme activity in immobilized lipase-catalyzed reactions in organic solvents, Enzyme Microb. Technol., 2003, 33(6), 845–853 DOI: 10.1016/S0141- 0229(03)00219-9 [13] Kwon, C. H.; Lee, J. H.; Kim, S. W.; Kang, J. W.: Lipase-catalyzed Esterification of (S)-naproxen ethyl ester in supercritical carbon dioxide, J. Mi- crobiol. Biotechnol., 2009, 19(12), 1596–1602 DOI: 10.4014/jmb.0905.05051 [14] Gubicza, L.; Bélafi-Bakó, K.; Fehér, E.; Fráter, T.: Waste-free process for continuous flow enzy- matic esterification using a double pervaporation system, Green Chem., 2008, 10(12), 1284–1287 DOI: 10.1039/B810009H [15] Benedict, D. J.; Parulekar, S. J.; Tsai, S.-P.: Pervaporation-assisted esterification of lactic and succinic acids with downstream ester recovery, J. Membr. Sci., 2006, 281(1-2), 435–445 DOI: 10.1016/j.memsci.2006.04.012 [16] Fontes, N.; Harper, N.; Halling, P. J.; Barreiros, S.: Salt hydrates for in situ water control have acid-base effects on enzymes in nonaqueous me- dia, Biotechnol. Bioeng., 2003, 82(7), 802–808 DOI: 10.1002/bit.10627 [17] Márkus, Zs.; Bélafi-Bakó, K.; Tóth, G.; Nemestóthy, N.; Gubicza, L.: Effect of chain length and order of the alcohol on enzyme activity during enzymatic esterification in organic media, Hung. J. Ind. Chem., 2017, 45(2), 35–39 DOI: 10.1515/hjic-2017-0018 [18] Robb, D. A.; Yang, Z.; Halling, P. J.: The use of salt hydrates as water buffers to control enzyme activity in organic solvents, Biocatalysis, 2009, 9(1-4), 277– 283 DOI: 10.3109/10242429408992127 [19] Eckstein, M.; Wasserscheid, P.; Kragl, U.: En- hanced enantioselectivity of lipase from Pseu- domonas sp. at high temperatures and fixed water activity in the ionic liquid, 1-butyl-3- methylimidazolium bis[(trifluoromethyl)sulfonyl]- amide, Biotechnol. Lett., 2002, 24(10), 763–767 DOI: 10.1023/A:1015563801977 Hungarian Journal of Industry and Chemistry https://doi.org/10.1002/3527602364.ch12 https://doi.org/10.1002/3527602364.ch12 https://doi.org/10.1186/s13065-017-0276-2 https://doi.org/10.14440/jbm.2015.60 https://doi.org/10.1021/cr0683820 https://doi.org/10.1016/j.ijbiomac.2012.06.020 https://doi.org/10.1016/j.ijbiomac.2012.06.020 https://doi.org/10.1016/j.molcatb.2004.07.003 https://doi.org/10.1016/j.molcatb.2014.01.001 https://doi.org/10.1016/j.molcatb.2014.01.001 https://doi.org/10.3923/jas.2010.337.342 https://doi.org/10.3923/jas.2010.337.342 https://doi.org/10.1021/jf026071u https://doi.org/10.1002/bit.260440502 https://doi.org/10.1016/S0141-0229(03)00219-9 https://doi.org/10.1016/S0141-0229(03)00219-9 https://doi.org/10.4014/jmb.0905.05051 https://doi.org/10.4014/jmb.0905.05051 https://doi.org/10.1039/B810009H https://doi.org/10.1016/j.memsci.2006.04.012 https://doi.org/10.1016/j.memsci.2006.04.012 https://doi.org/10.1002/bit.10627 https://doi.org/10.1002/bit.10627 https://doi.org/10.1515/hjic-2017-0018 https://doi.org/10.1515/hjic-2017-0018 https://doi.org/10.3109/10242429408992127 https://doi.org/10.1023/A:1015563801977 HUNGARIAN JOURNAL OF INDUSTRY AND CHEMISTRY Vol. 48(2) pp. 13–21 (2020) hjic.mk.uni-pannon.hu DOI: 10.33927/hjic-2020-23 FOLLOW-UP CONTROL OF A SECOND ORDER SYSTEM IN SLIDING MODE MÁRK DOMONKOS *1 AND NÁNDOR FINK1 1Department of Mechatronics, Optics and Mechanical Engineering Informatics, Budapest University of Technology and Economics, Bertalan Lajos u. 4-6, Budapest, 1111, HUNGARY This paper deals with an instantaneous feedback controlled inverter using sliding mode control. The theoretical contribu- tion of this paper is that a common state-space-based sliding surface design method is extended for follow-up control. Starting from the basic theory of sliding mode control, the trajectory of the error signal is modelled. The practical contri- bution of this paper is the relationship between the trajectory of the error signal and the Total Harmonic Distortion (THD) of the output voltage signal. The conditions of the sliding mode control using an Uninterruptible Power Supply (UPS) are discussed. A rectifier as a non-linear load is simulated. Experimentally validated simulation results are presented. Keywords: sliding mode control, hysteresis control, second order system, UPS 1. Introduction Power electronics equipment that produces alternating voltage and current impulses is typical of Variable Struc- ture Systems (VSSs). A VSS usually consists of a state when it is insensitive to variations in parameters and load disturbances. This state is referred to as the sliding mode. The cost of insensitivity is an infinitely high switching frequency. Due to the limits with regard to switching de- lays and frequencies of controlled switches, an ideal slid- ing mode does not exist. However, the ideal sliding mode can be approximated with an acceptable degree of accu- racy. The theory of Variable Structure Systems and slid- ing mode control was developed decades ago in the So- viet Union, mainly by Vadim I. Utkin [1] and David K. Young [2]. According to the theory, sliding mode control should be robust but experiments have shown that it is subject to serious limitations. The main problem with ap- plying the sliding mode is the high frequency of oscilla- tion around the sliding surface, referred to as chattering, which strongly reduces the performance of the control. Very few have managed to put the robust behavior pre- dicted by the theory into practice. Many have concluded that the presence of chattering makes the sliding mode control a good game theory, which is not applicable in practice. Over the following period, researchers invested most of their energy in chattering-free applications, lead- ing to the development of numerous solutions like the discrete-time sliding mode [3], sector sliding mode [4], adaptive sliding mode [5] and terminal sliding mode [6]. *Author for correspondence: domonkos@mogi.bme.hu Sliding modes can also be used for feedback compensa- tion [7]. The design of a sliding mode controller consists of three main steps. The first is the design of the slid- ing surface, the second step is the design of the con- trol law which holds the system trajectory on the slid- ing surface, and the third and key step is implementation of the chattering-free sliding mode control. The system- atic sliding manifold design for linear systems was pro- posed by Utkin [1]. This method was extended in several ways and optimal sliding manifold designS proposed, e.g. frequency-shaped sliding mode control (FSSMC) [8] sur- face design based on H∞ control theory [9, 10] and Ten- sor Product Model Transformation-based Sliding Surface Design [11]. The reference signal was constant in all the aforementioned papers. Recently, the application of pre- dictive control has become popular [12]. The new ele- ment in this paper is that the original method was ex- tended for Follow-up Control. According to Refs. [13] and [14], Uninterruptible Power Supplies (UPSs) are being broadly adopted for the protection of sensitive loads, e.g. PCs, air traffic control systems, life care medical equipment, etc., against line failures or other perturbations in AC mains. Ideally, an UPS should be able to deliver: 1) a sinusoidal output volt- age with low total harmonic distortion during normal op- eration, even when feeding nonlinear loads (particularly rectified loads); 2) the voltage dip and recovery time due to a change in load step must be kept as small as possible, that is, provide a fast dynamic response; 3) the steady- state error between the sinusoidal reference and load reg- ulation must be zero. To achieve these, a Proportional In- https://doi.org/10.33927/hjic-2020-23 mailto:domonkos@mogi.bme.hu 14 DOMONKOS AND FINK tegral (PI) controller is usually used [15], moreover, is- sues concerning robust stability and controller robustness are discussed in [16]. However, the system using the PI controller subject to a variable load rather than the nom- inal ones cannot produce a fast and stable output volt- age response. In the literature, some hybrid solutions to overcome this problem can be found [17,18]. The princi- ple of Pulse Width Modulation (PWM) plays a very im- portant role in power electronics [19]. In the field of in- verter technology, which produces a sinusoidal voltage, a great number of "optimized PWM" techniques have been proposed in the literature. These types of PWM invert- ers have very good steady-state characteristics, but for the voltage regulator to respond to a sudden change in the load takes a few cycles and nonlinear loads can cause high "load harmonics". This is unacceptable in UPS ap- plications for which instantaneous feedback is preferred [20, 21]. The sliding mode control of power electronic inverters is suggested in Refs. [22] and [23]. On the one hand the main advantage of the sliding mode-based method proposed in this paper is the direct control of the transistor switches, but on the other hand uncontrolled THD results. That is why the THD is the focus of this paper. The structure of this paper is as follows. After the In- troduction, Section 2 presents the problem statement, de- scribes the system configuration and summarizes the de- sign of the sliding mode control. Section 3 describes the equations of the system and shows how the mathematical foundations are applied to a practical example. Follow- ing the main steps of sliding mode design, namely the surface design, a control law is selected by taking into consideration the reduced chattering. Section 4 presents the simulation results, which are validated by experimen- tal measurements of a 10 kVA UPS. Finally, in Section 5, the presented results are analyzed. 2. Problem Statement 2.1 System Configuration A simplified diagram of the inverter and filter is shown in Fig. 1 Vb denotes the battery voltage and vi stands for the input voltage of the system, which is a filter with a load. The input signal consists of three different values (Vb, −Vb and 0) depending on the switching states of the tran- sistors. The goal is that the transistors must be switched in such a way that vo, the output voltage of the system, follows the sinusoidal reference signal. Ls in Fig.1 de- notes the leakage inductance of the transformer, which has a special structure to increase and set the value of Ls. The main field inductance Lp cannot be ignored from the point of view of the resonance circuit. The loss of the transformer is modelled by an increased load. vo(t) = √ 2 · 230 cos (2π50t) (1) Figure 1: Simplified figure of the inverter and filter 2.2 Sliding Mode Control A Single-Input Single-Output (SISO) system is given in state-space controllable canonical form. ẋ(t) = Ax(t) + bu(t) (2) y(t) = cx(t) (3) where x(t) ∈ Rn, A ∈ Rn×n, b ∈ Rn×1, c ∈ R1×n and u(t), y(t) ∈ R. y(t), the output variable of the con- trolled plant, can be described by a n-th order differential equation supposing that the reference signal yr(t) can be differentiated at least n times. The goal is given in yr(t) = y(t) if t > Tc (4) where Tc denotes the control period. The system error, ey(t), is given by ey(t) = yr(t)− y(t) (5) Thus the error signal, ey(t), can also be differentiated at least n times, so the n − 1-th derivative must exist and is continuous. Due to the latter property, when trying to eliminate the error, it is also useful to control its deriva- tives (including the n − 1-th derivative). Otherwise, be- cause of the system inertia, an oscillation with a large amplitude could arise. The essence of sliding mode con- trol could be summarized as follows: in order to elimi- nate the error in the n-th dimensional phase space, a con- tinuous error trajectory running into the origin has been designed. Whilst planning, the physical limits should be taken into account. By implementing our very own tran- sient, the ideal system follows the reference signal with- out any errors. The control signal is designed so that the Hungarian Journal of Industry and Chemistry FOLLOW-UP CONTROL OF A SECOND ORDER SYSTEM IN SLIDING MODE 15 trajectory realized does not deviate from the prescribed one or once the origin has been reached, it remains at rest. Usually σ, a scalar variable, can be defined as a pos- itive or negative distance between the desired and actual trajectories, or each trajectory sector can be prescribed by a single scalar variable. The task of the controller is to ensure this scalar variable remains zero. In the classi- cal method of sliding mode control, this scalar variable is calculated as a linear combination of the error and its derivatives [1]. When n = 2, the scalar variable can be defined by the following equation: σ(t) = ey(t) + τ ėy(t) (6) where τ denotes a time constant-type control parameter chosen by us. In sliding mode control: σ = 0 (7) and the trajectory is described by the following equation: ey(t) = −τ ėy(t) (8) that corresponds to a −1/τ gradient in the phase plane. This is usually referred to as the sliding line or sliding surface in multidimensional phase space. The solution of Eq. 8 is an exponential function with a negative exponent: ey(t) = E0e − t τ t ≥ 0 (9) where E0 = ey(0). Consequently, the error will decrease according to the chosen time constant, τ , independently from the system parameters, e.g. load. After an exponen- tial transient process, yr(t) = y(t). To ensure that the system moves in all cases towards the sliding mode (i.e. towards σ(t) = 0), the following condition is necessary: σ(t)σ̇(t) ≤ 0 (10) The design of a sliding mode controller does not re- quire accurate modelling, it is sufficient to only know the boundaries of the model parameters and the disturbance. During sliding mode control, the only task is to switch the control signal so that Eq. 10 is valid in every instance. No other information concerning the controlled plant and the disturbances is needed. It is sufficient to determine whether Eq. 10 holds or not. In simple cases (or within a range of errors and its derivatives), the signs of the control signal and σ̇(t) are opposite. Thus it is often satisfactory to use a relay controller, which switches the control signal according to the sign of the parameter σ(t). 2.3 Follow-up Control Since yr(t) is not constant, Wyr,u(s), the transfer func- tion from u(t) (vi(t)) to yr(t) (vo(t)), must be calculated. According to Eq. 4, it is the inverse of the transfer func- tion with regard to the inverted system: y(s) = Wu,y(s)u(s) (11) Wyr,u(s) = W−1u,y(s) (12) Usually, the inverse of Wu, y(s) cannot be realized. In our case, yr(t) is sinusoidal: yr(t) = √ 2Vr cos (ωrt) (13) Only two parameters must be calculated, namely the gain (Gr) and phase shift (ϕr) of the transfer function of the system at the reference angular frequency (ωr). The defi- nition of the error (Eq. 5) must be modified: ey(t) = √ 2 Gr Vr cos (ωrt− ϕr)− y(t) (14) 3. Equations of the System 3.1 Filter and Load Using the notation of Fig. 1, the equation for the currents is: ii(t) = io(t) + ip(t) + ic(t) (15) By assuming resistive load RL: io(t) = 1 RL vo(t) (16) The equation of the input voltage is vi(t) = Ls dii(t) dt + vo(t) (17) By substituting Eqs. 15 and 16 into Eq. 17, the filter cir- cuit with a resistive load can be described by the follow- ing differential equation: vi(t) = LsCpv̈o(t) + Ls RL v̇o(t) + 1 G vo(t) (18) where G = Lp Ls + Lp (19) The transfer function from u(t) (vi(t)) to yr(t) (vo(t)) (including the effect of the resistive load) can be calcu- lated from the Laplace transformed form of Eq. 18: vo(s) = G GLsCps2 + GLs RL s+ 1 vi(s) = W (s)vi(s) (20) 3.2 The states of the state-space equation Since the inverter and load are handled separately, the system has two inputs, namely ui and io. Since the in- put current must be calculated, three state variables are selected for the three storage elements, even if they are not independent and the rank of the system matrix is only 2. Using the notation of Fig. 1, the three state variables are ii(t), ip(t) and vc(t): x(t) =  ii(t)ip(t) vo(t)  (21) 48(2) pp. 13–21 (2020) 16 DOMONKOS AND FINK The load is connected in parallel to the inverter capacitor Cp. The load current includes the effect of transformer losses. The system has three outputs since vo(t) = vc(t) as well as its derivative are necessary to calculate the scalar variable and ii(t) is visualised. In the real system, the current of the capacitor is measured instead of v̇o(t). According to Eqs. 15, 16, and 17, the matrices of the sys- tem are: A =  0 0 − 1 Ls 0 0 1 Lp 1 Cp − 1 Cp 0  B =  0 − 1 Ls 0 0 − 1 Cp 0  C = 1 0 0 0 1 0 0 0 1  D = 0 0 0 0 0 0  (22) 3.3 Error Trajectory To calculate the error defined by Eq. 14, Gr and ϕr must first be calculated. According to Eq. 20: W (jωr) = G GLsCp(jωr)2 + GLs RL jωr + 1 (23) Gr and ϕr can be calculated from Eq. 23, where W (jωr) denotes a simple complex number: Gr = |W (jωr)| (24) ϕr = angle(W (jωr)) (25) Since the filter must be inductive, ϕr must be negative. Therefore, the reference signal must lead to vo(t), which is the controlled signal in Eq. 14: y(t) = vo(t) (26) The derivative of the error is: ėy(t) = −ωr √ 2 Gr Vr sin (ωrt− ϕr)− v̇o(t) (27) The scalar variable of the sliding mode control is calcu- lated by Eq. 6. 3.4 Control Law Three different control laws were examined. All of them can directly control the switching of the transistors. This is the main advantage of sliding mode control in the field of power electronics. More advanced controllers can be found in Ref. [24]. Simple Relay The input voltage on the filter is switched according to the sign of σ(t): vi(t) = Vbsign(σ(t)) (28) From a practical point of view, the main problems of Eq. 28 are • the inverter has a zero state, which is not applied; • the switching frequency is uncontrolled. Figure 2: Double Relay with dead zone and hysteresis Double Relay The solution to the first problem is the usage of two re- lays: vi(t) = Vb 2 sign(σ(t)) + Vb 2 sign(cos (ωrt− ϕr)) (29) Vb and 0 are switched in the positive half period, while −Vb and 0 are switched in the negative half period. How- ever, Eq. 29 does not solve the second problem. Double Relay with dead zone and hysteresis Both problems can be solved by a double relay with dead zone and hysteresis. This control law is shown in Fig. 2. 3.5 Stability Analysis Using a simple relay controller, whether or not Eq. 10 holds can be verified. First, σ̇(t) must be expressed: σ̇(t) = ėy(t) + τ ëy(t) (30) According to Eq. 27: ëy(t) = −ω2 r √ 2 Gr Vr cos (ωrt− ϕr)− v̈o(t) (31) v̈o(t) can be expressed from Eq. 18: v̈o(t) = 1 LsCp vi(t)− 1 RLCp v̇o(t)− 1 GLsCp vo(t) (32) By substituting Eq. 28 into Eq. 32: v̈o(t) = Vbsign(σ(t)) LsCp − v̇o(t) RLCp − vo(t) GLsCp (33) According to Eqs. 30 and 31, if the value of v̈o(t) is suffi- ciently large and the system operates in an area which is close enough to the sliding mode, then the sign of v̈o(t) is the opposite to that of σ̇(t). From Eq. 33, it is clear that if the value of Vb is sufficiently large and the system operates in an area which is close enough to the sliding mode, then the sign of v̈o(t) is the same as that of σ(t). In conclusion, if the value of Vb is sufficiently large and the Hungarian Journal of Industry and Chemistry FOLLOW-UP CONTROL OF A SECOND ORDER SYSTEM IN SLIDING MODE 17 system operates in an area which is close enough to the sliding mode, then the sign of σ(t) is the opposite to that of σ̇(t). Similar analyses can be conducted for all control laws. In practice, the simulations proved that by substi- tuting the actual parameters of the system, all the control laws examined fulfil the condition (Eq. 10) during normal operations. 4. Simulation MATLAB-Simulink simulations were carried out. 4.1 MATLAB-Simulink model The main elements of the MATLAB-Simulink model are shown in Figs. 3–6. Even if the structure of the controller shown in Fig. 6 is quite simple, its performance is quite robust. 4.2 Model validation The simulation results were compared to previous results measured by [25] as shown in Fig. 7. The nominal power of the inverter is 10 VA. All parameters of (14) and (22) in addition to the values of dead zone and hysteresis shown in Fig. 2 are known from Ref. [25]. Like in real systems, where σ is tuned manually to set the number of switches per period, σ is changed to achieve a similar result in the simulation as in real sys- tems. The simulation result is shown in Fig. 8 after the value of σ was set. The first problem faced in the simulation was the steady state. When the real system was measured, the screen of the oscilloscope seemed to be frozen for most settings of σ. Therefore, the error trajectory was a closed curve during a period in a steady state. Using the default settings of Simulink, a quasi-steady- state period of the error trajectory of the simulation re- sulted as is shown in Fig. 9. The consequent periods are slightly different, which causes subharmonics that are crucial from a practical point of view. The main question is whether these subharmonics are an immanent property of the applied switching method or a result of an insufficiently precise simulation. Several simulations were carried out using different model con- figuration parameters in Simulink. It is concluded that the precise calculation of the switching instant is key to sim- ulating proper steady states. The Ode113(Adam) method is the most suitable integral method for switched systems. (The equation solvers for UPS are compared in Ref. [26]). 4.3 Analysis of the simulation results The main advantage of the PWM technique is that the filter can be smaller resulting in a smaller no-load current. The parameters of the filter are given in Ls = 3.5mH, Lp = 32mH, Cp = 320µF (34) Figure 3: The whole system. Figure 4: Subsystem for calculating scalar variableS. Figure 5: Subsystem for the load. Figure 6: Subsystem for the relay controller of the dead zone and hysteresis τ , the gradient of the switching line in the sliding mode, has been changed. The value of the nominal load is RL = 5.3Ω. The relationship between the gradient of the switching line and the number of switches is shown in Fig. 10. The larger the value of τ , the smaller the gra- dient of the switching line and the greater the number of switches per period. On the other hand, the bigger the value of τ , the longer the transient according to Eq. 9. The optimum must be calculated. The number of switches per period and the total har- monic distortion (THD) are shown in Fig. 11 as a func- tion of τ . The first ONE is staggered. The larger the num- 48(2) pp. 13–21 (2020) 18 DOMONKOS AND FINK Figure 7: Reference measurement result Figure 8: Reference simulation result Figure 9: Error trajectory during a quasi-steady-state pe- riod ber of switches is, the greater the switching loss and the smaller the THD are. The optimum is approximately 52 switches, therefore, 13 pulses per period, as is shown in Fig.7 and Fig. 8. The step concerning the optimal number of switches is magnified in Fig. 12. A local minimum of THD is located at the middle of a staircase (in Fig. 12) when the transient between the positive and negative half periods is smooth as is shown in Fig. 13. By increasing τ , the transients change slightly. First, a V shape appears (see Fig. 14). By further increasing τ , the V shape transforms into a loop (see Fig. 15) and the last pulses in all half periods have the opposite signs when compared to the reference signal (see Fig. 16) which increases the THD. Finally, the number of switches per period is increased by 4 and the Figure 10: The effect of τ on the number of switches Figure 11: Number of switches and THD as a function of the gradient of the switching line Figure 12: The optimal number of switches and its THD transients are in the form of Vs and loops once more (see Fig. 17), which disappear at the middle of the staircase. 4.4 Non-linear load The most challenging load is the rectifier [27], since its current is not sinusoidal and the value of its peak is rela- tively large. A rectifier resembling a load is simulated, which has approximately the same root mean square (RMS) value of the current as that of the nominal cur- rent. The time functions of the output voltage and current in addition to the input current are shown in Fig. 18. The RMS value of the input no-load current is zero at the be- ginning of the period, when the output current is zero. Since the load current is continuous and the reaction of the sliding mode controller is practically instantaneous as a result, this type of non-linear load has no significant ef- Hungarian Journal of Industry and Chemistry FOLLOW-UP CONTROL OF A SECOND ORDER SYSTEM IN SLIDING MODE 19 Figure 13: Smooth transient of error trajectory Figure 14: V-shaped transient of error trajectory Figure 15: Loop-shaped transient of error trajectory fect on the output voltage - the THD depends on the shape of the error trajectory, which is shown in Fig. 19: 4.5 Switching on the load A nominal resistive load is assumed and the load is switched on at the peak value of the output voltage. The time functions of the voltage and current are shown in Fig. 20. Since the current is discontinuous, a transient is Figure 16: Control signal Figure 17: Transient of error trajectory after the number of switches is increased present, which is recognizable in terms of the shape of the error trajectory in Fig. 21. The length of the transient depends on the value of τ , that is, 0.22 ms. The transient caused by any step change in the load occurs extremely quickly and only lasts approximately 1 ms. 5. Conclusions This paper introduced a follow-up control method with a sinusoidal reference signal for the design of a sliding sur- face and the performance of a Double Relay controller with a dead zone and hysteresis operating in a sliding mode was analysed. The simulation of variable structure systems is very sensitive to the integral method of simu- lation. The zero-crossing must be detected. Even a rela- tively simple controller, which can be implemented by an analogue operational amplifier, also performs very well with a non-linear load. The positive and negative half pe- riods can be separated by a dead zone and the switch- ing frequency limited by hysteresis. The actual number of switches can be set by the gradient of the sliding line. The value of the THD depends on the shape of the error phase trajectory. A non-linear load with a continuous out- put current has no significant effect on the output voltage because of the instantaneous behavior of the sliding mode controller. 48(2) pp. 13–21 (2020) 20 DOMONKOS AND FINK Figure 18: Output voltage as well as input and output cur- rents with a rectifier-like load Figure 19: Error trajectory of the non-linear rectifier-like load Acknowledgement The research reported in this paper and carried out at the Budapest University of Technology and Economics has been supported by the National Research Develop- ment and Innovation Fund (TKP2020 Institution Excel- lence Subprogram, Grant No. BME-IE-MIFM) based on the charter of bolster issued by the National Research De- velopment and Innovation Office under the auspices of the Ministry for Innovation and Technology. REFERENCES [1] Utkin, V. I.: Variable structure control optimization (Springer-Verlag), 1992 DOI: 10.1007/978-3-642-84379-2 [2] Young, K.: Controller design for manipulator using theory of variable structure systems, IEEE Trans. Syst. Man Cybern. Syst., 1978, 8(2), 101–109 DOI: 10.1109/TSMC.1978.4309907 [3] Korondi, P.; Hashimoto, H.; Utkin, V.: Discrete slid- ing mode control of two mass system, in: Proceed- ings of the 1995 IEEE International Symposium on Industrial Electronics, ISIE’95 (Piscataway (NJ), USA), 338–343 DOI: 10.1109/ISIE.1995.497019 Figure 20: Output voltage as well as input and output cur- rents after switching on the resistive load Figure 21: Error trajectory after switching on the resistive load [4] Korondi, P.; Xu, J., Hashimoto, H.: Sector sliding mode controller for motion control, in: Proceedings of the 8th international power electronics & motion control conference (PEMC, Prague, Czech), 254– 259 [5] Yu, H.; Ozguner, U.: Adaptive seeking slid- ing mode control, in IEEE American Con- trol Conference (Minneapolis, USA), 2006 DOI: 10.1109/ACC.2006.1657462 [6] Chang, E.-C.; Chang, F.-J.; Liang, T.-J.; Chen, J.- F.: DSP-based implementation of terminal sliding mode control with grey prediction for UPS invert- ers, the 5th IEEE Conference on Industrial Elec- tronics and Applications (ICIEA), 2010, 1046–1051 DOI: 10.1109/ICIEA.2010.5515790 [7] Fink, N.: Model reference adaptive control for tele- manipulation, Hung. J. Ind. Chem., 2019, 47(1), 41– 48 DOI: 10.33927/hjic-2019-07 [8] Young, K.; Özgüner, U.: Frequency shaped sliding mode synthesis, in International Workshop on VSS and Their Applications (Sarajevo) [9] Fodor, D.; Tóth, R.: Speed sensorless linear param- eter variant H∞ control of the induction motor, in Proceedings of the 43rd IEEE Conference Decision Hungarian Journal of Industry and Chemistry https://doi.org/10.1007/978-3-642-84379-2 https://doi.org/10.1109/TSMC.1978.4309907 https://doi.org/10.1109/TSMC.1978.4309907 https://doi.org/10.1109/ISIE.1995.497019 https://doi.org/10.1109/ACC.2006.1657462 https://doi.org/10.1109/ACC.2006.1657462 https://doi.org/10.1109/ICIEA.2010.5515790 https://doi.org/10.33927/hjic-2019-07 FOLLOW-UP CONTROL OF A SECOND ORDER SYSTEM IN SLIDING MODE 21 and Control (IEEE, Piscataway, USA), 4435–4440 DOI: 10.1109/CDC.2004.1429449 [10] Hashimoto, H.; Konno, Y.: Sliding surface design in the frequency domain, in: Variable structure and Lyapunov control , 75–86 (Springer-Verlag), 1993 DOI: 10.1007/BFb0033679 [11] Korondi, P.: Tensor product model transformation- based sliding surface design, Acta Polytech. Hung., 2006, 3(4), 23–36 [12] Neukirchner, L. R.; Magyar, A.; Fodor, A.; Kutasi, N.D.; Kelemen, A.: Constrained predictive control of three-phase buck rectifiers, Acta Polytech. Hung., 2020, 17(1), 41–60 DOI: 10.12700/APH.17.1.2020.1.3 [13] Tai, T.-L.; Chen, J.-S.: UPS Inverter design using discrete-time sliding-mode control scheme, IEEE Trans. Ind. Electron., 2002, 49(1), 67–75 DOI: 10.1109/41.982250 [14] Fedak, V.; Bauer, P.; Hájek, V.; Weiss, H.; Davat, B.; Manias, S.; Nagy, I.; Korondi, P.; Miksiewicz, R.; Duijsen, P.; Smékal, P.: Interactive E-learning in electrical engineering, in: 15th International Con- ference on Electrical Drives and Power Electronics (EDPE) (Podbanské), 368–373 [15] Caceres, R.; Rojas, R.; Camacho, O.: Robust PID control of a buck-boost DC-AC converter, Proc. IEEE INTELEC’02, 2000, 180–185 DOI: 10.1109/intlec.2000.884248 [16] Precup, R.-E.; Preitl, S.: PI and PID controllers tun- ing for integral-type servo systems to ensure ro- bust stability and controller robustness, Electr. Eng., 2006, 88(2), 149–156 DOI: 10.1007/s00202-004-0269-8 [17] Al-Hosani, K.; Malinin, A.; Utkin, V. I.: Sliding mode PID control of buck convert- ers, European Control Conference, 2009 DOI: 10.23919/ecc.2009.7074821 [18] Li, H.; Ye, X.: Sliding-mode PID control of DC- DC converter, 5th IEEE Conference on Industrial Electronics and Applications (ICIEA), 2010, 730– 734 DOI: 10.1109/ICIEA.2010.5516952 [19] Holtz, J.: Pulsewith modulation - A Survey, IEEE Trans. Ind. Electron., 1992, 39(5), 410–420 DOI: 10.1109/41.161472 [20] Kawamura, A.; Hoft, R.: Instantaneous feedback controlled PWM inverter with adaptive hysteresis, IEEE Trans. Ind. Appl., 1984, 20(4), 769–775 DOI: 10.1109/TIA.1984.4504486 [21] Aamir, M.; Kalwar, K. A.; Mekhilef, S.: Review: Uninterruptible power supply (UPS) system, Re- new. Sust. Energ. Rev., 2016, 58, 1395–1410 DOI: 10.1016/j.rser.2015.12.335 [22] Korondi, P.; Hashimoto, H.: Park vector based slid- ing mode control of UPS with unbalanced and non- linear load, in: Variable Structure Systems, Slid- ing Mode and Nonlinear Control (Springer, Lon- don, UK), 193–209 DOI: 10.1007/bfb0109978 [23] Korondi, P.; Yang, S. H.; Hashimoto, H.; Ha- rashima, F.: Sliding mode controller for parallel res- onant dual converters, J. Circ. Syst. Comp., 1995, 5(4), 735–746 DOI: 10.1142/s0218126695000424 [24] Chang, E.-C.: Study and application of intelligent sliding mode control for voltage source inverters, Energies, 2018, 11(10) 2544 DOI: 10.3390/en11102544 [25] Széll, K.; Korondi, P.: Mathematical basis of slid- ing mode control of an uninterruptible power sup- ply, Acta Polytech. Hung., 2014, 11(3), 87–106 DOI: 10.12700/APH.11.03.2014.03.6 [26] Keiel, G.; Flores, J. V.; Pereira, L. F. A.; Salton, A. T.: Discrete-time multiple resonant con- troller design for uninterruptible power supplies, IFAC-PapersOnLine, 2017, 50(1), 6717–6722 DOI: 10.1016/j.ifacol.2017.08.1169 [27] Khan, H. S.; Aamir, M.; Ali,M.; Waqar, A.; Ali, S. U.; Imtiaz, J.: Finite control set model predictive contol for parellel connected online UPS system un- der unbalanced and nonlinear load, Energies, 2019, 12(4), 581 DOI: 10.3390/en12040581 48(2) pp. 13–21 (2020) https://doi.org/10.1109/CDC.2004.1429449 https://doi.org/10.1007/BFb0033679 https://doi.org/10.12700/APH.17.1.2020.1.3 https://doi.org/10.1109/41.982250 https://doi.org/10.1109/41.982250 https://doi.org/10.1109/intlec.2000.884248 https://doi.org/10.1109/intlec.2000.884248 https://doi.org/10.1007/s00202-004-0269-8 https://doi.org/10.23919/ecc.2009.7074821 https://doi.org/10.23919/ecc.2009.7074821 https://doi.org/10.1109/ICIEA.2010.5516952 https://doi.org/10.1109/41.161472 https://doi.org/10.1109/41.161472 https://doi.org/10.1109/TIA.1984.4504486 https://doi.org/10.1109/TIA.1984.4504486 https://doi.org/10.1016/j.rser.2015.12.335 https://doi.org/10.1016/j.rser.2015.12.335 https://doi.org/10.1007/bfb0109978 https://doi.org/10.1142/s0218126695000424 https://doi.org/10.3390/en11102544 https://doi.org/10.12700/APH.11.03.2014.03.6 https://doi.org/10.12700/APH.11.03.2014.03.6 https://doi.org/10.1016/j.ifacol.2017.08.1169 https://doi.org/10.1016/j.ifacol.2017.08.1169 https://doi.org/10.3390/en12040581 HUNGARIAN JOURNAL OF INDUSTRY AND CHEMISTRY Vol. 48(2) pp. 23–36 (2020) hjic.mk.uni-pannon.hu DOI: 10.33927/hjic-2020-24 INFORMATIVE ENVIRONMENT QUALIFYING INDEX ANETT UTASI *1, VIKTOR SEBESTYÉN1, AND ÁKOS RÉDEY1 1Sustainability Solutions Research Lab, University of Pannonia, Egyetem u. 10, Veszprém, 8200, HUNGARY Improvement in the quality of the environment and, as a result, in the quality of life, including environmental impact assessments and environmental management, play an important role in the practical implementation of environmental regulations. The goal of this paper is to develop a new type of quantitative environmental impact assessment method to describe changes in environmental elements as well as the environment in an objective and reliable manner for various projects, investments, plans and proposals. An easily adaptable method was sought which provides a clear and well-interpretable result on the condition of and foreseeable changes in the environment. The algorithm operates using the limit values of environmental elements set forth by national regulations. The evaluation is independent of the number of environmental parameters chosen as it was included in the informativity rates of the method. The process results in an aggregated index for qualifying the total environment but, nonetheless, the affected environmental elements and measured environmental parameters can be analysed independently. The Informative Envi- ronment Qualifying Index evaluates the environmental parameters in proportion to the strictness of the limit values. The final assessment of the total environment is performed by using varying intervals, therefore, the different cases can be compared to each other. Experts interpret the results as well as explain the changes in the state of the environment and, therefore, identify the cause-effect relationships. Keywords: Environmental Impact Assessment, Quantitative methods, Informative Environment Qualifying Index, Environmental pollution 1. Introduction The state of the environment has been changing drasti- cally in an unfavourable direction due to economic devel- opment, rapid population growth, as well as increases in the rates of production and consumption resulting in ex- tremely severe levels of environmental pollution amongst several other consequences. The European Union itself is committed to introduce actions and measures in order to take preventive steps to protect the environment. Regard- ing immediate actions to be implemented, environmental protection is an essential objective. The Environmental Impact Assessment (EIA) and sustainability planning are intertwined. Both approaches aim to optimize future activities in an environmentally sound way and support decision-makers with appropri- ate, scientifically based evidence. Analyses must pro- vide quantified information as well as easily understand- able and comparable results, therefore, the aggregation of quantitative methods is widespread. The aim of this work is to develop an informative environment qualifying in- dex. In addition to indexing based on immission limits, it is important to evaluate the sustainability of cities, for *Correspondence: utasi.anett@uni-pannon.hu which the SDEWES Index is an excellent approach [1]. This method has been implemented in Southeast Euro- pean cities [2] and adapted for Hungarian cities [3]. Toro et al. developed a qualitative methodology for Environmental Impact Assessment (EIA) processes, which uses a double matrix to identify the impacts and assign quantitative values to the categories of quality [4]. Pavlickova and Vyskupova elaborated on a cumula- tive method for the evaluation of landscape vulnerability by taking this as well as ecological stability and the ratio of different measures of stability into account [5]. Herva and Roca reviewed the combined approaches and multi- criteria analysis for the purposes of evaluating corporate environmental performance. It was suggested to use the Multi-Criteria Decision-Making (MCDM) techniques in the industrial sector, decision-making in energy projects, waste management and wastewater treatment [6]. A possible method is the Environmental Impact As- sessment, which comprises 25 different EIA methods as well as uses a quantitative framework and standard clas- sification method as an optimal technique for the actual evaluation [7]. An integrated weight of evidence approach for en- vironmental risk assessment proposed by Caeiro et al. https://doi.org/10.33927/hjic-2020-24 mailto:utasi.anett@uni-pannon.hu 24 UTASI, SEBESTYÉN AND RÉDEY where anthropogenic pollution, the impacts on human health, the exposition of polluted wells and agricultural soils as well as pollutants in sediments are combined with 14 categories of absolute conditions [8]. Phillips used an enhanced Rapid Impact Assessment Matrix (RIAM) method to evaluate quantitatively the potential impacts of an onshore wind farm during its construction and oper- ation [9], as well as for the sustainability evaluation of municipal solid waste management [10]. The RIAM ap- proach was successfully used by Brindusa et al. to evalu- ate the environmental impacts resulting from heavy metal pollution on the southern coast of the Romanian Black Sea [11]. Sun and Wang developed a comprehensive EIA system for shale-gas exploration, which includes the eval- uation systems of influence with regard to the natural and macro environments. The algorithm includes social, pol- icy and economic impacts, therefore, it can be adapted to other fields of environmental analysis [12]. Robu et al. analysed the impacts and risks of heavy metal pollu- tants in bodies of surface water. The basis of such eval- uations was the measured concentrations of the environ- mental parameters, therefore, the method is more objec- tive [13]. The reliability of the different quantitative and qualitative methods may be increased by integrating the technical background of Environmental Risk Assessment [14]. An Environmental Evaluation System (EES) that in- corporates relationships between environmental parame- ters and environmental quality was developed by Battelle Columbus Laboratories [15] and implemented for water resources planning by Ferreira et al. [16]. A review of aquatic environmental assessment meth- ods was published by Foden et al. in which a new clas- sification system is suggested that differentiates between static and dynamic links [17]. Yu et al. developed a uni- versal calibrated model for the evaluation of bodies of surface- and groundwater. The main advantage of this al- gorithm is that it works with any combination of water quality indicators. It was developed in accordance with Chinese legislation, therefore, must be adapted for inter- national applications [18]. On the basis of the literature review on EIA, it can be concluded that the aforementioned methods have sev- eral benefits, however, their limits should be taken into consideration. The main advantage of such methods is their suitability to compare different project alternatives. However, in light of its practical applications, two main issues need to be addressed. Namely, the assessment is based on the limit values of environmental param- eters, measured/calculated values as well as the rank- ing/weighting/scaling of the environmental parameters and elements. In addition to these, it can also be con- cluded that the methods in the literature are less sensitive to extreme values, namely to discharges above the limit values. In addition to environmental impacts, risk assessment is important for decision-makers. Wu et al. proposed a quantitative environmental risk assessment for the iron and steel industrial symbiosis network, which provides an aggregate index value and is able to identify the most important driving forces [19]. Risks were identified in a chemical plant in Zhejiang province in China, where an index-based approach with the Analytic Hierarchy Pro- cess (AHP) and fuzzy operators was used for the evalua- tion [20]. The objective of this paper is to develop a new, easily adaptable, objective and reliable quantitative EIA method which provides an unambiguous outcome in the case of projects, investments, plants and proposals in compari- son with the methods given in the literature. Environmen- tal impacts should be identified during the early design phases [21] in order to avoid unlawful and polluting ac- tivities. A widely usable, target-oriented, objective and comprehensive new type of quantitative EIA technique was sought and its applicability verified. The environmental impacts were linked to human ac- tivities in order to explore the cause-and-effect relation- ships, therefore, systems thinking is crucial in the case of an EIA. Rocha et al. developed a multiple indicator- based approach for environmental quality assessment in urban areas [22]. The proposed IIEQ method is suitable for tracking environmental changes which can be linked to different human activities. 2. Results: Methodology The Informative Environment Qualifying Index (IIEQ) method was developed in accordance with legal and other relevant stipulations. The IIEQ characterizing the total environment can be determined as depicted in Fig. 1. The environmental assessment of alternatives to the project begins with screening the projects according to the applicable laws and regulations. The preparation of a reference databased Bepends on the scope of the analy- sis. Firstly, the relevant environmental elements must be selected and their limit values determined. Following the preparation of the database, the algorithm is expanded by defining the environmental parameters studied for the se- lected environmental elements and the weighting of these parameters determined. The workflow of the method has been developed in such a way that it can be used to study an individual environmental element or complex cases, e.g. where several environmental elements are considered simultaneously. By analysing the environmental parame- ters (the second block in Fig. 1 which qualify the given environmental element, it is possible to plan the optimal mitigation strategy for that element. Therefore, the driv- ing forces/drivers in the quality of the element are iden- tified. The analysis with regard to the level of the envi- ronmental element provides a comprehensive picture of the state of the studied elements and important informa- tion in the evaluation of the environmental impacts of the projects. The environmental analysis (the last block in Fig. 1) ensures a basis for the comparison of the different cases investigated. The IIEQ value helps decision-makers to determine the optimal measure or project alternative. Hungarian Journal of Industry and Chemistry INFORMATIVE ENVIRONMENT QUALIFYING INDEX 25 Figure 1: Methodology of the Informative Environment Qualifying Index The method is suitable for multilevel analysis. The levels are represented by different colours in Fig. 1. The final outcome of the method is the determination of the IIEQ. These steps are discussed in the following section. 2.1 Reference database Laws and regulations pertaining to the investigation The basis for the method is the set-up of the environ- mental reference database according to Hungarian stip- ulations. When used in other countries, the reference database should be adjusted to pertain to the legal reg- ulations of the country in question. The environmental elements are studied in the present paper in light of the Hungarian law on the environment. The following envi- ronmental elements were taken into consideration: 1. Surface water: water flows (W) 2. Surface water: lakes (L) 3. Groundwater (G) 4. Soil (S) 5. Air (A) Some EIA techniques, which were developed for the analyses of specific environmental elements, e.g. Németh et al. provided a quantitative tool for bodies of surface water [23] to show changes in water quality of Lake Balaton, the largest natural shallow lake in Central Eu- rope [24, 25]. In the case of air pollution, the Air Qual- ity Index (AQI) is a useful tool to improve public un- derstanding and participation [26]. Calculations of AQI can be supported by the two-phase decomposition tech- nique and machine learning [27]. Indoor Environmental Quality (IEQ) can also be taken into consideration as this was analysed in a university building [28]. The EIA tech- niques can be sector-specific as well, e.g. Sanz et al. de- veloped a new well-being index to describe the environ- mental quality of renewable energy sources and nuclear power [29]. One of the most important goals during the develop- ment of the IIEQ was to provide a tool that facilitates the public understanding of environmental impacts in a sim- ple, clear way, which includes more graphical representa- tions of the impacts and is capable of describing changes in the state of the environment across an aggregated index value. Determination of the environmental parameters for the environmental elements studied Lists of environmental parameters are collected for every environmental element which is to be evaluated accord- ing to the pertaining stipulations. The database includes the names of the environmental parameters as well as their limit values and validity. The list for surface water is based on GD 2010 [31], the Water Framework Directive. The lists for soil as well as groundwater are based on GD 2006 [32], and the one for the environmental parameters of air can be defined on the basis of GD 2011 [33]. Environmental parameters for which no limit values are specified can be studied as well, since the numerical target for the environmental parameters should be deter- mined by specialists and such values are to be included in the list of environmental parameters. Weight of environmental parameters Generally speaking, the objectivity of the EIA is com- promised or questionable when a specialist defines the weighted preferences for the environmental parameters. In order to solve this problem, the authors have developed a weighting procedure based solely on environmental regulations and specifications. The hypothesis assumes that legislators took the risks and impacts of the environ- mental parameters into account whilst defining the limit values. The IIEQ weighting procedure defines different categories of importance with regard to the environmen- tal elements on the basis of the order of magnitude of the limit values. To determine the confined values of the intervals, the lowest (most severe) and highest (mildest) limit values should be sought in the specifications and classified in the appropriate categories of magnitude. The other limit values should be ranked between those two 48(2) pp. 23–36 (2020) 26 UTASI, SEBESTYÉN AND RÉDEY Figure 2: Weights of the various environmental elements predefined confined values. The environmental parame- ters are classified by fitting a natural logarithmic curve as can be seen in Fig. 2, where the x and y axes indicate the magnitude and weight of the environmental parameters, respectively. Weights of 1 and 0.01 represent the most se- vere and mildest situations, respectively. The equations of the aligned curves are given in Fig. 2 by taking into consideration environmental elements (according to Hun- garian regulations, the same magnitudes are assigned to certain environmental elements). Fig. 2 presents the relationship between the air, bodies of surface water, soil and groundwater. Using the equa- tions given in Fig. 2, weights, presented in Table 1, can be generated for the given environmental parameters. In the case of surface water, the limit value of PO4-P is 200µg/l, which belongs to the order of magnitude of 102, there- fore, its value is 0.122. In the case of mercury, the limit value is 0.05µg/l, which can be assigned to the order of magnitude of 10−2, therefore, its value is 0.505. Table 1: Weights according to orders of magnitude Orders of magni- tude of the envi- ronmental parame- ters studied Expo- nent of 10 WA WW&S WG&L 0.000001 -6 1 *o.r. *o.r. 0.00001 -5 0.714 *o.r. *o.r. 0.0001 -4 0.546 1 1 0.001 -3 0.427 0.687 0.714 0.01 -2 0.335 0.505 0.546 0.1 -1 0.26 0.375 0.427 1 0 0.196 0.274 0.335 10 1 0.141 0.192 0.26 100 2 0.093 0.122 0.196 1,000 3 0.049 0.062 0.141 10,000 4 0.010 0.010 0.093 100,000 5 *o.r. *o.r. 0.01 *o.r.: outside the range of interpretation Figure 3: The qualifying diagram of an environmental ele- ment on the basis of the quality factor (FPi), quality index (IPi) and quality indicator (∆Pi) of the environmental pa- rameter 2.2 Analysis of Parameters In order to better understand the status of environmental elements, a visualization technique has been developed where the pollution/environmental load of an environ- mental element is represented by circles of defined radii and areas (Fig. 3). As a result, the parameters describing the environmental elements can be represented by seg- ments of a circle (hereinafter referred to as “segment”). The following basic considerations were taken into con- sideration: the radii of the different segments are the ra- tios of the measured parameters to limit values expressed as percentages, referred to as the so-called environmental parameter quality index. Environmental parameter quality index (IPi) The environmental parameter quality index (IPi) speci- fies the relationship between the immission concentration and limit values defined by the environmental specifica- tions. IPi is determined by IPi = MVi LVi 100 (1) where IPi is the environmental parameter quality index for parameter i, MVi is the measured value of environ- mental parameter i, and LVi is the limit value of environ- mental parameter i. If the value of IPi changes between 0 and 100%, the environmental parameter meets the legal specifications. However, if it exceeds 100%, the limit value is exceeded and mitigation measures must be considered. Environmental parameter quality factor (FPi) The environmental parameter quality factor (Pi) is deter- mined on the basis of the weight. The central angles (FP1, FP2, FP3) of the circle as depicted in Fig. 3 are calculated by FPi = Wi 360∑nPj l=1 Wl (2) Hungarian Journal of Industry and Chemistry INFORMATIVE ENVIRONMENT QUALIFYING INDEX 27 Figure 4: Immission analysis as a function of the environmental parameter quality index and factor where FPi is the environmental parameter quality factor for environmental parameter i,Wl is the weight of the ex- amined environmental parameter, and nPj is the number of environmental parameters in the case of environmental element i. Environmental parameter quality indicator (∆Pi) The environmental parameter quality indicator (∆Pi) is given by ∆Pi = (IPi) 2 π FPi 360 (3) where ∆Pi is the environmental parameter quality indica- tor for environmental parameter i, IPi is the environmen- tal parameter quality index for environmental parameter i (according to Eq. 1), and FPi is the environmental param- eter quality factor for environmental parameter i (accord- ing to Eq. 2). The environmental parameter quality index for environmental parameter i is of square/quadratic form to emphasize the importance of the measured concentra- tion of the parameter. According to this interpretation, the different param- eters with different units can be compared to each other so the environmental parameters can be easily qualified whether they exceed the limit values or fall within them. The central angles of the segments (FP1, FP2, FP3) rep- resent the importance of the environmental parameter in question which is expressed as the environmental param- eter quality factor as defined by Eq. 2 . Regarding Fig. 3, the expression of the environmen- tal parameter quality indicator is introduced based on the area of the segment (Eq. 3), which represents the extent to which the environmental parameter has an impact on the environmental element. Immission analysis The environmental parameters defined above can be illus- trated in a three-dimensional system, therefore, the key parameters of the studied impact area identified. Fig. 4 illustrates the environmental parameter quality indicators (∆Pi) as a function of the environmental parameter qual- ity factor (FPi) and the environmental parameter quality index (IPi). Fig. 4 shows the environmental parameters, their risks and the environmental damage caused should their values be extreme. According to the Informative En- vironment Qualifying Index method, the risk is a function of the specified limit value or target. The following conclusions can be drawn on the ba- sis of Fig. 4. The area (Xmin; Ymin; Zmin) represents the group of environmental parameters for which the limit values are mildest and present in low concentrations, therefore, their negative impact on the environment is minimal. The point (Xmax; Ymax; Zmax) represents the group of environmental parameters for which the limit values are most severe and present in high concentrations, there- fore, their negative impact on the environment is maxi- mal. According to the protocol defined here, the key fac- tors can be identified on the basis of a system-oriented method and the outcome of the analysis used for plan- 48(2) pp. 23–36 (2020) 28 UTASI, SEBESTYÉN AND RÉDEY Figure 5: The relationship between the load of the envi- ronmental element and the reference areas ning the environmental mitigation measures. 2.3 Analysis of the Environmental element level The analysis of the environmental element level provides information to specialists about the cumulative impact of the environmental parameters on the environmental ele- ment in question. Load of environmental elements (ELj) The cumulative environmental impacts of the environ- mental elements can be determined by totalling the envi- ronmental parameter quality indicators (∆Pi), which are equal to the sum of the areas of the segments. The envi- ronmental element can be interpreted as a circle and the total levels of pollution stemming from the environmental parameters are represented by circles with different radii (Fig. 5). In Fig. 4, the circle with R = 100 represents the case when the load of the environmental element is equal to the total limit load of the environmental parameters. Qualification of environmental elements For the numerical evaluation of an environmental ele- ment, the relationships required are defined in Table 2. On the basis of the outcome of case studies and pro- fessional experience, 10 categories were defined for the environmental evaluation: 10-30-50-70-90-100-110-150- 200-300. In Fig. 5, blue-coloured zones mark the acceptable categories (categories in Table 2: 10, 30 and 50), the green-coloured zones represent maximum environmen- tal quality with regard to immission limit values (cate- gories 70, 80 and 100), and the orange- as well as yellow- coloured zones represent situations when the level of pol- lution exceeds the limit value (categories 110, 150, 200 and 300). Given these defined categories, the relative radius can be calculated from which, after working out the area of the circle (AS = R2π), the corresponding area can be determined (AS). This corresponding area can be interre- lated to the load of the environmental parameters studied, that is to the total area of the segments (ELj) representing the environmental parameters. From the load of the environmental parameters, the total area can be calculated by a numerical evaluation. The qualification/level of pollution of the environmental element can be determined from Table 2. The interval when the Load of the Environmental Element (ELj) is less than the reference area (AS) needs to be determined. Table 2 can be used generally to evaluate all environ- mental elements and is independent from the number of environmental parameters with regard to the environmen- tal elements since the environmental parameter quality factors (Flip) are normalized for 360◦. The values of the environmental parameter quality factors depend on the number of environmental parameters and their weights. Theoretical Number of Environmental Parameters (ntj) The theoretical numbers of environmental parameters de- fine the number of environmental parameters to be mea- sured as well as monitored according to the stipulations and are based on the reference database. They are sum- marized in Table 3: Practical Number of Environmental Parameters (npj) During the environmental impact assessment, specialists define the environmental elements to be studied and the scope of the environmental parameters. The set of envi- ronmental parameters can be different in the case of a natural environment, post-disaster situation or artificial environment. The method puts a special emphasis on the determination of the uncertainties. Element Quality Index (IEj) The element quality index determines how the immission concentrations are related to the pollutants to be theoreti- cally released into the environment: IEj = √ ELj π (4) where IEj is the element quality index for environmental element j and ELj is the environmental load of environ- mental element j. Element Quality Factor (FEj) The element quality factor (FEj) specifies the weight of the environmental element during the evaluation process. The goal of this factor is to take the number of environ- mental parameters into consideration (Eq. 7): FEj = npj360∑m k=1 ntk (5) Hungarian Journal of Industry and Chemistry INFORMATIVE ENVIRONMENT QUALIFYING INDEX 29 Table 2: The qualification system for evaluating the different environmental elements Reference radius (R) Reference area (AS) Evaluation 300 282743 The environmental element is seriously damaged which the ecosystem cannot tolerate. 200 125664 Natural regeneration is impossible because the environmental element is seriously damaged. 150 70686 Natural regeneration is inhibited because the environmental element is damaged. 110 38013 The concentrations of the environmental parameters defining the environmental element exceed the limit value. 100 31416 The concentrations of the environmental parameters defining the environmental element are equal to the limit values. 90 25447 The concentrations of the environmental parameters defining the environmental element are close to the limit value. 70 15394 The environmental element with environmental parameters below the limit value or influenced by anthropogenic impacts. 50 7854 The environmental element with minimal disturbances. 30 2827 The natural environmental element with indirect or direct anthropogenic impacts. 10 314 The natural environmental element free of anthropogenic impacts. where FEj is the element quality factor for environmental element j, npj is the practical number of environmental parameters in the case of environmental element j, ntk is the theoretical number of environmental parameters in the case of environmental element j (Table 3), and m is the number of environmental elements. Environmental Element Informativity Rate (FIj) The environmental element informativity rate (FIj) repre- sents the ratio of the number of theoretical environmental parameters (ntk) for which legal requirements are in ef- fect to the number of investigated environmental parame- ters. The ideal value of the environmental element infor- mativity rate is equal to when all parameters set forth by the legal stipulations are involved in the investigation: FIj = npj ntk (6) where FIj is the environmental element informativity rate for environmental element j, npj is the practical number of environmental parameters in the case of environmental element j, and ntk is the theoretical number of environ- mental parameters in the case of environmental element k (Table 3). 2.4 Environmental analysis The next step to be undertaken, following the investiga- tion of the environmental parameters and elements, is the comprehensive analysis of the whole environment. The whole environmental system should be evaluated by tak- ing the human, health, social, economic and cultural as- Table 3: The theoretical numbers of environmental param- eters (ntk) for different environmental elements Environmental element Theoretical number of environmental parame- ters (pcs) Surface water: water flow 59 Surface water: lake 59 Groundwater 58 Soil 52 Air 33 pects into consideration in an integrated way. It is insuf- ficient to solely focus on the natural or artificial environ- ment. The usability of a given project area depends on several factors, not only on the excellence of one environ- mental element. Therefore, a holistic approach is adopted during the evaluation. Element Quality Indicator (∆Ej) The element quality indicator (∆Ej) enables the environ- mental element to be taken into consideration in light of the weights of the environmental parameters that de- scribe the environmental element. Those environmental elements which are monitored less contribute to a lesser extent with regard to the characterization of the environ- ment. During the analysis, the cumulative load of the en- vironmental elements is taken into account: ∆Ej = I2Ejπ FEj 360 (7) where ∆Ej is the element quality indicator for environ- mental element j, IEj is the element quality index for environmental element j (according to Eq. 4), and FEj is the element quality factor for environmental element j (according to Eq. 5). Total Environmental Load (TL) The total environmental load (TL) represents the propor- tional pollution of the whole environment, which can be generated by the summation of the element quality indi- cators (∆EQj). The environmental load is regarded as the most important issue during the calculation of the infor- mative environment qualifying index (IIEQ) as defined by TL = m∑ j=1 ∆Ej (8) where TL is the Total Environmental Load, ∆Ej is the El- ement Quality Indicator for environmental element j (ac- cording to Eq. 7), and m is the number of environmental elements. 48(2) pp. 23–36 (2020) 30 UTASI, SEBESTYÉN AND RÉDEY Table 4: Evaluation table of the whole environment Reference radius (R) Dynamical reference area (AD) Evaluation 300 3002πFEnv.I Degraded area which the ecosystems cannot accommodate. 200 2002πFEnv.I The natural regeneration of the environment is impossible because the area is severely damaged. 150 1502πFEnv.I Natural regeneration is inhibited because of the damaged area. 110 1102πFEnv.I The concentration of the environmental parameters exceeds the limit value. 100 1002πFEnv.I The concentration of the environmental parameters is equal to the limit value. 90 902πFEnv.I The concentration of the environmental parameters is similar to the limit value. 70 702πFEnv.I The environment is influenced by levels of pollution under the limit value and directly influenced by anthropogenic impacts. 50 502πFEnv.I The environment closely resembles the natural conditions or with minimal disturbances. 30 302πFEnv.I The natural environment with direct or indirect anthropogenic impacts. 10 102πFEnv.I The natural environment free of anthropogenic impacts. Qualification of the total environment Table 4 is used to evaluate the total environment, which is based on dynamical reference areas as calculated from AD = R2πFEnv.I (9) where AD is the dynamical reference area (the level of the whole environment), R is the reference radius (Table 4), and FEnv.I is the environment informativity rate (Eq. 10). The basis of the dynamical reference area is the ref- erence radius (Fig. 5 and Table 2) by which 10 different categories of quality were defined. The dynamical refer- ence area depends on the ratio of the studied environmen- tal parameters to the theoretical number of environmental parameters (informativity defined in the method). The numerical assessment of the whole environment results in the element quality indices from the environ- mental load stemming from the total environmental pa- rameters for the environmental elements. The evalua- tion of Table 4 concerns the results in terms of qual- ity/pollution during the interval, in which case the value of the total environmental load (TL) is less than the dy- namical reference area (AD). Environment Informativity Rate (FEnv.I) The environment informativity rate (FEnv.I) shows the depth of monitoring with regard to the environmental el- ements during the environmental evaluation: FEnv.I = ∑m j=1 npj∑m k=1 ntk (10) where FEnv.I is the environment informativity rate, npj is the practical number of environmental parameters in the case of environmental element j, ntk is the theoret- ical number of environmental parameters in the case of environmental element k, (Table 3), and m is the number of environmental elements. The environment informativity rate (FEnv.I) indicates the coverage of the environmental parameters used in the investigation compared to the specified environmental pa- rameters. The environmental impact assessment is more informative if the scope of the environmental parameters is larger. lim npj→ntk (FEnv.I) = 1 (11) According to Eq. 11, the value of FEnv.I approaches 1 if the practical number of environmental parameters (npj) closely resembles the theoretical number of parameters (ntk). Informative Environment Qualifying Index (IIEQ) The final outcome of the method that is elaborated on is the informative environment qualifying index, which is a complex indicator (Eq. 12). The status of the environ- ment is determined on the basis of the load of the dif- ferent environmental elements and the informativity. Its value depends on the actual load of the environmental el- ements as well as the practical and theoretical numbers of environmental parameters. From the reference radius of Table 4 during the evalu- ation, IIEQ determines the accurate radius of the area de- rived from the actual load of the environmental elements: IIEQ = √ TL πFEnv.I (12) where IIEQ is the informative environment qualifying in- dex, TL is the total environmental load (Eq. 8), and FEnv.I is the environment informativity rate (Eq. 10). The application of the informative environment qual- ifying index provides a solid basis to compare the out- comes of different environmental evaluations since cer- tain cases can differ from each other regarding the param- eter sets. IIEQ includes all these variables. The method can be expediently used to evaluate the environment be- fore and after a disaster (e.g. the red mud disaster of De- vecser), and the outcomes of the studies can be evaluated as a function of time. The method provides an opportu- nity to compare different cases (industrial parks, settle- ments, the natural environment) as well as provides a sys- temized and comprehensive approach to the evaluation. 2.5 A case study Zirc is a small city in the heart of the Bakony Mountains in western Hungary. The city and its surroundings is a dis- tinguished touristic area in Hungary with several natural attractions. An arboretum and National Parks are situated in the direct vicinity of the city. The water quality of the Cuha Stream which flows through the city is influenced Hungarian Journal of Industry and Chemistry INFORMATIVE ENVIRONMENT QUALIFYING INDEX 31 Table 5: The measured parameters of Zirc on June 13, 2016 The basic data of surface water Environmental parameters Measured Value Limit value Electrical conductivity (µS/cm) 985 1,000 CODcr (µg/l) 3,300 30,000 NO3-N (µg/l) 1,497.24 2,000 NH4-N (µg/l) 50 400 PO4-P (µg/l) 169 200 The basic data of air Environmental parameters Measured Value Limit value NO (µg/m3) 3.708 100 NOX (µg/m3) 8.406 200 CO (µg/m3) 278.77 10,000 O3 (µg/m3) 80.77 120 Benzene (µg/m3) 0.331 10 PM10 (µg/m3) 16 50 by the wastewater treatment plant located here. The pop- ulation of Zirc is 7, 106 and the main economic activity in the region is agriculture. During the field studies in the summer of 2016, two environmental elements were measured, namely surface water and air. The location of the measurement points is shown in Fig. 6. In the case of surface water and air, the environmen- tal parameters as defined in Table 5 were measured. The weights as well as quality indices, quality factors and quality indicators of parameters were determined and are summarized in Tables 6 and 7. FPi in the last rows of Ta- bles 6 and 7 are used as a control since their total value must be equal to 360. Values of ELj , as defined in Table 2, constitute the basis of the numerical evaluation. The value of the parameter quality index for the envi- ronmental parameter PO4-P calculated on the basis of Eq. 1 is illustrated below. The measured value of the param- eter PO4-P was 169 mg/l and the limit value pertained to it was 200 mg/l [30]. IPPO4−P = 169(mg l ) 200(mg l ) 100 = 84.5 The parameter quality factor for the environmental pa- rameter PO4-P is determined on the basis of Eq. 2. The weight of PO4-P is 0.12 and the five numbers in the de- nominator include the weight of the five environmental parameters of water. FPPO4−P = 0.12 · 360 0.06+0.01+0.06+0.12+0.12 = 116.22 The values of IPi and FPi calculated for the environmen- tal parameters listed in Tables 6 and 7 constitute the basis of the calculation. Parts A and B of Fig. 7 refer to the surface water and air, respectively. On the basis of the parametric analysis of surface wa- ter, it can be concluded that the maximum limit value is defined for PO4-P. Therefore, in the following steps, the maximum weight (FPi = max) is assigned to PO4-P. The electrical conductivity is close to the limit value and the parameter quality index of NO3-N is ∼ 75%. These pa- rameters are key to improve the water quality in Zirc and are represented by circles in Fig. 7. A similar analysis of air was also carried out. For the environmental parameter PO4-P, the parame- ter quality indicator is based on Eq. 2 using the param- eter quality index (84.50) and parameter quality factor (116.22): ∆PPO4−P = 84.52 · 3.14 · 116.22 360 = 7, 241.59 The quantitative analysis of the environmental elements of surface water and air is depicted in Fig. 8. Parts A and B of Fig. 8 refer to the surface water and air, respectively. The weights of the environmental parameters can be seen in Fig. 8 during the evaluation procedure (the total of the interior angles of the segments is equal to FPi). The radii of the sectors are identical to the values of IPi which provide information on the quality. The load of the envi- ronmental element (ELW) of the surface water is equal to 15, 296. The qualification system for evaluation of the different environmental elements (Table 2) functions by substitution. An assignment in the category of R = 50 (environmental element with minimal disturbances, Ta- ble 2 is obtained. It can be concluded that the water qual- ity of the Cuha Stream was disturbed to a minimal extent. Nevertheless, the ecosystem can tolerate this level of pol- lution. By applying Eq. 4, the values of the environmental quality index express the actual loads of the environmen- tal elements. On the basis of measurements in Zirc, the five parameters for surface water represent an area, IEW, equal to R = 69.78, while the six environmental param- eters for air represent an area, IEA, of R = 31.58: IEW = √ 15, 295.6 3.14 = 69.78 IEA = √ 3, 132.57 3.14 = 31.58 Following these steps, the aforementioned algorithm was followed. The element quality factors for water and air, FEW and FEA, were calculated according to the theoret- ical number of environmental parameters, altogether 92 parameters are to be monitored according to the specifi- cations of GD 2010 [30] and GD 2011 [32]. In the case studies for surface water and air, five and six environmen- tal parameters were investigated, respectively. FEW = 5 · 360 92 = 19.57 FEA = 6 · 360 92 = 23.48 Next, the environmental element informativity rate was determined. The environmental element informa- tivity rates for surface water and air are 0.08 and 48(2) pp. 23–36 (2020) 32 UTASI, SEBESTYÉN AND RÉDEY Figure 6: The measuring points and location of Zirc in Hungary Table 6: The results of the calculations (Eqs. 1-3) for all measured parameters in the case of surface water from Cuha Stream, Zirc Parameter (unit) Measured value Limit value Wi IPi FPi ∆Pi Electrical conductivity (µS/cm) 985 1,000 0.06 98.5 59.03 4,998.33 COD (mg/l) 3,300 30,000 0.01 11 9.5 10.03 NO3-N (mg/l) 1,497.24 2,000 0.06 74.86 59.03 2,887.19 NH4-N (mg/l) 50 400 0.12 12.5 116.22 158.47 PO4-P (mg/l) 169 200 0.12 84.5 116.22 7,241.59 Total *n/a *n/a *n/a *n/a 360 ELW = 15, 296.6 *n/a: not applicable Table 7: The results of the calculations (Eqs. 1-3) for all measured environmental parameters in the case of air in the vicinity of the Mayor’s Office, Zirc Parameter (unit) Measured value Limit value Wi IPi FPi ∆Pi NO2 (µg/Nm3) 3.71 100 0.09 3.71 58.45 7.01 NOX (µg/Nm3) 8.41 200 0.09 4.2 58.45 9.01 CO (µg/Nm3) 278.77 10,000 0.01 2.79 6.32 0.43 O3 (µg/Nm3) 80.77 120 0.09 67.31 58.45 2,310.77 Benzene (µg/Nm3) 0.33 10 0.14 3.31 89.17 8.53 PM10 (µg/Nm3) 16 50 0.14 32 89.17 796.83 Total *n/a *n/a *n/a *n/a 360 ELA = 3, 132.57 *n/a: not applicable Hungarian Journal of Industry and Chemistry INFORMATIVE ENVIRONMENT QUALIFYING INDEX 33 Figure 7: Immission analysis of the surface water (left-hand side, Part A) and air (right-hand side, Part B) Figure 8: The quantitative analysis of the environmental elements (left-hand side: water, Part A; right-hand side: air, Part B) 0.18, respectively. It should be noted that the to- tal of FEQW/FEIW = 19.57/0.08 = 230.87) and FEQA/FEIA = 23.48/0.18 = 129.13) is equal to 360. FIW = 5 59 = 0.08 FIA = 6 33 = 0.18 On the basis of Eq. 7, the load of the whole environment (∆Ej) can be calculated using the actual load of the en- vironmental elements. The actual load is substituted into the equation in the square/quadratic form, while npj/ntj for the informativity is linear. ∆EW = 69.782 · 3.14 · 19.57 360 = 831.28 ∆EA = 31.582 · 3.14 · 23.48 360 = 204.3 The total load of the environment is equal to the total of the summarized loads of the environmental elements (Eq. 8). In the case of Zirc, the larger proportion of environ- mental load stems from the surface-water pollution and the load of the air pollution represents roughly 20%. TL = 831.28 + 204.3 = 1, 035.58 The whole environment can be evaluated by the dynami- cal reference areas as given by Eq. 9 and Table 4. By substituting TL into Table 4, it can be stated that the parameters describing the status of the environment are close to the concentrations of limit values. The radius assigned to the environmental quality of the city is equal to R = 50. A50 = 502 · 3.14 · 0.12 = 939 In the aforementioned formula, the radius is equal to 50, representing the worst category of quality. The environ- mental element informativity rate is 0.12 (Eq. 10) which provides information on the number of environmental pa- rameters in the study. FEnv.I = 5 + 6 59 + 33 = 0.12 The Informative Environment Qualifying Index (IIEQ) is calculated on the basis of Eq. 12: IIEQ = √ 1, 035.58 3.14 · 0.12 =52.51 IIEQ of Zirc is 52.51. By substituting this value into Ta- ble 4, it can be seen that the environmental parameters 48(2) pp. 23–36 (2020) 34 UTASI, SEBESTYÉN AND RÉDEY describing the status of the environment closely resemble the natural conditions with minimal disturbances. Spe- cialists agree with the results, which are supported by the outcome of the field study. 3. Conclusion The novelty of the Informative Environment Qualifying Index method is that the maximum environmental loads of the environmental parameters as stipulated in the legal specifications and the actually measured/calculated lev- els of pollution are taken into consideration when com- pared to the methods published in the literature. Dur- ing the evaluation, the algorithm is applied at different levels, namely at the levels of environmental parame- ters, environmental elements and the total environment, to expediently elaborate on the planning of environmen- tal mitigation measures. The quantitative methods used in the environmental impact assessment procedure in- clude several subjective components during the weight- ing/ranking/scaling which could result in different inter- pretations with regard to the outcome of the evaluation. The Informative Environment Qualifying Index method is based on the specifications of national regulations, while the weight of the environmental elements depends on their status and the number of environmental parame- ters included in the study. The Informative Environment Qualifying Index method is suitable for following up and monitoring the status of the environment in the cases of protected areas, national parks, urban areas, disasters, etc. The applicabil- ity of the method was demonstrated in the case of Zirc, a small city in the heart of the Bakony Mountains in west- ern Hungary. The scope of the evaluation ranges from the cat- egory “environment close to the natural conditions or with minimal disturbances” to “natural environmental el- ements with indirect or direct anthropogenic impacts”. The method casts light on the significant environmental impacts without concealing extreme situations nor dis- torting the final conclusions. Acknowledgement The financial support of Széchenyi 2020 under project GINOP-2.3.2-15-2016-00016 is acknowledged. The fi- nancial support of Széchenyi 2020 under the project EFOP-3.6.1-16-2016-00015 is acknowledged. Nomenclature ∆Ej Quality indicator for environmental element j ∆Pi Quality indicator for environmental parameter i A Subscript, air AD Dynamical reference area (level of the total environment) AS Reference area (level of the environmental elements) ELj Environmental element load of environmental element j FEj Quality factor for environmental element j FEnv.I Informativity rate of total environment FIj Informativity rate of environmental element j FPi Quality factor for environmental element i G Subscript, groundwater IEj Quality index for environmental element j IIEQ Informative environment qualifying index IPi Quality index for environmental element i L Subscript, surface water: lake Lvi Limit value of environmental parameter i m Number of environmental elements Mvi Measured value of environmental parameter i npj Practical number of parameters in the case of element j ntk Theoretical number of parameters in the case of element k R Reference radius S Subscript, soil TL Total environmental load W Subscript, surface water: water flow Wi, Wl Weights of environmental parameters i and l REFERENCES [1] Kılkış, Ş.: Composite index for benchmark- ing local energy systems of Mediterranean port cities, Energy, 2015, 92(3), 622–638 DOI: 10.1016/j.energy.2015.06.093 [2] Kılkış, Ş.: Sustainable development of energy, water and environment systems index for Southeast Euro- pean cities, J. Cleaner Prod., 2016, 130(1), 222–234 DOI: 10.1016/j.jclepro.2015.07.121 [3] Sebestyén, V.; Somogyi V.; Utasi A.: Adapting the SDEWES index to two Hungarian cities, Hung. J. Ind. Chem., 2017, 45(1), 49–59 DOI: 10.1515/hjic-2017- 0008 [4] Toro, J.; Requena, I.; Duarte, O.; Zamorano, M.: A qualitative method proposal to improve environ- mental impact assessment, Environ. Impact Assess. Rev., 2013, 43, 9–20 DOI: 10.1016/j.eiar.2013.04.004 [5] Pavlickova, K.; Vyskupova, M.: A method pro- posal for cumulative environmental impact assess- ment based on the landscape vulnerability evalua- tion, Environ. Impact Assess. Rev., 2015, 50, 74–84 DOI: 10.1016/j.eiar.2014.08.011 Hungarian Journal of Industry and Chemistry https://doi.org/10.1016/j.energy.2015.06.093 https://doi.org/10.1016/j.energy.2015.06.093 https://doi.org/10.1016/j.jclepro.2015.07.121 https://doi.org/10.1515/hjic-2017-0008 https://doi.org/10.1515/hjic-2017-0008 https://doi.org/10.1016/j.eiar.2013.04.004 https://doi.org/10.1016/j.eiar.2014.08.011 INFORMATIVE ENVIRONMENT QUALIFYING INDEX 35 [6] Herva, M.; Roca, E.: Review of combined ap- proaches and multi-criteria analysis for corporate environmental evaluation, J. Cleaner Prod., 2013, 39, 355–371 DOI: 10.1016/j.jclepro.2012.07.058 [7] Carvalho, A.; Milmoso, A. F.; Mendes, A.N.; Matos, H. A.: From a literature review to a frame- work for environmental process impact assessment index, J. Cleaner Prod., 2014, 64, 36–62 DOI: 10.1016/j.jclepro.2013.08.010 [8] Caeiro, S.; Vaz-Fernandes, P.; Martinho, A. P.; Costa, P. M.; Silva, M. J.; Lavinha, J.; Matias-Dias, C.; Machado, A.; Castanheira, I.; Costa, M. H.: En- vironmental risk assessment in a contaminate es- tuary: An integrated weight of evidence approach as a decision support tool, Ocean Coastal Manage., 2017, 143, 51–62 DOI: 10.1016/j.ocecoaman.2016.09.026 [9] Phillips, J.: A quantitative-based evaluation of the environmental impact and sustainability of a pro- posed onshore wind farm in the United King- dom, Renewable Sustainable Energy Rev., 2015, 49, 1261–1270 DOI: 10.1016/j.rser.2015.04.179 [10] Phillips, J.; Gholamalifard, M.: Quantitative evalu- ation of the sustainability or unsustainability of mu- nicipal solid waste options in Tabriz, Iran. Int. J. Environ. Sci. Technol., 2016, 13(6), 1615–1624 DOI: 10.1007/s13762-016-0997-0 [11] Robu, B. M.; Jitar, O.; Teodosiu, C,; Strungaru, S. A.; Nicoara, M.; Plavan, G.: Environmental impact and risk assessment of the main pollution sources from the romanian black sea coast, Environ. Eng. Manage. J., 2015, 14(2), 331–340, http://eemj. eu/index.php/EEMJ/article/view/2184 [12] Sun, R.; Wang, Z,: A comprehensive environmen- tal impact assessment method for shale gas develop- ment, Nat. Gas Ind. B, 2015, 2(2-3), 203–210 DOI: 10.1016/j.ngib.2015.07.012 [13] Robu, B. M.; Bulgariu, D.; Bulgariu, L.; Macov- eanu, M.: Quantification of impact and risk induced in surface water by heavy metals: case study – Bahlui River Iasi, Environ. Eng. Manage. J., 2008, 7(3), 263–267 http://eemj.eu/index.php/EEMJ/ article/view/417 [14] Robu, B. M.; Căliman, F. A.; Beţianu, C.; Gavrilescu, M.: Methods and procedures for envi- ronmental risk assessment, Environ. Eng. Manage. J., 2007, 6(6), 573–592 http://eemj.eu/index. php/EEMJ/article/view/371 [15] Dee, N.; Baker, J.; Drobny, N.; Duke, K.; Whitman, I.; Fahringer, D.: An environmental evaluation system for water resource planning, Water Resour. Res., 1972, 9(3), 523–536 DOI: 10.1029/WR009i003p00523 [16] Ferreira, A. P.; da Cunha, C. L. N.; Kling, A. S. M.: Environmental evaluation model for wa- ter resource planning. Study case: Piabanha hydro- graphic basin, Rio de Janeiro, Brazil, Revista Elek- toronicado Promeda., 2008, 2(1), 7–18 [17] Foden, J.; Rogers, S. I.; Jones, A. P.: A critical re- view of approaches to aquatic environmental assess- ment, Mar. Pollut. Bull., 2008, 56(11), 1825–1833 DOI: 10.1016/j.marpolbul.2008.08.017 [18] Yu, C.; Yin, X.; Li, Z.; Yang, Z.: A universal cali- brated model for the evaluation of surface water and groundwater quality: Model development and a case study in China, J. Environ. Manage., 2015, 163, 20– 27 DOI: 10.1016/j.jenvman.2015.07.011 [19] Wu, J.; Pu, G.; Ma, Q.; Qi, H.; Wang, R.: Quantitative environmental risk assessment for the iron and steel industrial symbiosis network, J. Cleaner Prod., 2017, 157, 106–117 DOI: 10.1016/j.jclepro.2017.04.094 [20] Han, R.; Zhou, B.; An, L.; Jin, H.; Ma, L.; Li, N.; Xu, M.; Li, L.: Quantitative assessment of enter- prise environmental risk mitigation in the context of Na-tech disasters, Environ. Monit. Assess., 2019, 191(4), 1–13 DOI: 10.1007/s10661-019-7351-1 [21] Meex, E.; Hollberg, A.; Knapen, E.; Hildebrand, L.; Verbeeck, G.: Requirements for applying LCA- based environmental impact assessment tools in the early stages of building design, Build. Environ., 2018, 133, 228–236 DOI: 10.1016/j.buildenv.2018.02.016 [22] Rocha, C. A.; Sousa, F. W.; Zanella, M. E.; Oliveira, A. G.; Nascimento, R. F.; Souza, O. V.; Cajazeiras, I. M. P.; Lima, J. L. R.; Cavalcante, R. M.: Environ- mental quality assessment in areas used for physi- cal activity and recreation in a city affected by in- tense urban expansion (Fortaleza-CE, Brazil): Im- plications for public health policy, Environ. Sci. Pol- lut. Res., 2017, 9(3), 169–182 DOI: 10.1007/s12403-016- 0230-x [23] Németh, J.; Sebestyén, V.; Juzsakova, T.; Domokos, E.; Dióssy, L.; Le Phuoc, C.; Huszka, P.; Rédey, Á.: Methodology development on aquatic environ- mental assessment. Environ. Sci. Pollut. Res., 2017, 24(12), 11126–11140 DOI: 10.1007/s11356-016-7941-1 [24] Sebestyén, V.; Németh, J.; Juzsakova, T.; Domokos, E.; Kovács, Zs.; Rédey, Á.: Aquatic environmental assessment of Lake Balaton in the light of physical- chemical water parameters, Environ. Sci. Pollut. Res., 2017, 24(32), 25355–25371 DOI: 10.1007/s11356- 017-0163-3 [25] Sebestyén, V.; Németh, J.; Juzsakova, T.; Domokos, E.; Rédey, Á.; Lake Balaton: Water Quality of the Largest Shallow Lake in Central Europe, Encyclo- pedia of Water: Science, Technology, and Society, 2019, 1–15 DOI: 10.1002/9781119300762.wsts0063 [26] Jiang, L.; Zhou, H.; Bai, L.; Zhou, P.: Does foreign direct investment drive environmental degradation in China? An empirical study based on air quality index from a spatial perspective, J. Cleaner Prod., 2018, 176, 864–872 DOI: 10.1016/j.jclepro.2017.12.048 [27] Wang, D.; Wei, S.; Luo, H.; Yue, C.; Grunder, O.: A novel hybrid model for air quality in- dex forecasting based on two-phase decomposi- tion technique and modified extreme learning ma- 48(2) pp. 23–36 (2020) https://doi.org/10.1016/j.jclepro.2012.07.058 https://doi.org/10.1016/j.jclepro.2013.08.010 https://doi.org/10.1016/j.jclepro.2013.08.010 https://doi.org/10.1016/j.ocecoaman.2016.09.026 https://doi.org/10.1016/j.rser.2015.04.179 https://doi.org/10.1007/s13762-016-0997-0 https://doi.org/10.1007/s13762-016-0997-0 http://eemj.eu/index.php/EEMJ/article/view/2184 http://eemj.eu/index.php/EEMJ/article/view/2184 https://doi.org/10.1016/j.ngib.2015.07.012 https://doi.org/10.1016/j.ngib.2015.07.012 http://eemj.eu/index.php/EEMJ/article/view/417 http://eemj.eu/index.php/EEMJ/article/view/417 http://eemj.eu/index.php/EEMJ/article/view/371 http://eemj.eu/index.php/EEMJ/article/view/371 https://doi.org/10.1029/WR009i003p00523 https://doi.org/10.1029/WR009i003p00523 https://doi.org/10.1016/j.marpolbul.2008.08.017 https://doi.org/10.1016/j.jenvman.2015.07.011 https://doi.org/10.1016/j.jclepro.2017.04.094 https://doi.org/10.1016/j.jclepro.2017.04.094 https://doi.org/10.1007/s10661-019-7351-1 https://doi.org/10.1016/j.buildenv.2018.02.016 https://doi.org/10.1007/s12403-016-0230-x https://doi.org/10.1007/s12403-016-0230-x https://doi.org/10.1007/s11356-016-7941-1 https://doi.org/10.1007/s11356-017-0163-3 https://doi.org/10.1007/s11356-017-0163-3 https://doi.org/10.1002/9781119300762.wsts0063 https://doi.org/10.1016/j.jclepro.2017.12.048 36 UTASI, SEBESTYÉN AND RÉDEY chine, Sci. Total Environ., 2017, 580, 719–733 DOI: 10.1016/j.scitotenv.2016.12.018 [28] Zuhaib, S.; Manton, R.; Griffin, C.; Hajdukiewicz, M.; Keana, M. M.; Goggins, J.: An Indoor Envi- ronmental Quality (IEQ) assessment of a partially- retrofitted university building, Build. Environ., 2018, 139, 69–85 DOI: 10.1016/j.buildenv.2018.05.001 [29] Sanz-Garcia, M. T.; Caselles Moncho, A.; Micó Ruiz, J. C.; Soler Fernández, D.: Including an en- vironmental quality index in a demographic model, Int. J. Global Warming 2016, 9(3), 362–396 DOI: 10.1504/IJGW.2016.075448 [30] Sebestyén, V.; Somogyi, V.; Szőke, Sz.; Utasi, A.: Adapting the SDEWES index to two Hungarian cities, Hung. J. Ind. Chem., 2017, 45(1), 49–59 DOI: 10.1515/hjic-2017-0008 [31] GD (2010) Government Decree No. 10/2010. (VIII. 18.) of Ministry of Rural Development (VM) defin- ing the rules for establishment and use of water pol- lution limits of surface water (in Hungarian) [32] GD (2006) Joint Government Decree No. 6/2009. (IV. 14.) of Ministry of Health and Ministry of Agriculture and Rural Development (KvVM-EüM- FVM) from the limit values and the measurement of pollutants for the protection from pollution of soil and groundwater (in Hungarian) [33] GD (2011) Government Decree No. 4/2011. (I. 14.) of Ministry of Rural Development (VM) from the ambient air quality limit values and the emission limit values of stationary point sources of air pol- lutants (in Hungarian) Hungarian Journal of Industry and Chemistry https://doi.org/10.1016/j.scitotenv.2016.12.018 https://doi.org/10.1016/j.scitotenv.2016.12.018 https://doi.org/10.1016/j.buildenv.2018.05.001 https://doi.org/10.1504/IJGW.2016.075448 https://doi.org/10.1504/IJGW.2016.075448 https://doi.org/10.1515/hjic-2017-0008 https://doi.org/10.1515/hjic-2017-0008 HUNGARIAN JOURNAL OF INDUSTRY AND CHEMISTRY Vol. 48(2) pp. 37–43 (2020) hjic.mk.uni-pannon.hu DOI: 10.33927/hjic-2020-25 THE EFFECT OF pH ON BIOSURFACTANT PRODUCTION BY BACILLUS SUBTILIS DSM10 RÉKA CZINKÓCZKY1 AND ÁRON NÉMETH *1 1Department of Applied Biotechnology and Food Science, Budapest University of Technology and Economics, Műegyetem rkp. 3, Budapest, 1111, HUNGARY The genus Bacillus has long been known for its ability to produce many industrially useful products. These bacteria mostly produce extracellular products like organic acids, enzymes and biosurfactants. In this paper, the production of surfactin using the Bacillus subtilis strain DSM10 is investigated. Biosurfactant was produced in a lab-scale 1-liter fermenter. pH control using different bases (NH4OH and NaOH) was compared to observe whether the amount of produced biosurfac- tant or the quality of the product was influenced. The formation of the product was followed by measuring the surface tension, and the product formed was analyzed by reversed-phase chromatography. The investigation of the effect of pH control showed that it can be omitted during the fermentation of the biosurfactant. The highest concentration of surfactin (5 g/L) was achieved without pH control in contrast with when the pH was kept constant (pH = 7). Keywords: biosurfactant, surfactin, fermentation, purification, Bacillus subtilis 1. Introduction In recent years, the microbial production of tensio-active molecules with various properties, e.g. emulsifying, wet- ting, foaming, detergency, solubilizing and dispersing, has been gaining interest [1]. Biosurfactants are am- phiphilic compounds produced by a variety of microbial communities. Natural surfactants are of great importance in the pharmaceutical, cosmetic, agricultural and food in- dustries due to their beneficial properties including low toxicity, biodegradability, high selectivity and activity un- der extreme environmental conditions [2, 3]. The market of bio-based surfactants is predicted to be worth $5.52 billion by 2022 [4]. This is unsurprising given our high degree of dependency on kinds of hygiene products, the majority of which include surfactants or emulsifiers. Biosurfactants are classified based on their chemical structure into the following groups: glycolipids, lipopep- tides, fatty acids and lipids, as well as polymeric and particulate biosurfactants [3, 5]. One of the most effec- tive biosurfactants is surfactin. This lipopeptide-type sur- factant contains a cyclic peptide linked to a fatty acid chain (Fig. 1) [6]. Bóka et al. reported that the molec- ular weights of surfactins range from 993 Da to 1049 Da [7]. Several gram-positive Bacillus species naturally produce surfactins, which help the bacteria to stabilize their cell membranes and adhere to a surface [8, 9]. The biosynthesis of surfactin occurs through different mecha- nisms: the conversion of glucose or glycerol as a substrate *Correspondence: naron@f-labor.mkt.bme.hu to glucose 6-phosphate through the glycolytic pathway, providing the main precursor of carbohydrates located in the hydrophilic part and the oxidation of glucose to pyruvate then to acetyl-CoA, which serves as a precur- sor for the synthesis of lipids and amino acids (Asp, Glu, Leu, Val). However, if the substrate is a hydrocarbon, the metabolism is shifted towards the lipolytic pathway (β- oxidation into acetyl-CoA) and gluconeogenesis (acetyl- CoA involved in the synthesis of the precursor glucose 6-phosphate) [1]. The effectiveness of surfactants is defined by their ability to reduce the surface tension (ST), defined as the cohesive force between molecules which is proportional to the concentration of surfactant in the solution [2]. Their efficiency is measured by the critical micelle concentra- tion (CMC) [1,11]. Above the CMC, surfactants form mi- cellar structures, but below it, the aggregates dissociate into monomers. Lipopeptides from B. subtilis are partic- ularly compelling because their surface activity has been reported to be strong [6,12,13]. Powerful surfactants can decrease the surface tension of water (72 mN/m at 20 ◦C) to less than 30 mN/m [14]. The emulsifying capacity can be monitored by calculating the emulsification index (EI, %) and emulsion stability. As the pH decreases, surfactin becomes less soluble in water because the carboxyl group is protonated [12]. Under neutral or basic conditions, the carboxyl group is in the ionic form, thus its solubility and emulsification capability increases [15]. Moro et al. eval- uated the influence of the pH on the stability of the surfac- tants produced by species of B. subtilis, B. gibsonii and B. https://doi.org/10.33927/hjic-2020-25 mailto:naron@f-labor.mkt.bme.hu 38 CZINKÓCZKY AND NÉMETH Figure 1: Chemical structure of surfactin: peptide loop of amino acids: five L-amino acids (Val, Asp, Leu, Glu and Leu) two D-amino acids (Leu and Leu), and a α, β- hydroxy C13-C15 fatty acid chain [10] amyloliquefaciens [9]. All isolates exhibited surface ten- sions below 30 mN/m. In strongly acidic conditions, the emulsifying activity significantly decreased for both B. subtilis ODW02 and B. subtilis ODW15. As the pH was increased from 7 to 12, the stability of the surfactant pro- duced by B. subtilis ODW02 decreased even further, but the one by B. subtilis ODW15 remained stable. In the case of B. amyloliquefaciens MO13, a significant increase was observed both under acidic and basic conditions. This dif- ferent pH-responsive behavior makes surfactin applicable in a variety of industrial fields. The choice of fermentation cultivation media (i.e. of average composition, carbon source, nitrogen source and trace elements) as well as fermentation strategies (i.e. temperature, pH, aeration and agitation) also need to be considered in relation to the type of applications. Based on a literature review, the best combination among the fermentation conditions was 1.5 vvm at 300 rpm, result- ing in a maximum yield of surfactin of 6.45 g/L [16]. B. subtilis ATCC 21332 was grown on an iron-enriched min- imal salt (MSI) medium including glucose (40 g/L). In a recent study, Yang et al. used nanoparticles (NPs) to im- prove the total yield of surfactin. 5 g/L of Fe NPs were added to the fermentation medium of B. amyloliquefa- ciens MT45, which increased the titer of surfactin from 5.94 to 9.18 g/L. Modifying the biosynthesis of surfactin with metabolic engineering tools can further increase production and titers of surfactin in B. subtilis, as demon- strated by Wu et al. (12.8 g/L) [17]. Few studies have fo- cused on individual characteristics and relative amounts of surfactin variants in the extracts of lipopeptides. Many surfactin variants exist with various lengths of fatty acid chains and different amino acid sequences [5]. Akpa et al. analyzed the replacement of L-glutamic acid with four other amino acids, namely L-leucine, L-valine, L- isoleucine and L-threonine, in the culture medium. The presence of Thr was found to be favorable for the syn- thesis of longer (C15-C16) fatty acid chains in B. subtilis S499 [18]. Supplementation with Mn2+, Cu2+ and Ni2+ ions can also promote the production of novel variants of surfactin with different components of fatty acids (C16- C18) and amino acids (central aspartic acid methyl ester residue instead of aspartate) [19]. This approach can be useful for studying specific biological activities. The objectives of our study were to explore the effect of pH control on the production of biosurfactants (i.e. on titers and productivity) by the B. subtilis strain DSM10 and evaluate the properties of biosurfactants, i.e. type, surface tension reduction and emulsifying activity. 2. Materials and methods 2.1 Cultivation conditions Bacillus subtilis DSM10 (NCAIM B.02624T) was used for biosurfactant fermentation in this study. Cultivation was performed at 37 ◦C in 250 ml Erlenmeyer flasks con- taining 100 ml of an inorganic medium based on the composition used by Joshi et al. (2013): 34 g glucose, 1.0 g NH4NO3, 6.0 g KH2PO4, 2.7 g Na2HPO4, 0.1 g MgSO4•7 H2O, 1.2·10−3 g CaCl2•2 H2O, 1.65·10−3 g FeSO4•7 H2O, 1.5·10−3 g MnSO4•4 H2O and 2.2·10−3 g Na-EDTA [20]. The experiments were carried out in a 1 L bench- top bioreactor, with a working volume of 0.8 L (Bio- stat Q fermenter, B. Braun Biotech International, Ger- many) and a 10% v/v inoculum. For biosurfactant pro- duction, the temperature was adjusted to 37 ◦C with an agitation speed of 300 rpm and an aeration rate of 0.25 vvm. The pH was controlled by 25% H2SO4 and two different bases, namely 25% NH4OH and 25% NaOH. Biosurfactant fermentation without external pH control served as a controlled experiment. A cyclone separator for reducing foam was connected to the outlet airstream of the fermenter (Fig. 2). The foam could overflow from the fermenter via the air outlet, through the cyclone sep- arator to the collector flask. 2.2 Analysis of biomass Bacterial growth was monitored by measuring the optical density of the fermentation broth at 600 nm using a Phar- macia LKB Ultrospec Plus spectrophotometer in compar- ison with that of the centrifuged supernatant of the sam- ple. The biomass concentration (g cell dry weight/L) was determined by using a calibration curve (R2 = 1): Biomass [g/L] = 0.4283 · OD600 + 1.4568 (1) The sampled broth was centrifuged at 6, 000 rpm for 15 mins. The cell pellets were collected and dried at 105 ◦C to constant weight by a Sartorius MA35 moisture ana- lyzer to measure the cell dry weight. Hungarian Journal of Industry and Chemistry THE EFFECT OF pH ON BIOSURFACTANT PRODUCTION BY BACILLUS SUBTILIS DSM10 39 Figure 2: Fermentation setup with foam-separating glass cyclone 2.3 Analysis of glucose consumption Glucose consumption was determined using the Waters Breeze 2 HPLC System. The mobile phase was 5 mM H2SO4 and the rate of elution was 0.5 mL/min. A BIO- RAD Aminex HPX-87H (300 × 7.8 mm, 9 µm) col- umn (65 ◦C) was applied with a Refractive Index detector (40 ◦C). The glucose concentration was calculated from the peak area by the following calibration curve equation (R2 = 1): Glucose [g/L] = 4 · 10−6 · PeakArea + 0.0147 (2) 2.4 Analysis of biosurfactants Surface tension measurement The surface activities of biosurfactants produced by the bacterial strains were determined by measuring the sur- face tension of the samples of cell-free broth using the stalagmometric method with a Traube Stalagmometer (2.5 mL, Wilmad-LabGlass LG-5050-102 Stalagmome- ter Tube for samples of low viscosity) at room temper- ature (25 ◦C). To increase the accuracy of the surface tension measurements, the averages of triplicates were used in this report. The surface tension can be determined based on the number of drops that fall per unit volume, the density of the sample and the surface tension of a liq- uid reference, e.g. deionized water. The actual number of drops was calculated using N = N0 + x− y c (3) where N denotes the number of drops of the sample cal- culated to the nearest tenth of a drop;N0 represents an in- teger of drops counted between capillary-scale readings x and y; x and y stand for capillary-scale readings based on the maximum data point as 0 and the minimum data point as 40; x and y refer to the distances in millimeters from the beginning of each scale; and c is the capillary-scale calibration in millimeters per drop. The surface tension (ST in mN/m) was calculated ac- cording to ST = STw ·Nw ·D N ·Dw (4) where STw denotes the surface tension of water at 25 ◦C (72 mN/m);Nw represents the number of water drops (20 drops); D stands for the density of the sample in g/mL; N refers to the number of drops of sample, and Dw is the density of water at 25 ◦C. Emulsifying activity The emulsifying activity was determined by the addition of 2 mL of sunflower oil to the same volume of cell- free sample or surfactin solution in a test tube, which was vortex-mixed vigorously for 2 mins. [21]. The tubes were incubated at 25 ◦C and the emulsification index (EI) de- termined after 24 hours according to: EIt = ( He Ht ) 100 (5) where He and Ht are the height of emulsion and total height of the liquid in the tube, respectively. To study the emulsion stability, the same protocol was used. The emulsification index (EI, %) was determined after 1 h and the EI measured after 24 h (EI24, %), the tubes were incubated at 25 ◦C. The emulsion stability was expressed as a function of the changes in EI over the 24 h. High-performance liquid chromatography (HPLC) The surfactin concentration was measured by HPLC us- ing a Waters Alliance 2695 Separations Module, which is a high-performance liquid chromatographic system equipped with a Waters 2996 photodiode array detector, at 205 nm and a Symmetry C18 Column (4.6 × 150 mm, 5 µm - Waters, Ireland). The mobile phase consisted of 20% v/v trifluoroacetic acid (TFA) (3.8 mM) and 80% v/v acetonitrile. The elution rate was 1 mL/min at 25 ◦C and the sample volume was 10 µL. The purified surfactin was identified by using commercially available surfactin (Wako Chemicals) as the authentic compound [22]. 2.5 Isolation of the biosurfactant The method for purifying the biosurfactant was adapted from the one outlined by Joshi et al. (2008) [23]. The cell-free broth was obtained by centrifuging the fermen- tation broth at 4, 000 rpm for 20 mins. at 4 ◦C using a Janetzki MLW K23D centrifuge. The cell-free broth was used for further purification steps. The biosurfactant was recovered from the supernatant by acid precipitation: the 48(2) pp. 37–43 (2020) 40 CZINKÓCZKY AND NÉMETH Table 1: Summary of the results of the fermentations without pH control 25% NH4OH 25% NaOH Biomass yield [g/g] 0.06±0.02 0.06±0.02 0.08 Biosurfactant yield [g/g] 0.120±0.04 0.073±0.07 0.123 Glucose conversion [%] 78.90±22 71.40±4 58.35 Final biosurfactant concentration [g/L] 3.36±2.3 2.00±1.5 2.34 Minimum surface tension [mN/m] 51.1±1 54.3±17 68.4 Biomass productivity [g/l·h] 0.060±0.03 0.089±0.05 0.064 Biosurfactant productivity [g/l·h] 0.100±0.05 0.034±0.02 0.064 pH was adjusted to 2.0 using 6 N HCl and kept at 4 ◦C overnight. The precipitate was collected by centrifuga- tion at 4, 000 rpm for 20 mins. at 4 ◦C, then resuspended in distilled water. The pH was adjusted to 7.0 using 6 N NaOH and the solution lyophilized by a Christ Alpha 2-4 LSC freeze dryer. The concentration of biosurfactant was determined gravimetrically from the resulting yellowish white powder. The concentration of biosurfactant was determined gravimetrically from the lyophilized powder. The identity of the purified biosurfactant was checked by HPLC. 2.6 Calculation of fermentation parameters To compare the results of the fermentation, the following parameters were determined. Substrate (glucose) conversion was calculated accord- ing to: ∆S % = S0 − Sf S0 (6) where S0 and Sf denote the initial substrate and final glu- cose concentrations, respectively. The biomass yield on glucose (Y x S , g/g) was defined by: Y x s = xf − x0 S0 − Sf (7) where xf and x0 are the final and initial biomass concen- trations, respectively. The biosurfactant yield on glucose (YP s , g/g) was de- fined by: YP s = Pf − P0 Sf − S0 (8) where Pf and P0 are the final and initial biosurfactant concentrations, respectively. The volumetric productivities Jx = xmax txmax (9) and JP = Pmax tPmax (10) (g/l · h) were calculated as the quotients of the maximum biomass concentration (xmax, g/l) or the maximum bio- surfactant concentration (Pmax, g/l) and the fermentation time (txmax or tPmax , h) when the maximum concentra- tion was achieved, respectively. 3. Results and Discussion To evaluate the effect of pH on surfactin production, a se- ries of batch fermentations were performed either with or without pH control. The biosurfactant solution was analysed quantitatively and qualitatively using the HPLC method reported by Mubarak et al. [24]. An overview of the calculated parameters of the batch runs can be seen in Table 1. In the absence of pH con- trol, the maximum biomass concentration achieved was 4.00 g/L after 35 h (Fig. 3). Without pH control, the in- creased acidity of the medium inhibited further growth at pH 4.4. The maximum biosurfactant concentration was 4.99 g/L, which resulted in the surface tension decreas- ing to 50.1 mN/m. In pH-controlled fermentations, the biomass yields (0.06 and 0.08 g/g - pH adjusted with 25% NH4OH and 25% NaOH to 7.0, respectively) were sim- ilar to that in the absence of pH control (0.06 g/g) (Ta- ble 1), while the production of biosurfactants was unable, with a few exceptions, to reduce the surface tension sig- nificantly (66.5 mN/m with 25% NH4OH, Table 1; Figs. 4 and 5). This may account for the presence of residual glucose concentrations of 8 to 12 g/L (Figs. 4 and 5). The maximum surfactant concentrations were 3.50 and 2.34 g/L by adjusting the pH using NH4OH and NaOH, re- spectively. Although these results are similar to the av- erage yield of surfactants (3.36 g/L) in the absence of pH control, a significant drop in productivity of approxi- Figure 3: Fermentation of Surfactin - without external pH control Hungarian Journal of Industry and Chemistry THE EFFECT OF pH ON BIOSURFACTANT PRODUCTION BY BACILLUS SUBTILIS DSM10 41 Figure 4: A) HPL chromatogram of a 1.25 g/L surfactin standard, B) HPLC chromatogram of the isolated biosur- factant fraction from the foam out sample Figure 5: Fermentation of Surfactin - pH controlled by 25% NH4OH. mately 50% was observed (Table 1). The highest value of the emulsifying activity (EI24) was in excess of 70% at the end of the exponential phase of the growth curve (at 35 h, Fig. 3). The EI24 values ob- tained from samples extracted from pH-controlled exper- iments increased from 45 to 55% (Figs. 5 and 6, respec- tively). These inconsistencies can be explained in part by the fact that different Bacillus species and strains are ca- pable of producing numerous surfactin variants with dis- tinct properties. It is highly likely that the production of surfactants is independent of cell growth as EI24 de- creased and ST increased during the stationary phase per- haps as a result of degradation by enzymatic hydrolysis or uptake under substrate-limiting conditions [25, 26]. Since the spectrum of lipopeptide-type biosurfactants is broad, the profiles of the extracts obtained after the purification process were compared to the surfactin stan- dard. Fig. 4 presents two representative chromatograms: Figure 6: Fermentation of Surfactin - pH controlled by 25% NaOH (A) surfactin standard and (B) purified culture broth of B. subtilis DSM10. Comparatively speaking, the sam- ples from our study exhibited similar peaks (number of peaks, retention time). The intense peak at the beginning of the chromatogram indicates the presence of some non- retained impurities, namely contaminants and inorganic salts such as NH4NO3, which are often co-extracted with the targeted biosurfactant, that may need to be sepa- rated. Overall, based on the separation of peaks and re- tention time, the isolated biosurfactant was identified as surfactin. The surfactin titer of B. subtilis DSM10 was compared with the results from relevant studies (Table 2). Even though the fermentation strategies differ to some extent, the surfactin concentration from our work compares well with the values previously reported in the literature. 4. Conclusion The aim of this study was to assess the biosurfactant- producing capability of Bacillus subtilis DSM10 and es- tablish an economically feasible fermentative process. This paper investigated the effect of pH control on the amount of biosurfactant production. The maximum amount of biosurfactant (approximately 5 g/L) was re- covered from fermentation experiments in the absence of pH control at 37 ◦C. Furthermore, a preliminary char- acterization of the surface-active compounds produced during fermentation was conducted. HPLC analysis con- firmed the presence of surfactin in the purified product. Table 2: Surfactin production by Bacillus species Strain Surfactin titer [g/L] Ref. B. subtilis ATCC 21332 6.45 [16] B. subtilis 2.00 [27] B. subtilis SPB1 4.92 [28] B. subtilis DSM10T 3.99 [29] B. subtilis #573 4.80 [6] B. subtilis CN2 7.15 [30] B. subtilis DSM10 4.99 this work 48(2) pp. 37–43 (2020) 42 CZINKÓCZKY AND NÉMETH The minimum surface tension was 50 mN/m. The emul- sifying activity achieved using sunflower oil was approx- imately 70%. These results represent an initial step to- wards large-scale production of this biosurfactant. From a technical and economic standpoint, the fermentative pro- cess of surfactin carried out in the absence of pH control in a mineral salt medium using glucose as the sole car- bon source seems to be an effective strategy for pilot- and industrial-scale production. Acknowledgment The research was supported by the Gedeon Richter’s Talentum Foundation, founded by Gedeon Richter Plc. (Gedeon Richter Ph.D. fellowship). The research re- ported in this paper has been supported by the Na- tional Research, Development and Innovation Fund TUDFO/51757/2019-ITM, Thematic Excellence Pro- gram. REFERENCES [1] Santos, D. K. F.; Rufino, R. D.; Luna, J. M.; San- tos, V. A.; Sarubbo, L. A.: Biosurfactants: Multi- functional biomolecules of the 21st century. Int. J. Mol. Sci. 2016, 17(3), 1–31 DOI: 10.3390/ijms17030401 [2] Satpute, S. K.; Banpurkar, A. G.; Dhakephalkar, P. K.; Banat, I. M.; Chopade, B. A.: Methods for in- vestigating biosurfactants and bioemulsifiers: A re- view. Crit. Rev. Biotechnol. 2010, 30(2), 127–144 DOI: 10.3109/07388550903427280 [3] Fenibo, E. O.; Douglas, S. I.; Stanley, H. O.: A review on microbial surfactants: Produc- tion, classifications, properties and characteriza- tion. J. Adv. Microbiol. 2019, 18(3), 1–22 DOI: 10.9734/jamb/2019/v18i330170 [4] Markets and Markets. Natural Surfactants Market (Bio-based Surfactants) https: //www.marketsandmarkets.com/search.asp? search=surfactants [5] Sajna, K. V.; Höfer, R.; Sukumaran, R. K.; Got- tumukkala, L. D.; Pandey, A.: White biotechnol- ogy in biosurfactants. in: Industrial biorefineries and white biotechnology; Elsevier B.V., 2015; pp. 499–521 DOI: 10.1016/B978-0-444-63453-5.00016-1 [6] Gudiña, E. J.; Fernandes, E. C.; Rodrigues, A. I.; Teixeira, J. A.; Rodrigues, L. R.: Biosurfactant pro- duction by Bacillus subtilis using corn steep liquor as culture medium. Front. Microbiol. 2015, 6, 1–7 DOI: 10.3389/fmicb.2015.00059 [7] Bóka, B.; Manczinger, L.; Kecskeméti, A.; Chan- drasekaran, M.; Kadaikunnan, S.; Alharbi, N. S.; Vágvölgyi, Cs.; Szekeres, A.: Ion trap mass spec- trometry of surfactins produced by Bacillus subtilis SZMC6179J reveals novel fragmentation features of cyclic lipopeptides. Rapid Commun. Mass Spec- trom. 2016, 30(13), 1581–1590 DOI: 10.1002/rcm.7592 [8] From, C.; Hormazabal, V.; Hardy, S. P.; Granum, P. E.: Cytotoxicity in Bacillus mojavensis is abolished following loss of surfactin synthesis: Implications for assessment of toxicity and food poisoning po- tential. Int. J. Food Microbiol. 2007, 117(1), 43–49 DOI: 10.1016/j.ijfoodmicro.2007.01.013 [9] Moro, G. V.; Almeida, R. T. R.; Napp, A. P.; Porto, C.; Pilau, E. J.; Lüdtke, D. S.; Moro, A. V.; Vainstein, M. H. Identification and ultra-high- performance liquid chromatography coupled with high-resolution mass spectrometry characterization of biosurfactants, including a new surfactin, iso- lated from oil-contaminated environments. Microb. Biotechnol. 2018, 11(4), 759–769 DOI: 10.1111/1751- 7915.13276 [10] Surfactin - Wikimedia Commons. https: //commons.wikimedia.org/wiki/File: Surfactin.png [11] De, S.; Malik, S.; Ghosh, A.; Saha, R.; Saha, B.: A review on natural surfactants. RSC Adv. 2015, 5(81), 65757–65767 DOI: 10.1039/c5ra11101c [12] Nitschke, M.; Pastore, G. M.: Production and properties of a surfactant obtained from Bacil- lus subtilis grown on cassava wastewater. Bioresour. Technol. 2006, 97(2), 336–341 DOI: 10.1016/j.biortech.2005.02.044 [13] Fonseca, R. R.; Silva, A. J. R.; De França, F. P.; Cardoso, V. L.; Sérvulo, E. F. C.: Optimizing car- bon/nitrogen ratio for biosurfactant production by a Bacillus subtilis strain. Appl. Biochem. Biotechnol. 2007, 137(1), 471–486 DOI: 10.1007/s12010-007-9073-z [14] Seydlová, G.; Svobodová, J.: Review of surfactin chemical properties and the potential biomedical applications. Cent. Eur. J. Med. 2008, 3(2), 123–133 DOI: 10.2478/s11536-008-0002-5 [15] Long, X.; He, N.; He, Y.; Jiang, J.; Wu, T.: Bio- surfactant surfactin with pH-regulated emulsifica- tion activity for efficient oil separation when used as emulsifier. Bioresour. Technol. 2017, 241, 200– 206 DOI: 10.1016/j.biortech.2017.05.120 [16] Yeh, M.-S.; Wei, Y.-H.; Chang, J.-S.: Bioreactor de- sign for enhanced carrier-assisted surfactin produc- tion with Bacillus subtilis. Process Biochem. 2006, 41(8), 1799–1805 DOI: 10.1016/j.procbio.2006.03.027 [17] Wu, Q.; Zhi, Y.; Xu, Y.: Systematically engineering the biosynthesis of a green biosurfactant surfactin by Bacillus subtilis 168. Metab. Eng. 2019, 52, 87– 97 DOI: 10.1016/j.ymben.2018.11.004 [18] Akpa, E.; Jacques, P.; Wathelet, B.; Paquot, M.; Fuchs, R.; Budzikiewicz, H.; Thonart, P.: Influence of culture conditions on lipopeptide production by Bacillus subtilis. Appl. Biochem. Biotechnol. - Part A Enzym. Eng. Biotechnol. 2001, 91, 551–561 DOI: 10.1385/ABAB:91-93:1-9:551 [19] Bartal, A.; Vigneshwari, A.; Bóka, B.; Vörös, M.; Takács, I.; Kredics, L.; Manczinger, L.; Varga, M.; Vágvölgyi, Cs.; Szekeres, A.: Effects Hungarian Journal of Industry and Chemistry https://doi.org/10.3390/ijms17030401 https://doi.org/10.3109/07388550903427280 https://doi.org/10.9734/jamb/2019/v18i330170 https://doi.org/10.9734/jamb/2019/v18i330170 https://www.marketsandmarkets.com/search.asp?search=surfactants https://www.marketsandmarkets.com/search.asp?search=surfactants https://www.marketsandmarkets.com/search.asp?search=surfactants https://doi.org/10.1016/B978-0-444-63453-5.00016-1 https://doi.org/10.3389/fmicb.2015.00059 https://doi.org/10.1002/rcm.7592 https://doi.org/10.1016/j.ijfoodmicro.2007.01.013 https://doi.org/10.1111/1751-7915.13276 https://doi.org/10.1111/1751-7915.13276 https://commons.wikimedia.org/wiki/File:Surfactin.png https://commons.wikimedia.org/wiki/File:Surfactin.png https://commons.wikimedia.org/wiki/File:Surfactin.png https://doi.org/10.1039/c5ra11101c https://doi.org/10.1016/j.biortech.2005.02.044 https://doi.org/10.1016/j.biortech.2005.02.044 https://doi.org/10.1007/s12010-007-9073-z https://doi.org/10.2478/s11536-008-0002-5 https://doi.org/10.1016/j.biortech.2017.05.120 https://doi.org/10.1016/j.procbio.2006.03.027 https://doi.org/10.1016/j.ymben.2018.11.004 https://doi.org/10.1385/ABAB:91-93:1-9:551 https://doi.org/10.1385/ABAB:91-93:1-9:551 THE EFFECT OF pH ON BIOSURFACTANT PRODUCTION BY BACILLUS SUBTILIS DSM10 43 of different cultivation parameters on the pro- duction of surfactin variants by a Bacillus sub- tilis strain. Molecules, 2018, 23(10), 2675 DOI: 10.3390/molecules23102675 [20] Joshi, S. J.; Geetha, S. J.; Yadav, S.; De- sai, A. J.: Optimization of bench-scale produc- tion of biosurfactant by Bacillus licheniformis R2. APCBEE Procedia 2013, 5, 232–236 DOI: 10.1016/j.apcbee.2013.05.040 [21] Vaz, D. A.; Gudiña, E. J.; Alameda, E. J.; Teix- eira, J. A.; Rodrigues, L. R.: Performance of a biosurfactant produced by a Bacillus subtilis strain isolated from crude oil samples as com- pared to commercial chemical surfactants. Col- loids Surf. B: Biointerfaces 2012, 89, 167–174 DOI: 10.1016/j.colsurfb.2011.09.009 [22] Freitas de Oliveira, D. W.; Lima França, Í. W.; Nogueira Félix, A. K.; Lima Martins, J. J.; Apare- cida Giro, M. E.; Melo, V. M. M.; Gonçalves, L. R. B.: Kinetic study of biosurfactant production by Bacillus subtilis LAMI005 grown in clarified cashew apple juice. Colloids Surf. B: Biointerfaces 2013, 101, 34–43 DOI: 10.1016/j.colsurfb.2012.06.011 [23] Joshi, S.; Bharucha, C.; Desai, A. J.: Produc- tion of biosurfactant and antifungal compound by fermented food isolate Bacillus subtilis 20B. Bioresour. Technol. 2008, 99(11), 4603–4608 DOI: 10.1016/j.biortech.2007.07.030 [24] Mubarak, M. Q. E.; Hassan, A. R.; Hamid, A. A.; Khalil, S.; Isa, M. H. M.: A simple and effective isocratic HPLC method for fast identification and quantification of surfactin. Sains Malaysiana 2015, 44(1), 115–120 DOI: 10.17576/jsm-2015-4401-16 [25] Mukherjee, S.; Das, P.; Sivapathasekaran, C.; Sen, R.: Antimicrobial biosurfactants from marine Bacil- lus circulans: Extracellular synthesis and purifica- tion. Lett. Appl. Microbiol. 2009, 48(3), 281–288 DOI: 10.1111/j.1472-765X.2008.02485.x [26] Rodríguez, N.; Salgado, J. M.; Cortés, S.; Domínguez, J. M.: Alternatives for biosurfac- tants and bacteriocins extraction from Lactococcus lactis cultures produced under different pH condi- tions. Lett. Appl. Microbiol. 2010, 51(2), 226–233 DOI: 10.1111/j.1472-765X.2010.02882.x [27] Amani, H.; Mehrnia, M. R.; Sarrafzadeh, M. H.; Haghighi, M.; Soudi, M. R.: Scale up and applica- tion of biosurfactant from Bacillus subtilis in en- hanced oil recovery. Appl. Biochem. Biotechnol. 2010, 162(2), 510–523 DOI: 10.1007/s12010-009-8889-0 [28] Ghribi, D.; Ellouze-Chaabouni, S.: Enhancement of Bacillus subtilis lipopeptide biosurfactants produc- tion through optimization of medium composition and adequate control of aeration. Biotechnol. Res. Int. 2011, 1–6 DOI: 10.4061/2011/653654 [29] Willenbacher, J.; Rau, J. T.; Rogalla, J.; Syldatk, C.; Hausmann, R.: Foam-free production of surfactin via anaerobic fermentation of Bacillus subtilis DSM 10T. AMB Express 2015, 5(1) DOI: 10.1186/s13568-015- 0107-6 [30] Bezza, F. A.; Chirwa, E. M. N.: Production and applications of lipopeptide biosurfactant for biore- mediation and oil recovery by Bacillus subtilis CN2. Biochem. Eng. J. 2015, 101, 168–178 DOI: 10.1016/j.bej.2015.05.007 48(2) pp. 37–43 (2020) https://doi.org/10.3390/molecules23102675 https://doi.org/10.3390/molecules23102675 https://doi.org/10.1016/j.apcbee.2013.05.040 https://doi.org/10.1016/j.apcbee.2013.05.040 https://doi.org/10.1016/j.colsurfb.2011.09.009 https://doi.org/10.1016/j.colsurfb.2011.09.009 https://doi.org/10.1016/j.colsurfb.2012.06.011 https://doi.org/10.1016/j.biortech.2007.07.030 https://doi.org/10.1016/j.biortech.2007.07.030 https://doi.org/10.17576/jsm-2015-4401-16 https://doi.org/10.1111/j.1472-765X.2008.02485.x https://doi.org/10.1111/j.1472-765X.2010.02882.x https://doi.org/10.1007/s12010-009-8889-0 https://doi.org/10.4061/2011/653654 https://doi.org/10.1186/s13568-015-0107-6 https://doi.org/10.1186/s13568-015-0107-6 https://doi.org/10.1016/j.bej.2015.05.007 https://doi.org/10.1016/j.bej.2015.05.007 HUNGARIAN JOURNAL OF INDUSTRY AND CHEMISTRY Vol. 48(2) pp. 45–49 (2020) hjic.mk.uni-pannon.hu DOI: 10.33927/hjic-2020-26 ADSORPTION OF NICKEL IONS FROM PETROLEUM WASTEWATER ONTO CALCINED KAOLIN CLAY: ISOTHERM, KINETIC AND THERMODY- NAMIC STUDIES ALEXANDER ASANJA JOCK *1, ANIETIE NDARAKE OKON1, UCHECHUKWU HERBERT OFFOR1, FESTUS THOMAS2, AND EDMOND OKWUDILICHUKWU AGBANAJE3 1Department of Chemical and Petroleum Engineering, University of Uyo, PMB 1017, Uyo, NIGERIA 2Department of Engineering Infrastructure, National Agency for Science and Engineering Infrastructure (NASENI), Idu Industrial Area, PMB 391, Garki-Abuja, NIGERIA 3Chemical and Petroleum Technique, Department of Science Laboratory Technology, University of Jos, PMB 2084, Jos, NIGERIA The removal of nickel ions onto calcined kaolin clay using a batch adsorption technique was conducted. The effect of the adsorbent mass, contact time and temperature on the removal process was investigated. The calcined kaolin clay was characterized using X-ray fluorescence (XRF) and Fourier-Transform InfraRed spectroscopy (FTIR). The adsorption data was analyzed by isotherm, kinetic and thermodynamic studies. The major chemical components in the clay are alumina (41.14 wt %) and silica (53.16 wt %). FTIR showed that the functional groups of aluminium monoxide (Al-O) and silicon monoxide (Si-O) are present in the clay. The study yielded a removal efficiency of 89.89% for nickel ions at 25 ◦C. The adsorption process appeared to follow a Freundlich isotherm and pseudo-second order kinetics were found to be in good agreement with the experimental data. The thermodynamics of the rate processes showed the adsorption of nickel ions to be endothermic and negative values of Gibbs free energy indicated the spontaneity of these processes. This proves that calcined kaolin clay is a good material for the removal of nickel ions from the wastewater produced by petroleum refineries. Keywords: Adsorption, Kaolin, Nickel, Wastewater 1. Introduction Environmental pollution is an anthropogenic phe- nomenon and mainly a result of industrialization. The contamination of bodies of water by the indiscriminate disposal of heavy metals has led to serious threats to hu- mans as well as aquatic and living creatures. Nickel com- pounds are highly toxic contaminants and are emitted into the environment from various industries, e.g. min- ing, metal coatings, batteries, chemical, tanneries, etc., in quantities that pose risks to human health [1]. Many wastewater treatment methods have been intro- duced to control water pollution such as chemical precip- itation, ion exchange, electrodialysis, reverse osmosis as well as membrane filtration and adsorption. Adsorption is one of the most efficient techniques due to its simplicity and affordability, moreover, it is more feasible even at low concentrations of heavy metal ions [2]. The adsorbent used for the adsorption process is of organic origin, e.g. activated carbon and biosorbents, or mineral origin, e.g. natural zeolite, calcium silicate powder and natural clay *Correspondence: alsanja@gmail.com [3]. Activated carbon is the most commonly used adsor- bent for wastewater treatment but due to it expense, low- cost alternatives such as clay, coal, fly ash, peat, siderite, agricultural wastes and charcoal are being developed. Low-cost adsorbents are those that require little process- ing and are abundant in nature, by-products or waste ma- terials from industry [4]. Clay minerals such as kaolinite, montmorillonite, vermiculite and illite are potential ad- sorbents of heavy metals. They have several economic advantages and intrinsic characteristics, e.g. are readily available, inexpensive, have excellent textural and surface properties, are physically and chemically stable in harsh environments as well as offer a cost-effective alternative to the conventional treatment of wastewater [5]. The aim of this study is to investigate the removal of nickel ions from wastewater produced by petroleum refineries using calcined kaolin clay from Alkaleri in North-East Nigeria. 2. Materials and Methods 2.1 Beneficiation and calcination of samples 10 kg of raw kaolin clay was crushed and soaked in water for 24 hours. The clay-water mixture was plunged for 3 https://doi.org/10.33927/hjic-2020-26 mailto:alsanja@gmail.com 46 JOCK, OKON, OFFOR, THOMAS, AND AGBANAJE hours at room temperature. Colloidal kaolin clay was sep- arated from the quartz-rich sediment and sieved through a 230 mesh Tyler sieve to remove other coarse impuri- ties. The thickened clay was then put in a filter cloth and pressed under hydraulic pressure to squeeze out the wa- ter. The cake was dried in an oven at 110 ◦C to constant weight before being pulverized. 100 g of the beneficiated clay was fired gradually in an electric furnace at 650 ◦C for 3 hours before being soaked. The calcined clay adsorbent was cooled, charac- terized and used for adsorption experiments. 2.2 Batch Adsorption The wastewater used of a known initial concentration of nickel ions was obtained from the effluent of a petroleum refinery operated by Kaduna Refining and Petrochemi- cals Company. The batch experiments were conducted by varying the adsorbent mass, contact time and temperature as described. The effect of the adsorbent mass was deter- mined at 25 ◦C and 0.5 g of oven-dried calcined kaolin was mixed with 50 mL of wastewater in a 250 mL Erlen- meyer flask. The mixture was stirred with a magnetic stir- rer at 200 rpm for between 10 and 50 mins. The process was repeated by varying the adsorbent mass in increments of 0.5 g, namely 1.0, 1.5, 2.0 and 2.5 g. The effect of the contact time was analyzed using 0.5 g of adsorbent after 10, 20, 30, 40 and 50 mins. The effect of the temperature was investigated within the range of 25-65 ◦C following a contact time of 30 mins. with adsorbent masses of 0.5 and 2.5 g. The residual Ni(II) ions obtained from the fil- trate and determined by Atomic Absorption Spectroscopy (AAS) were analyzed to evaluate the percentage removal, adsorption kinetics and thermodynamics. 3. Results and Discussion 3.1 Characterization of the adsorbent Chemical composition The chemical composition of the adsorbent is shown in Table 1. The main components of the clay are SiO2 (53.158 wt %) and Al2O3 (41.143 wt %). Metallic ox- ides such as TiO2, MgO and Fe2O3 are present in small amounts, while traces of CaO, Cr2O3, ZnO and Mn2O3 are detected. The large amounts of SiO2 and Al2O3 present de- fine the sample as an aluminosilicate clay. Generally speaking, kaolin clay, the chemical formula of which is Al2Si2O5(OH)4, is principally composed of SiO2, Al2O3 and water [6]. Figure 1: Fourier-transform infrared spectrum of calcined kaolin clay Fourier-transform infrared spectroscopy Fourier Transform InfraRed spectroscopy (FTIR) shows the functional groups present in the sample of clay. The FTIR spectra of the kaolin clay shown in Fig. 1 are within the wavenumber range of 4000 - 400 cm−1. The spectra depict three major absorption bands, namely silicon diox- ide, alumina and hydroxyl groups. The peaks at 1030, 1045 and 1049 cm−1 are assigned to the stretching vi- brations of the Si–O bond and the peak observed at 922 cm−1 corresponds to the Al–Al–OH bonds. Peaks at 733, 750 and 752 cm−1 indicate the presence of OH result- ing from the expulsion of water and hydroxyl groups in clay minerals during calcination [7]. The differences in the peak intensities can be attributed to the interaction of Ni(II) ions with functional groups on the kaolin adsorbent surface [8]. 3.2 Adsorption studies Effect of adsorbent mass The adsorbent mass plays a vital role in adsorption pro- cesses. It determines the percentage removal of metal ions and is calculated by: %Ads = ci − cf ci (1) where %Ads denotes the amount of Ni(II) ions removed, and ci and cf stand for the initial and final concentrations (ppm) of the Ni(II) ions, respectively. The percentage removal of Ni(II) ions increased from 76.31 to 89.04% when the adsorbent mass was increased from 0.5 to 2.5 g as shown in Fig. 2. As the adsorbent mass increases, more adsorption sites of nickel ions become available. Effect of contact time on the uptake of nickel ions The adsorption isotherm describes the adsorption pattern between the Ni(II) ions adsorbed on the kaolin clay and Table 1: Chemical composition of the calcined kaolin sample Components SiO2 Al2O3 TiO2 MgO Fe2O3 CaO Cr2O3 ZnO Mn2O3 Amount (wt %) 53.158 41.143 3.017 0.442 0.126 0.044 0.018 0.013 0.008 Hungarian Journal of Industry and Chemistry ADSORPTION OF NICKEL IONS ONTO CALCINED KAOLIN CLAY 47 Figure 2: Effect of adsorbent mass on the removal of nickel ion Figure 3: Effect of contact time on the uptake of nickel ions the residual ions. The equilibrium uptake was determined using: qe = (ci − ce)V m (2) where ce denotes the equilibrium concentration, V stands for the volume of the solution and m represents the ad- sorbent mass. Fig. 3 shows that the uptake of nickel ions is increased by increasing the contact time and reaches a maximum or saturation point after 30 to 40 mins., and thereafter the rate of adsorption remains almost constant even as the contact time and adsorbent mass are further increased. The extent of the adsorption of nickel ions initially increased rapidly and then gradually until an equilibrium was attained. The high removal rate was due to the large surface area initially available for adsorption of Ni(II) ions but the capacity of the adsorbent was gradually ex- hausted over time since the occupation of the few vacant surface sites that remained was inhibited due to repul- sive forces between the solute molecules in the solid and bulk phases [9]. As the adsorption sites on the surface be- come exhausted, the uptake rate is controlled by the rate at which the nickel ions are transported from the exterior to the interior sites of the adsorbent particles [10]. It was reported that during the adsorption of metal ions, initially the Ni(II) ions reach the boundary layer; then have to dif- fuse onto the surface of the adsorbent and finally, must diffuse into its porous structure. Therefore, this process requires a relatively longer contact time [11]. Figure 4: Langmuir isotherm for the adsorption of nickel ions Figure 5: Freundlich isotherm for the adsorption of nickel ions 3.3 Equilibrium isotherms The Langmuir and Freundlich models describe this isotherm: 1 qe = 1 qm + 1 qmbce (3) and log qe = logKF + 1 n log ce, (4) where qe denotes the uptake of Ni(II) ions adsorbed on the clay (mg/g), qm and b stand for the single-layer ad- sorption capacity (mg/g) and the Langmuir equilibrium constant (L/mg), respectively, and KF, n and b represent Freundlich adsorption constants. The Langmuir and Freundlich constants shown in Ta- ble 2 were determined from the gradients and intercepts using the equations displayed in Figs. 4 and 5. The mag- nitudes of KF and n are 0.2535 (mg/g)(L/mg)1/n and −5.08 L/mg, respectively. The constant, n, is related to the ionic strength with regard to the adsorption of Ni(II) ions and KF is related to both the ionic strength and amount of Ni(II) ions adsorbed. The significance of n is as follows: n < 1 (chemical process); n = 1 (linear pro- cess) and n > 1 (physical process). The negative value of n (−5.08 L/mg) obtained is indicative of chemical ad- sorption [12]. The Langmuir constant, b, shows the affinity of bind- ing sites for nickel ions of the adsorbent. Similarly, the 48(2) pp. 45–49 (2020) 48 JOCK, OKON, OFFOR, THOMAS, AND AGBANAJE Table 2: Parameters of Freundlich and Langmuir isotherm models Freundlich Model n (L/mg) KF (mg/g)(L/mg)1/n R2 (%) -5.08 0.2535 98.40 Langmuir Model qm (mg/g) b (L/mg) R2 (%) 0.2378 -11.5210 94.00 Table 3: Pseudo-kinetics constants for the adsorption of nickel onto calcined kaolin Pseudo-first order kinetics Pseudo-second order kinetics K1 = −0.0230 (L/min) K2 = 0.0073 (mg/(mg/min)) qe = 1.127 (mg/g) qe = 5.291 (mg/g) R2 = 0.87 R2 = 0.98 negative value of b (−11.5210 L/mg) suggests a low de- gree of adsorption of Ni(II) ions by kaolin clay as is shown by the maximum adsorption capacity, qm (0.2378 mg/g). The experimental data fitted well in the Freundlich model (R2 = 98.40%) indicating multilayer adsorption on the heterogeneous surface. 3.4 Adsorption kinetics The kinetics data were determined using the linear equa- tions of pseudo-first and second order kinetics: log (qe − qt) = log qe − 1 2.303 K1t (5) and t qt = 1 K2q2e + t qe . (6) The parameters of adsorption kinetics are useful to pre- dict the adsorption rate and provide considerable infor- mation to design and model the adsorption process as well as evaluate the adsorbent and operation control [13]. Figs. 6 and 7 showed pseudo-first and second order ki- netics, respectively with regard to the adsorption of Ni(II) ions. The kinetics constants are summarized in Table 3. The pseudo-first order kinetics exhibit a higher rate constant (K1) and lower uptake (qe). According to the values of R2 in Table 3, it is clear that pseudo-second or- der kinetics fitted better to the adsorption data. This sug- gests the adsorption process is controlled by a chemisorp- tion mechanism and the rate-limiting step is probably the surface adsorption of nickel ions [5]. 3.5 Adsorption thermodynamics The effect of temperature on the adsorption of nickel ions was investigated between 25 and 65 ◦C. The thermody- namic parameters determined from equations Kc = qe ce (7) Figure 6: Pseudo-first order kinetics for the adsorption of nickel ions onto calcined kaolin clay Figure 7: Pseudo-second order kinetics for the adsorption of nickel ions onto calcined kaolin clay ∆G◦ = −RT lnKc (8) lnKc = −∆H◦ RT + ∆S◦ R (9) ∆G◦ = ∆H◦ − T∆S◦ (10) include changes in Gibbs free energy (∆G◦), enthalpy (∆H◦) and entropy (∆S◦). Fig. 8 depicts the Van’t Hoff plots used to evaluate the thermodynamic parameters summarized in Table 4. The negative values of ∆G◦ con- firm that the adsorption process is feasible and sponta- neous while the positive values of ∆H◦ show that the adsorption process of Ni(II) ions is endothermic. ∆H◦ can indicate the type of adsorption process in- volved. If ∆H◦ of the adsorbent exceeds 40 or is less than 20 kJ/mol, chemisorption or adsorption that is phys- ical in nature occurs, respectively [8]. The positive values of ∆H◦ and ∆S◦ obtained show that the adsorption pro- cess is physical in nature and the solid-aqueous solution interface becomes more irregular and random during the adsorption of Ni(II) ions by the calcined kaolin adsor- bent. 4. Conclusions Thermally activated kaolin clay as an adsorbent was suc- cessfully prepared by calcination and used to remove Hungarian Journal of Industry and Chemistry ADSORPTION OF NICKEL IONS ONTO CALCINED KAOLIN CLAY 49 Figure 8: Effect of temperature with regard to the adsorp- tion of nickel (II) ions on calcined kaolin Table 4: Thermodynamic parameters for the adsorption of nickel (II) ions onto calcined kaolin at 25 ◦C Adsorbent mass (g) ∆G◦ (J/mol) ∆S◦ (J/mol K) ∆H◦(J/mol) R2 0.5 -875.26 62.27 17600.74 0.979 2.5 -2553.22 111.07 30545.64 0.951 nickel ions from wastewater produced by a petroleum re- finery. Adsorption isotherms, kinetics and thermodynam- ics were also studied. It was discovered that the adsor- bent mass, contact time and temperature significantly in- fluenced the adsorption of nickel ions onto the calcined kaolin adsorbent. The removal efficiency was increased by increasing the adsorbent mass, contact time and tem- perature. The adsorption data were described well by a Freundlich isotherm and pseudo-second order kinet- ics fitted well to the adsorption process. The negative values of ∆G◦ indicated that the adsorption of nickel ions was feasible and spontaneous. The positive values of ∆H◦ showed that the process was endothermic and irre- versible. Generally speaking, the results revealed that cal- cined kaolin is a potential adsorbent for the treatment of wastewater laden with nickel ions produced by petroleum refineries. REFERENCES [1] Padmavathy, K. S.; Madhu, G.; Haseena, P. V.: A study on effects of pH, adsorbent dosage, time, initial concentration and adsorption isotherm study for the removal of hexavalent chromium (Cr(VI)) from wastewater by magnetite nanopar- ticles. Procedia Technol., 2016, 24, 585–594 DOI: 10.1016/j.protcy.2016.05.127 [2] Zubir, M. H. M.; Zaini, A. A. M.: Twigs- derived activated carbons via H3PO4/ZnCl2 com- posite activation for methylene blue and congo red dyes removal. Sci. Rep., 2020, 10(1), 14050 DOI: 10.1038/s41598-020-71034-6 [3] Es-sahbany, H.; Berradi, M.; Nkhili, S.; Hsissou, R.; Allaoui, M.; Loutfi M.; Bassir, D.; Belfaquir, M; El Youbi, S. M.: Removal of heavy metals (nickel) contained in wastewater-models by the adsorption technique on natural clay. Mater. Today: Proc., 2019, 13(3), 866–875 DOI: 10.1016/j.matpr.2019.04.050 [4] Alhawas, M.; Alwabel, M.; Ghoneim, A.; Al- farraj, A; Sallam, A.: Removal of nickel from aqueous solution by low-cost clay adsorbents. Proc. Int. Acad. Ecol. Env. Sci., 2013, 3(2), 160– 169 http://www.iaees.org/publications/ journals/piaees/articles/2013-3(2) /removal-of-nickel-from-aqueous-solution. pdf [5] Jock, A. A.; Muhammad, A. A. Z.; Abdul- salam, S.; El-Nafaty, U. A.; Aroke, U. O.: In- sight into kinetics and thermodynamics properties of multicomponent lead(II), cadmium(II) and man- ganese(II) adsorption onto Dijah-Monkin bentonite clay. Part. Sci. Technol., 2018, 36(5), 569–577 DOI: 10.1080/02726351.2016.1276499 [6] Jongs, S. L.; Jock, A. A.; Ekanem, E. O.; Jauro, A.: Statistical analysis on physico-chemical properties of some Nigerian clay deposits. J. Mat. Sci. Chem. Eng., 2019, 7(8), 52–63 DOI: 10.4236/msce.2019.78007 [7] Jock, A. A.; Muhammad, A. A. Z.; Abdulsalam, S.; El-Nafaty, U. A.; Aroke, U. O.: Physicochemical characteristics of surface modified Dijah-Monkin bentonite. Part. Sci. Technol., 2016, 36(3), 287–297 DOI: 10.1080/02726351.2016.1245689 [8] Gorzin, F.; Abadi, M. M. B. R.: Adsorption of Cr(VI) from aqueous solution by adsorbent pre- pared from paper mill sludge: Kinetics and ther- modynamics studies. Adsorp. Sci. Technol., 2018, 36(1-2), 149–169 DOI: 10.1177/0263617416686976 [9] Kumar, P. S.; Sivaprakash, S.; Jayakumar, N.: Re- moval of methylene blue dye from aqueous solu- tions using Lagerstroemia indica seed (LIS) acti- vated carbon. Int. J. Mat. Sci., 2017, 12(1), 107– 116 https://www.ripublication.com/ijoms17/ ijomsv12n1_12.pdf [10] Alzaydien, A. S.: Adsorption behavior of methyl orange onto wheat bran: Role of surface and pH. Orient. J. Chem., 2015, 31(2), 643–651 DOI: 10.13005/ojc/310205 [11] Khodaie, M.; Ghasemi, N.; Moradi, B; Rahimi, M.: Removal of methylene blue from wastewater by adsorption onto ZnCl2 activated corn husk carbon equilibrium studies. J. Chem., 2013, 2013, 1–6 DOI: 10.1155/2013/383985 [12] Marrakchi, F.; Hameed, B. H.; Hummadi, E. H.: Mesoporous biohybrid epichlorohydrin crosslinked chitosan/carbon–clay adsorbent for effective cationic and anionic dyes adsorption. Int. J. Biol. Macromol., 2010, 163, 1079–1086 DOI: 10.1016/j.ijbiomac.2020.07.032 [13] Deniz, F.: Adsorption properties of low-cost bioma- terial derived from Prunus amygdalus L. for dye re- moval from water. Sci. World J., 2013, 2013, 1–8 DOI: 10.1155/2013/961671 48(2) pp. 45–49 (2020) https://doi.org/10.1016/j.protcy.2016.05.127 https://doi.org/10.1016/j.protcy.2016.05.127 https://doi.org/10.1038/s41598-020-71034-6 https://doi.org/10.1038/s41598-020-71034-6 https://doi.org/10.1016/j.matpr.2019.04.050 http://www.iaees.org/publications/journals/piaees/articles/2013-3(2)/removal-of-nickel-from-aqueous-solution.pdf http://www.iaees.org/publications/journals/piaees/articles/2013-3(2)/removal-of-nickel-from-aqueous-solution.pdf http://www.iaees.org/publications/journals/piaees/articles/2013-3(2)/removal-of-nickel-from-aqueous-solution.pdf http://www.iaees.org/publications/journals/piaees/articles/2013-3(2)/removal-of-nickel-from-aqueous-solution.pdf https://doi.org/10.1080/02726351.2016.1276499 https://doi.org/10.1080/02726351.2016.1276499 https://doi.org/10.4236/msce.2019.78007 https://doi.org/10.1080/02726351.2016.1245689 https://doi.org/10.1177/0263617416686976 https://www.ripublication.com/ijoms17/ijomsv12n1_12.pdf https://www.ripublication.com/ijoms17/ijomsv12n1_12.pdf https://doi.org/10.13005/ojc/310205 https://doi.org/10.13005/ojc/310205 https://doi.org/10.1155/2013/383985 https://doi.org/10.1155/2013/383985 https://doi.org/10.1016/j.ijbiomac.2020.07.032 https://doi.org/10.1016/j.ijbiomac.2020.07.032 https://doi.org/10.1155/2013/961671 HUNGARIAN JOURNAL OF INDUSTRY AND CHEMISTRY Vol. 48(2) pp. 51–53 (2020) hjic.mk.uni-pannon.hu DOI: 10.33927/hjic-2020-27 CONSIDERATIONS TO APPROACH MEMBRANE BIOFOULING IN MICRO- BIAL FUEL CELLS SZABOLCS SZAKÁCS *1 AND PÉTER BAKONYI1 1Research Institute on Bioengineering, Membrane Technology and Energetics, University of Pannonia, Egyetem u. 10, 8200 Veszprém, HUNGARY Among bioelectrochemical systems, those referred to as microbial fuel cells (MFCs) are widely implemented for wastew- ater management and simultaneous recovery of electrical energy. MFCs are fundamentally assisted by bacterial popu- lations, mostly mixed cultures to be more exact that, after oxidizing the substrate, are capable of facilitating the passage of electrons to an electron acceptor, usually the anode. However, certain undesired bacterial strains often colonize not only the electrode but the membrane separator over time and cause severe biofouling. The membrane is an architectural element of many MFCs and determines the efficiency of the system. In this paper, this issue is overviewed briefly and some considerations concerning how to approach the problem are presented. Keywords: microbial fuel cell, membrane, oxygen mass transfer, biofouling 1. Introduction Bioelectrochemical systems, including Microbial Fuel Cells (MFCs), are often installed with a physical sepa- rator such as a membrane as illustrated in Fig. 1 [1]. The membrane, which separates the anode from the cathode, should enable the adequate migration of ions (in this case, cations such as protons) between the elec- trodes to ensure the MFC continues to produce electricity [2]. Furthermore, the membrane should function as a bar- rier against the crossover effect of substances to avoid the loss of the substrate (which normally is injected into the anode chamber in order to feed the species of electroac- tive bacteria shown in Table 1 living on a biofilm on the electrically-conductive anode surface) and penetration of dissolved oxygen from the aerated cathode chamber to the anaerobic anode chamber [3]. In addition to these requirements which are associ- ated with the physical and chemical properties of the material, the membranes should be relatively affordable. Another point that needs to be addressed is the stability of the membrane, which can be influenced by the com- plex chemical environment of the bulk phases (anolyte in the anode chamber, catholyte in the cathode chamber) in an MFC and microbiological phenomena [5]. Altogether, these effects may cause fouling of the membrane during its operation, moreover, when the underlying mechanism is associated with the metabolism and/or growth of mi- croorganisms, the term “biofouling” is more appropriate [6]. In summary, various membranes can be evaluated *Correspondence: szakacs.szabolcs@mk.uni-pannon.hu Figure 1: Scheme of an MFC and ranked based on the characteristics of the membrane materials (typically but not exclusively fabricated from polymers) and their observed behavior during MFC op- eration which can be monitored by various electrochemi- cal measurements, e.g., recording the cell voltage, as pre- sented in Fig. 2. 2. Membranes and biofouling in MFCs – The potential role of oxygen mass trans- fer In accordance with the previous section, the membrane separator divides the anaerobic anode chamber from the aerobic cathode chamber [7]. Therefore, it is reasonable to assume that the actual microbial communities develop- ing on the anode and the membrane have completely dif- ferent structures and relationships with gaseous oxygen. From the literature, it can be concluded with a good de- gree of certainty that the electrochemically active bacteria https://doi.org/10.33927/hjic-2020-27 mailto:szakacs.szabolcs@mk.uni-pannon.hu 52 SZAKÁCS, AND BAKONYI Table 1: Electroactive species of bacteria and their oxygen tolerance [4] Species Oxygen tolerance E. coli facultatively anaerobic https://bacdive.dsmz.de/strain/4907 Shewanella oneidensis facultatively anaerobic https://img.jgi.doe.gov/cgi-bin/m/main.cgi?section= TaxonDetail&page=taxonDetail&taxon_oid=637000258 Geobacter sulfurreducens strict anaerobe https://bacdive.dsmz.de/strain/5792 Geobacter metallireducens strict anaerobe https://bacdive.dsmz.de/strain/5791 Desulfobulbus propionicus strict anaerobe https://bacdive.dsmz.de/strain/4004 Geothrix fermentans strict anaerobe https://bacdive.dsmz.de/strain/17672 Paracoccus pantotrophus facultatively anaerobic https://bacdive.dsmz.de/strain/13703 Rhodopseudomonas palustris DX-1 strict anaerobe https://bacdive.dsmz.de/strain/1819 on the anode are either strict or facultative anaerobes as shown in Table 1 [4]. However, a considerable knowledge gap seems to exist concerning the populations attached to the surface of the membrane and the occurrence of bio- fouling. In contrast, it is reasonable to suppose that these membrane-bound colonies and biofilms are more toler- ant of dissolved oxygen due to the technically direct and long-lasting contact with this substance. In general, the oxygen flux across a membrane in MFCs is described by the oxygen transfer coefficient which can be calculated by [8, 9] kO = − V At ln [ (C0 − C) C0 ] (1) where kO denotes the oxygen transfer coefficient (cm3/cm2s), V stands for the volume of liquid (cm3), A represents the surface area of the membrane (cm2), C0 refers to the saturation oxygen concentration (mol/dm3), C is the actual oxygen concentration mea- sured (mol/dm3) and t denotes the time of the measure- ment (s); and kO = DO L (2) where DO stands for the oxygen diffusion coefficient (cm2/s) and L represents the thickness of the membrane (cm). Figure 2: Schematic diagram of the MFC Therefore, it is worth examining which strains col- onize the membrane and how these mixed communi- ties vary depending on ko. Even though some previ- ous papers have demonstrated the use of various micro- scopic imaging techniques based on visual observations to study these biofouling layers on the surface of mem- branes [10–12], qualitative and quantitative feedback to highlight “what type of” and “how many” microbes can coexist is scarce. As a result, experimental methodology involving the apparatus of modern molecular biology, e.g. DNA-based identification, should be encouraged and im- plemented to answer such questions [13]. 3. Conclusions The capability of membranes to permeate dissolved oxy- gen in microbial fuel cells is key. On the one hand, re- duced oxygen transport membranes (OTMs) are likely to maintain the typically less oxygen tolerant, electroactive bacteria located on the surface of the anode in a good con- dition. On the other hand, oxygen mass transfer through the membrane is expected to affect the biofouling of the separator and thus, how microbial communities respond to changes in material properties, in particular ko, needs to be understood. The assessment of membranes with different values of ko should be carried out relative to Nafion, which is by far the most broadly employed poly- mer for benchmarking studies [14]. Acknowledgement This work was supported by the National Research, De- velopment and Innovation Office (NKFIH, Hungary) un- der grant number FK 131409 and GINOP-2.3.2-15-2016- 00016 Excellence of strategic R+D workshops, entitled “Development of modular, mobile water treatment sys- tems and waste water treatment technologies based on University of Pannonia to enhance growing dynamic ex- port of Hungary” (2016-2020) Hungarian Journal of Industry and Chemistry https://bacdive.dsmz.de/strain/4907 https://img.jgi.doe.gov/cgi-bin/m/main.cgi?section=TaxonDetail&page=taxonDetail&taxon_oid=637000258 https://img.jgi.doe.gov/cgi-bin/m/main.cgi?section=TaxonDetail&page=taxonDetail&taxon_oid=637000258 https://bacdive.dsmz.de/strain/5792 https://bacdive.dsmz.de/strain/5791 https://bacdive.dsmz.de/strain/4004 https://bacdive.dsmz.de/strain/17672 https://bacdive.dsmz.de/strain/13703 https://bacdive.dsmz.de/strain/1819 MEMBRANE BIOFOULING IN MICROBIAL FUEL CELLS 53 REFERENCES [1] Leong, J. X.; Daud, W. R. W.; Ghasemi, M.; Liew, K. B.; Ismail, M.: Ion exchange membranes separa- tors in microbial fuel cells for bioenergy conversion: A comprehensive review. Renew. Sustain. Energy Rev., 2013, 28, 575–587 DOI: 10.1016/j.rser.2013.08.052 [2] Daud, S. M.; Kim, B. H.; Ghasemi, M.; Daud, W. R. W.: Separators used in microbial electro- chemical technologies: Current status and future prospects. Bioresour. Technol., 2015, 195, 170–179 DOI: 10.1016/j.biortech.2015.06.105 [3] Bakonyi, P.; Koók, L.; Kumar, G.; Tóth, G.; Rózsen- berszki, T.; Nguyen, D. D.; Chang, S. W.; Zhen, G.; Bélafi-Bakó, K.; Nemestóthy, N.: Architectural en- gineering of bioelectrochemical systems from the perspective of polymeric membrane separators: A comprehensive update on recent progress and fu- ture prospects. J. Memb. Sci., 2018, 564, 508–522 DOI: 10.1016/j.memsci.2018.07.051 [4] Sharma, V.; Kundu, P. P.: Biocatalysts in microbial fuel cells. Enzyme Microb. Technol., 2010, 47(5), 179–188 DOI: 10.1016/j.enzmictec.2010.07.001 [5] Koók, L.; Bakonyi, P.; Harnisch, F.; Kretzschmar, J.; Chae, K.-J.; Zhen, G.; Kumar, G.; Rózsenber- szki, T.; Tóth, G.; Nemestóthy, N.; Bélafi-Bakó, K.: Biofouling of membranes in microbial electrochem- ical technologies: Causes, characterization methods and mitigation strategies. Bioresour. Technol., 2019, 279, 327–338 DOI: 10.1016/j.biortech.2019.02.001 [6] Noori, Md. T.; Ghangrekar, M. M.; Mukherjee, C. K.; Min, B.: Biofouling effects on the perfor- mance of microbial fuel cells and recent advances in biotechnological and chemical strategies for mit- igation. Biotechnol. Adv., 2019, 37(8), 107420 DOI: 10.1016/j.biotechadv.2019.107420 [7] Patil, S. A.; Gildemyn, S.; Pant, D.; Zengler, K.; Lo- gan, B. E.; Rabaey, K.: A logical data representation framework for electricity-driven bioproduction pro- cesses. Biotechnol. Adv., 2015, 33(6), 736–744 DOI: 10.1016/j.biotechadv.2015.03.002 [8] Chae, K. J.; Choi, M.; Ajayi, F. F.; Park, W.; Chang, I. S.; Kim, I. S.: Mass transport through a pro- ton exchange membrane (Nafion) in microbial fuel cells. Energy and Fuels, 2008, 22(1), 169–176 DOI: 10.1021/ef700308u [9] Kim, J. R.; Cheng, S.; Oh, S.-E.; Logan, B. E.: Power generation using different cation, anion, and ultrafiltration membranes in microbial fuel cells. Environ. Sci. Technol., 2007, 41(3), 1004–1009 DOI: 10.1021/es062202m [10] Venkatesan, P. N.; Dharmalingam, S.: Effect of cation transport of SPEEK - Rutile TiO2 electrolyte on microbial fuel cell performance. J. Memb. Sci., 2015, 492, 518–527 DOI: 10.1016/j.memsci.2015.06.025 [11] Angioni, S.; Millia, L.; Bruni, G.; Tealdi, C.; Mustarelli, P.; Quartarone, E.: Improving the performances of NafionTM-based mem- branes for microbial fuel cells with silica-based, organically-functionalized mesostructured fillers. J. Power Sources, 2016, 334, 120–127 DOI: 10.1016/j.jpowsour.2016.10.014 [12] Mokhtarian, N.; Ghasemi, M.; Daud, W. W. R.; Is- mail, M.; Najafpour, G.; Alam, J.: Improvement of microbial fuel cell performance by using nafion polyaniline composite membranes as a separator. J. Fuel Cell Sci. Technol., 2013, 10(4), 041008 DOI: 10.1115/1.4024866 [13] Saratale, R. G.; Saratale, G. D.; Pugazhendhi, A.; Zhen, G.; Kumar, G.; Kadier, A.; Sivagurunathan, P.: Microbiome involved in microbial electrochemi- cal systems (MESs): A review. Chemosphere, 2017, 177, 176–188 DOI: 10.1016/j.chemosphere.2017.02.143 [14] Rahimnejad, M.; Bakeri, G.; Ghasemi, M.; Zire- pour, A.: A review on the role of proton exchange membrane on the performance of microbial fuel cell. Polym. Adv. Technol., 2014, 25(12), 1426–1432 DOI: 10.1002/pat.3383 48(2) pp. 51–53 (2020) https://doi.org/10.1016/j.rser.2013.08.052 https://doi.org/10.1016/j.biortech.2015.06.105 https://doi.org/10.1016/j.memsci.2018.07.051 https://doi.org/10.1016/j.enzmictec.2010.07.001 https://doi.org/10.1016/j.biortech.2019.02.001 https://doi.org/10.1016/j.biotechadv.2019.107420 https://doi.org/10.1016/j.biotechadv.2019.107420 https://doi.org/10.1016/j.biotechadv.2015.03.002 https://doi.org/10.1016/j.biotechadv.2015.03.002 https://doi.org/10.1021/ef700308u https://doi.org/10.1021/ef700308u https://doi.org/10.1021/es062202m https://doi.org/10.1021/es062202m https://doi.org/10.1016/j.memsci.2015.06.025 https://doi.org/10.1016/j.jpowsour.2016.10.014 https://doi.org/10.1016/j.jpowsour.2016.10.014 https://doi.org/10.1115/1.4024866 https://doi.org/10.1115/1.4024866 https://doi.org/10.1016/j.chemosphere.2017.02.143 https://doi.org/10.1002/pat.3383 HUNGARIAN JOURNAL OF INDUSTRY AND CHEMISTRY Vol. 48(2) pp. 55–58 (2020) hjic.mk.uni-pannon.hu DOI: 10.33927/hjic-2020-28 NEW CHAINING CRITERIA IN DIPOLAR FLUIDS BASED ON MONTE CARLO SIMULATIONS SÁNDOR NAGY *1,2 1Berzsenyi Dániel Teacher Training Centre, Eötvös Loránd University, Károlyi Gáspár tér 4. A-building, 9700 Szombathely, HUNGARY 2Institute of Mechatronics Engineering and Research, University of Pannonia, Gasparich Márk u. 18/A, 8900 Zalaegerszeg, HUNGARY A new energy-based chaining criterion was introduced in dipolar systems based on an earlier article by the author, in which the probability of chaining for adjacent particles in a new formula of magnetic susceptibility was used. The probability of chaining and the magnitude of the energy criterion can be calculated from the Monte Carlo (MC) simulation values of magnetic susceptibility. The energy criterion also depends on the dipole moment and the density. At high densities, the energy criterion is well below 70−75%. In addition, it was confirmed by simulation results that the chain length distribution follows a geometric distribution. How the probability of chaining depends on the energy criterion was given empirically and two parameters were fitted to it. Keywords: dipolar fluids, chain formation, energy criterion 1. Introduction According to the literature, the criterion of chaining in dipolar systems is unclear. Usually, an energy criterion is used to decide whether two adjacent particles form part of a chain. The energy criterion defines the limit at a cer- tain level of interaction energy between them, which is usually 70 − 75% of the minimum pair interaction en- ergy [1–4]. According to another definition, if the pair interaction energy is negative and the two particles are closer together than 1.3 in diameter unit, then chaining occurs [5, 6]. The problem with this is that the minimum pair interaction energy depends on the magnitude of the dipole moment, so an identical amount of pair interaction energy between two adjacent particles indicates chaining in one case but not in the other. In another article, the author examined [7] the probability density function of pair interaction energies in a dipolar hard sphere (DHS) system and found that no unit jump-like change in the frequency of the pair interaction energy would justify the introduction of a general criterion based on the pair in- teraction energy. Furthermore, such a general definition does not take into account the effect of density, while it can be assumed that chaining occurs at different densities and different energy levels of the same dipole moments. Therefore, this study seeks to determine the magnitude of the energy criterion from another source, that is, a real, measurable, physical parameter. The physical parameter *Correspondence: sata123.sandor@gmail.com in this case is magnetic susceptibility. In a previous article [8], the author stated that the chain length distribution in dipolar fluids follows a ge- ometric distribution. If the probability that two adjacent particles form a chain is denoted by p, it can be deduced that the chain length distribution is gk = (1− p) pk−1, (1) where k stands for the chain length. The particle size dis- tribution, which yields the proportion of particles in ex- actly k-long chains is hk = (1− p)2kpk−1. (2) The average chain length is derived from the properties of the geometric distribution: 1/(1− p). Assuming that a chain of length k behaves as if its dipole moment is km, its initial magnetic susceptibility can be deduced (in c.g.s. units) as χ0 = 1 + p 1− p χL ( 1 + 4π 3 χL ) , (3) where χL denotes Langevin susceptibility [9], χL = ρm2 3kBT , (4) ρ stands for the density, T represents the temperature, and kB refers to the Boltzmann constant. In Eq. 3, the expres- sion χP = χL ( 1 + 4π 3 χL ) (5) https://doi.org/10.33927/hjic-2020-28 mailto:sata123.sandor@gmail.com 56 NAGY calculates the initial magnetic susceptibility from Pshenichnikov’s theory [10]. Eq. 3 can be used to deter- mine the value of p at a given density and dipole moment, since χ0 can be determined from simulations and χP can be calculated exactly from p = χ0 − χP χ0 + χP . (6) If the occurrence of chaining is linked to an energy crite- rion, it is clear that the value of p depends on the magni- tude of the energy criterion. Thus, if the correct value of p is known from Eq. 6, the magnitude of the correct energy criterion can be obtained from the relationship p − Ulim. The structure of the results is as follows: firstly, through several examples, it is shown that for any energy crite- rion, the number of chains follows a geometric distribu- tion; secondly, the p values calculated from the magnetic susceptibility values are given; thirdly, from simulations, the Ulim values are determined that provide the desired p value at a given density and dipole moment; finally, by fitting, an empirical formula is derived to describe the re- lationship p− Ulim. 2. Simulations and results Monte Carlo simulations of DHS fluids were performed using a canonical NVT ensemble. Boltzmann sampling [11], periodic boundary conditions and the minimum- image convention were applied. In order to take into ac- count the long-range character of the dipolar interaction, the reaction field method under periodic boundary con- ditions of conduction was used. After 100, 000 equili- bration periods, 1, 000, 000 production cycles were con- ducted involving N = 1, 000 particles. The reduced den- sity was calculated by ρ∗ = ρσ3 and the reduced dipole moment by m∗ = m/ √ σ3kBT , where σ denotes the di- ameter of the particles. The pair interaction energy be- tween two particles in a DHS system is determined only by the dipolar energy: Udd ij = −m 2 r3ij [3 (m̂i · r̂ij) (m̂j · r̂ij)− (m̂i · m̂j)] , (7) where the particles have dipole moments of strength m of an orientation given by unit vector m̂, moreover, the distance between the centers of the particles is denoted by rij and r̂ij = rij/rij . The dots symbolize the scalar product. The lowest and most favorable energy value was determined by Udd min = −2m2/σ3. The magnitude of the energy criterion (u) was given in the usual way in propor- tion to this: u = Udd/Udd min. To determine the chain length distribution (gk), the number of chains of a given length was counted in each cycle. This required a predefined energy criterion. Rear- ranging Eq. 1 leads to the chain length distribution: lg (gk) = lg (1− p) + (k − 1) lg (p) . (8) Figure 1: The logarithm of the chain length distribution as a function of chain length at six different dipole moments, densities and energy criteria. According to Eq. 8, the value of p (probability of chaining) is also derived from the gra- dient of the fitted lines as well as the intercept of the ver- tical axis. From this, it can be seen that if the logarithm of the chain length distribution is plotted as a function of k − 1, the gradient of the fitted straight line yields the logarithm p, while the logarithm of the vertical intercept gives 1 − p. Since in each case the resulting lines are linear, it fol- lows that the chain length distribution does indeed fol- low a geometric distribution. Fig. 1 shows the simulation results for the chain length distribution obtained for six different combinations of dipole moments, densities and energy criteria. It can be seen that the fitted lines are lin- ear on the logarithmic scale in all cases. It is clear from the inset graphs that the larger its gradient, the closer its intercept is to zero. The p values shown in Fig. 1 were derived from the gradient of the fitted lines. Further results are summarized in Table 1. In the third column of Table 1, using the terms mentioned in the In- troduction (Section 1), the values of p are given. The val- ues of χP can be precisely calculated from Eq. 5. The values of χ0 are derived from the simulations. (For the simulation results of χ0, reference [12] was used. The missing χ0 data were supplemented with our own sim- ulation results.) This was followed by the determination of ulim, also from our own simulations. According to Eq. 2, the num- ber of particles that do not form a chain or, in other words, which form single-element chains is h1 = (1− p)2. Thus, in each step of the simulation, it was only nec- Hungarian Journal of Industry and Chemistry NEW CHAINING CRITERIA IN DIPOLAR FLUIDS BASED ON MONTE CARLO SIMULATIONS 57 Table 1: The probabilities of chaining (3rd column), the values of the energy criterion (4th column) according to the simulations and the values of the fitted curves (5th and 6th columns) according to Eq. 9 (m∗)2 ρ∗ p ulim A B 2 0.1 0.0135 0.74 0.203 2.902 2 0.2 0.0209 0.76 0.358 2.864 2 0.3 0.0316 0.75 0.492 2.863 2 0.4 0.0231 0.80 0.623 2.881 2 0.5 0.0254 0.80 0.751 2.893 2 0.6 0.0387 0.78 0.885 2.901 2 0.7 0.0523 0.77 1.022 2.895 2 0.8 0.1674 0.66 1.167 2.874 2 0.9 0.2641 0.62 1.307 2.821 3 0.1 0.0802 0.68 0.426 2.284 3 0.2 0.0951 0.71 0.607 2.291 3 0.3 0.0772 0.75 0.732 2.333 3 0.4 0.0721 0.77 0.839 2.388 3 0.5 0.0762 0.77 0.943 2.442 3 0.6 0.0840 0.76 1.048 2.498 3 0.7 0.1889 0.67 1.154 2.535 3 0.8 0.3788 0.57 1.270 2.560 3 0.9 0.6745 0.45 1.383 2.547 4 0.1 0.2183 0.67 0.761 1.801 4 0.2 0.1777 0.73 0.900 1.842 4 0.3 0.1278 0.77 0.975 1.900 4 0.4 0.1174 0.78 1.044 1.989 4 0.5 0.1169 0.78 1.107 2.069 4 0.6 0.1818 0.72 1.180 2.159 4 0.7 0.4016 0.59 1.258 2.237 4 0.8 0.6814 0.46 1.341 2.295 essary to count how many particles have the minimum pair interaction energy (counted individually for the other particles) greater than the examined energy criterion. This greatly simplified the complexity of the simulations. Therefore, the value of p belonging to the given energy criterion was determined, and for each dipole moment and density, a function was generated to create a relation- ship between p and ulim. Of these, four are shown in Fig. 2. The dashed lines indicate the ulim values of the already defined p values of these functions. The fourth column of Table 1 shows the ulim values for each dipole moment, density and value of p which are also plotted in Fig. 3. (The uncertainty of the func- tions shown in Fig. 3 stems from the uncertainty of the magnetic susceptibilities. For dipole moments less than (m∗)2 = 2, this new energy criterion cannot be examined precisely because the uncertainty in the magnetic suscep- tibility is too great.) It can be seen that the frequently mentioned 70 − 75% criterion is more or less valid, al- though it differs significantly from it at high densities. Interestingly, the energy criterion is higher at medium densities than at low densities. This may be because the Figure 2: The probability of chaining as a function of the value of the energy criterion at four different dipole mo- ments and densities. The dashed lines show the true values of p and thus ulim as well. chains are so close to each other at medium densities that they have an effect on each other, but do not at low den- sities. It is true that at high densities this effect is even stronger, but at the same time, the strength of the forces acting on the chain also increases. In the following, the relationships between the p − ulim functions are specified by fitting. Four of these are shown in Fig. 2. In Fig. 4, for each of the three dipole moments examined, these functions are plotted at two densities. Since the functions are close to zero around ulim = 1, the cosine function seems to be a good choice for describing the curves as follows: p = AcosB (π 2 ulim ) (9) Figure 3: The values of ulim as a function of the reduced density at three different dipole moments. 48(2) pp. 55–58 (2020) 58 NAGY Figure 4: The probability of chaining as a function of the energy criterion at three different dipole moments. Dashed lines refer to low densities (ρ∗ = 0.1), solid lines refer to high densities (ρ∗ = 0.8). The lines of intermediate densities are between these two lines. where A and B are constants and their magnitudes are given in the fifth and sixth columns of Table 1. Fits were made within the range ulim = 0.5−1. The absolute error in the values of p is not greater than 0.01285 in all the cases examined. 3. Conclusion The main result of this article is shown in Fig. 3. A well-explained energy-based chaining criterion resulting from magnetic susceptibility was defined. From the sim- ulated values of magnetic susceptibility, the probability of chaining was calculated. From this, the magnitude of the chaining criterion was derived using simulations as well. Therefore, a well-explained theory was developed to define the chaining energy criterion, which produced different results for different values of density and dipole moment in DHS systems. The criterion value of 70−75% commonly given in the literature is only approximately true at low densities (ρ∗ ≤ 0.3). At medium densities (0.3 < ρ∗ < 0.6), the values are generally higher, while at high densities (ρ∗ ≥ 0.6), they are much lower. Acknowledgement This research was supported by the European Union and co-financed by the European Social Fund under the project EFOP-3.6.2-16-2017-00002. REFERENCES [1] Ivanov, A. O.; Wang, Z.; Holm, C.: Applying the chain formation model to magnetic properties of aggregated ferrofluids, Phys. Rev. E, 2004, 69(3), 031206 DOI: 10.1103/PhysRevE.69.031206 [2] Wang, Z.; Holm, C.; Müller, H. W.: Molecular dynamics study on the equilibrium magnetization properties and structure of ferrofluids, Phys. Rev. E, 2002, 66(2), 021405 DOI: 10.1103/PhysRevE.66.021405 [3] Valiskó, M.; Varga, T.; Baczoni, A.; Boda, D.: The structure of strongly dipolar hard sphere flu- ids with extended dipoles by Monte Carlo sim- ulations, Mol. Phys., 2010, 108(1), 87–96 DOI: 10.1080/00268970903514553 [4] Tavares, J. M.; Weis, J. J.; Telo da Gama, M. M.: Strongly dipolar fluids at low densities compared to living polymers, Phys. Rev. E, 1999, 59(4), 4388– 4395 DOI: 10.1103/PhysRevE.59.4388 [5] Kantorovich, S.; Ivanov, A. O.; Rovigatti, L.; Tavares, J. M.; Sciortino, F.: Nonmonotonic mag- netic susceptibility of dipolar hard-spheres at low temperature and density, Phys. Rev. Lett., 2013, 110(14), 148306 DOI: 10.1103/PhysRevLett.110.148306 [6] Rovigatti, L.; Russo, J.; Sciortino, F.: Structural properties of the dipolar hard-sphere fluid at low temperatures and densities, Soft Matt., 2012, 8(23), 6310–6319 DOI: 10.1039/C2SM25192B [7] Nagy, S.: The frequency of the two lowest energies of interaction in dipolar hard sphere systems, Anal. Tech. Szeged., 2020, 14(2) in press [8] Nagy, S.: The initial magnetic susceptibility of dense aggregated dipolar fluids, Hung. J. Ind. Chem., 2018, 46(2), 47–54 DOI: 10.1515/hjic-2018-0018 [9] Langevin, P.: Sur la théorie du magnétisme, J. Phys. Theor. Appl., 1905, 4(1), 678–693 DOI: 10.1051/jphystap:019050040067800 [10] Pshenichnikov, A. F.; Mekhonoshin, V. V.: Equilib- rium magnetization and microstructure of the sys- tem of superparamagnetic interacting particles: nu- merical simulation, J. Magn. Magn. Mater., 2000, 213(3), 357–369 DOI: 10.1016/S0304-8853(99)00829-X [11] Allen, M. P.; Tildesley, D. J.: Computer simulation of liquids (Clarendon Press, Oxford) 1987, ISBN: 978-0-198-55645-9 [12] Theiss, M.; Gross, J.: Dipolar hard spheres: com- prehensive data from Monte Carlo simulations, J. Chem. Eng. Data, 2019, 64(2), 827–832 DOI: 10.1021/acs.jced.8b01169 Hungarian Journal of Industry and Chemistry https://doi.org/10.1103/PhysRevE.69.031206 https://doi.org/10.1103/PhysRevE.66.021405 https://doi.org/10.1080/00268970903514553 https://doi.org/10.1080/00268970903514553 https://doi.org/10.1103/PhysRevE.59.4388 https://doi.org/10.1103/PhysRevLett.110.148306 https://doi.org/10.1039/C2SM25192B https://doi.org/10.1515/hjic-2018-0018 https://doi.org/10.1051/jphystap:019050040067800 https://doi.org/10.1051/jphystap:019050040067800 https://doi.org/10.1016/S0304-8853(99)00829-X https://doi.org/10.1021/acs.jced.8b01169 https://doi.org/10.1021/acs.jced.8b01169 HUNGARIAN JOURNAL OF INDUSTRY AND CHEMISTRY Vol. 48(2) pp. 59–64 (2020) hjic.mk.uni-pannon.hu DOI: 10.33927/hjic-2020-29 TEMPERATURE AND ELECTRIC FIELD DEPENDENCE OF THE VISCOS- ITY OF ELECTRORHEOLOGICAL (ER) FLUIDS: WARMING UP OF AN ELECTRORHEOLOGICAL CLUTCH SÁNDOR MESTER1 AND ISTVÁN SZALAI *1 1Institute of Mechatronics Engineering and Research, University of Pannonia, Gasparich Márk u. 18/A, Zalaegerszeg, 8900, HUNGARY Cognition of the temperature-dependence of intelligent fluids, e.g. electrorheological (ER) and magnetorheological (MR) fluids, is critical for their application. In this paper, the dependence of the viscosity of ER fluids on temperature and electric field strength is examined. A new correlation equation is presented to describe the dependence of the viscosity on temperature by extending the Andrade equation. Considering the dependence of viscosity on the electric field strength and temperature, that equation is used to model the warming up of an ER clutch. Keywords: electrorheological fluid, viscosity, heat effect in ER fluids, ER clutch 1. Introduction Electrorheological (ER) fluids are suspensions made by dispersing micron-sized solid particles with a relative permittivity of εp into a carrier fluid with a smaller rel- ative permittivity of εf [1–3]. Normally, small concen- trations of stabilizers are also used to avoid sedimenta- tion. The structure and, therefore, the rheological prop- erties are altered by applying an external electric field. The dispersed particles, guided by the electric field, form chain-like structures [4]. These structures impair the mo- tion of the suspended particles resulting in an increase in the apparent viscosity. Using silicone oil as the car- rier fluid is common, although other oils such as trans- former oil have also been examined. The dispersed phase can consist of oxides, carbides, etc. ER and magnetorhe- ological (MR) fluids are used in various applications, e.g. couplings, shock dampers, [5, 6] ultra-smooth polishing materials, etc. [7]. Most of the applications require the viscosity to be precisely adjusted, however, the viscosity alters as the temperature changes. [8,9] This disadvantage greatly lim- its its industrial use, as a change can impair fine-tuned systems by creating stern operating conditions. Apart from the need to measure the temperature, the depen- dence of the parameters of the ER fluids on temperature ought to be considered as well. In addition to the ER ef- fect, an ER fluid is also affected by the thermal motion of particles. This motion works against the ER effect, as it disrupts the chain-like structure [10, 11]. *Correspondence: szalai@almos.uni-pannon.hu 2. Experimental The dependence of the viscosity of fluids on the temper- ature is characterized by a law proposed by Andrade: η = A e B T , (1) where η denotes the dynamic viscosity of the fluid, T stands for the temperature, whileA andB represent char- acteristic constants of the fluid. The constants are experi- mentally defined for each fluid. For ER fluids, this equa- tion is inadequate because their dependency on the elec- tric field strength is not addressed. In the following chap- ters, the dependence on the electric field in Eq. 1 is intro- duced. 2.1 Samples and viscosity measurements To study the thermal and field effects, an Anton Paar Physica MCR 301 rotational rheometer was used to mea- sure the viscosity. Different measuring equipment can be used for MR and ER fluids, moreover, the samples can also be thermostated. The usage of a cylindrical probe is shown schematically in Fig. 1. The length of the probe was L = 40.046 mm, the radius of the probe was ri = 13.33 mm and the inner radius of the chamber was re = 14.46 mm. For the measurements, a self-prepared ER fluid was used. The carrier fluid was silicone oil with a viscosity of 1000 mPas at 298 K. The dispersed phase was silica powder of 0.5 − 10 µm in diameter (as the manufacturer https://doi.org/10.33927/hjic-2020-29 mailto:szalai@almos.uni-pannon.hu 60 MESTER AND SZALAI Figure 1: Schematic representation of the probe claims that 80% of the particles have diameters of be- tween 1 and 5 µm). As a result, three different concentra- tions of particles were investigated, namely 10, 20 and 30 wt%. The samples were prepared through a multi-step pro- cedure: after stirring by hand, the fluid was placed in an ultrasonic bath for 15 minutes to mix further. To ensure an air bubble-free ER fluid, the sample was exposed to a vac- uum for 10 minutes before being placed in the rheometer. The samples were measured at six different tempera- tures at increments of 10 K ranging from 293 K to 343 K. Considering the 1.13 mm gap at the measuring probe, the used voltages resulted in the following electrical field strengths: 0, 0.442, 0.885, 1.327 and 1.769 MV/m. The samples were constantly stirred in the rheome- ter. A two-minute-long stirring cycle in the absence of an electric field came after setting the temperature. A ten- minute-long measuring cycle with an electric field was applied and another one-minute-long stirring cycle fol- lowed in the absence of an external electric field. The two mixing cycles at the beginning and end ensured that no residual particle arrangements from the previous mea- surements were present. The viscosity was measured per second. 3. Measurement Results and Analysis 3.1 Temperature dependence of the viscosity As an example, the measurement results of an 30 wt% ER fluid at a temperature of 293 K are shown in Fig. 2: After an initial rise (while the chain-like structure was form- ing), the viscosity became roughly constant. The anoma- lies in the figure were caused by external interferences. For the analysis, the results of a specific mixing ratio, electric field strength and temperature were averaged into Figure 2: Viscosity measurements under various electric field strengths, c = 30%, T = 295 K Figure 3: Experimental results and fitted curves (Eq. 2) of viscosity at c = 10% under various electric field strengths a single value. The expected tendencies are as follows: as the electric field strength increases, so does the viscosity, while a rise in temperature is inversely proportional to the viscosity. It was found that Eq. 1 is inappropriate for the ex- act representation of the dependence of ER viscosity data on temperature, therefore, in terms of the pre-exponential factor, a further temperature dependence was proposed: η = (A0 +A1T ) e B T , (2) where A0, A1 and B denote constants derived by fitting Eq. 2 to measurement data. Figs. 3–5 demonstrate the fitted curves of the different concentrations of ER fluid. The extended formula (Eq. 2) correlates well with the measurements: the worst coeffi- cient of determination for the fittings is R2 = 97.3 %. Eq. 2 can be used to describe the viscosity of electrorhe- ological fluids as a function of the temperature. Hungarian Journal of Industry and Chemistry TEMPERATURE AND ELECTRIC FIELD DEPENDENCE OF THE VISCOSITY OF ER FLUIDS 61 Figure 4: Experimental results and fitted curves (Eq. 2) of viscosity at c = 20% under various electric field strengths Figure 5: Experimental results and fitted curves (Eq. 2) of viscosity at c = 30% under various electric field strengths 3.2 Electric field strength dependence of vis- cosity The introduced formula does not concern how the vis- cosity depends on the electric field strength. In the case of many practical applications, the electric field changes, therefore, the temperature-dependent description of vis- cosity alone is insufficient. Eq. 2 can be extended by making the pre-exponential factor dependent on the electric field strength as well. This expansion is carried out via the square of the electric field, indicating that the direction of the field is reversible: η = [ A0 +A1E 2 + ( A2 +A3E 2 ) T ] e B T (3) where A0, A1, A2, A3 and B denote constants, while E stands for the electric field strength. Eq. 3 fitted to the measurement data can be seen in Figs. 6–8. Here the viscosity is represented as a func- tion of the electric field strength and temperature. The coefficients of determination for the fittings are R2 10% = 99.62%, R2 20% = 98.62% and R2 30% = 99.06%. Fitting parameters are summarized in Table 1. Figure 6: Viscosity measurement data (•) and the fitted surface (Eq. 3) at c = 10% Figure 7: Viscosity measurement data (•) and the fitted surface (Eq. 3) at c = 20% Figure 8: Viscosity measurement data (•) and the fitted surface (Eq. 3) at c = 30% 48(2) pp. 59–64 (2020) 62 MESTER AND SZALAI Table 1: Fitted coefficients of Eq. 3 at different ER fluid concentrations Concentration [m/m] 10% 20% 30% A0 -2.17623E-4 0.12252 3.82564 A1 2.47298E-16 2.2336E-14 5.10306E-13 A2 9.7734E-6 -2.32975E-4 -9.89E-3 A3 -6.84985E-19 -5.8753E-17 -1.2747E-15 B 1831.25188 1017.00608 3.82564 Figure 9: Cylindrical ER clutch model for the dissipation of viscous energy 4. Temperature rise in an ER clutch 4.1 ER clutch model A schematic diagram of a simple cylindrical electrorhe- ological clutch is shown in Fig. 9. Nakamura et al. [12] described the temperature rise of similar clutches: their model consisted of several cylinders with gaps between them with radii from r(i) to r(i+1), where i = 1, 2 . . . n denotes the number of gaps which are filled with ER fluid with a viscosity of η. According to the model, assuming the clutch is insulated, the dissipation of the viscous en- ergy per second in the gap i is dEi = ∂(τArω) ∂r dr, (4) where A denotes the surface of the cylinder, τ stands for the shear stress and ω represents the rotational speed. By utilizing the attributes of the cylinders and integrating the formula, the following equation can be derived: T (t+ ∆t) = 1 C { n∑ i=1 2πhη(T )ω2r(i) 3 s ∆t } + T (t) , (5) where T denotes the temperature, s stands for the gap, C represents the heat capacity of the fluid, ω refers to the relative rotational speed and h is the height of the cylin- der. In our system, only one gap is examined so Eq. 5 can be simplified to a differential equation: dT dt = 2πhω2r3 sC η(T ). (6) 4.2 Viscous energy dissipation in the clutch Using the model of the ER clutch (Eq. 6), the viscous en- ergy dissipation was calculated by inserting the formula of the viscosity (Eq.3): 1 [A0 +A1E2 + (A2 +A3E2)T ] e B T dT = 2πhω2r3 sC dt. (7) The integration of the left-hand side of the equation can be solved numerically using mathematical software. Ana- lytical integration requires the expansion of the exponen- tial term into a Taylor series:∫ e − B T [A0 +A1E2 + (A2 +A3E2)T ] dT = = ∫ 1 − B T + 1 2! B2 T 2 − 1 3! B3 T 3 + [A0 +A1 · E2 + (A2 +A3E2)T ] dT (8) Eq. 7 can be integrated following the expansion into a Taylor series resulting in the following expression: T2∫ T1 1 [A0 +A1E2 + (A2 +A3E2)T ] e B T dT = (−1) 0 B0 0! 1 a0 [ 1 c ln (1 + cT ) ]T2 T1 + + (−1) 1 B1 1! 1 a0 [ c1−1(−1) 1 ln 1 + cT T ]T2 T1 + + ∞∑ i=2 (−1) i Bi i! 1 a0 ci−1(−1) i ln 1 + cT T + i∑ j=2 (−1) i−j+1 ci−j (j − 1)T j−1 T2 T1 , (9) Hungarian Journal of Industry and Chemistry TEMPERATURE AND ELECTRIC FIELD DEPENDENCE OF THE VISCOSITY OF ER FLUIDS 63 Figure 10: Heating of an ER clutch model as a function of the number of addends, c = 30%, E = 1.327 MV/m and T = 295 K Figure 11: Heating of an ER clutch model over time, c = 10% where a0 = A0 +A1E 2 and c = A2 +A3E 2 a0 . The expression in Eq. 9 can be used to calculate the time needed for the clutch to be heated to a given temper- ature. The closed formula, a double infinite sum, yields varying results depending on how many terms are used. In the present calculation, the following parameters are used: h = 0.04 m, r = 0.0133 m, s = 0.0113 m, ω = 4.057 rad/s and C = 0.05 J/K. The temperature as a function of the number of ad- dends N is shown in Fig. 10. After the 5th addend, the numerical and analytical solutions are almost identical. To calculate the amount of heating, the physical proper- ties of the Anton Paar probe and the calculated parame- ters (A0, A1, etc.) of the examined ER fluids were used. Figs. 11–13 show the heating results using three dif- ferent concentrations of ER fluids. As can be observed, as the electric field strength and concentration increase, the temperature also rises faster due to the internal friction. Figure 12: Heating of an ER clutch model over time, c = 20% Figure 13: Heating of an ER clutch model over time, c = 30% 5. Conclusion In our paper, a new correlation equation was proposed to describe the dependence of the viscosity of ER flu- ids on temperature and electric field strength. The pro- posed equation describes the measurement results with a correspondingly small deviation. The temperature rise of the model system examined by using the aforementioned equations can also form the basis for the description of real systems. The applied model can be further refined, e.g. by taking into account the dependence of heat capac- ities on temperature. Acknowledgement This research was supported by the European Union and co-financed by the European Social Fund under project EFOP-3.6.2-16-2017-00002. REFERENCES [1] Shin, K.; Kim, D.; Cho, J.-C.; Lim, H. S.; Kim, J. W.; Suh, K. D.: Monodisperse conducting colloidal dipoles with symmetric dimer structure for enhanc- ing electrorheology properties, J. Coll. Interf. Sci., 2012, 374(1), 18–24 DOI: 10.1016/j.jcis.2012.01.055 48(2) pp. 59–64 (2020) https://doi.org/10.1016/j.jcis.2012.01.055 64 MESTER AND SZALAI [2] Wu, J.; Xu, G.; Cheng, Y.; Liu, F.; Guo, J.; Cui, P.: The influence of high dielectric constant core on the activity of core–shell structure electrorheologi- cal fluid, J. Coll. Interf. Sci., 2012, 378(1), 36–43 DOI: 10.1016/j.jcis.2012.04.044 [3] Rankin, P. J.; Ginder, J. M.; Klingenberg, D. J.: Electro- and magneto-rheology, Curr. Op. Coll. In- terf. Sci., 1998, 3(4), 373–381 DOI: 10.1016/S1359- 0294(98)80052-6 [4] Sanchis, A.; Sancho, M.; Martínez, G.; Sebastián, J.; Muñoz, S.: Interparticle forces in electrorheo- logical fluids: effects of polydispersity and shape, Colloid Surf. A, 2004, 249(1-3), 119–122 DOI: 10.1016/j.colsurfa.2004.08.061 [5] Olabi, A. G; Grunwald, A.: Design and application of magneto-rheological fluid, Mat. Design, 2007, 28(10), 2658–2664 DOI: 10.1016/j.matdes.2006.10.009 [6] Bucchi, F.; Forte, P.; Frendo, F.; Musolino, A.; Rizzo, R.: A fail-safe magnetorheological clutch excited by permanent magnets for the disengagement of automotive auxiliaries, J. Int. Mat. Sys. Struc., 2014, 25(16), 2102–2114 DOI: 10.1177/1045389X13517313 [7] Peng, W.; Li, S.; Guan, C.; Li, Y.; Hu, X.: Ultra- precision optical surface fabricated by hydrody- namic effect polishing combined with magnetorhe- ological finishing, Optik, 2018, 156, 374–383 DOI: 10.1016/j.ijleo.2017.11.055 [8] Ayani, M.; Hosseini, L.: The Effect of Temperature and Electric Field on the Behavior of Electrorhe- ological Fluids, in 4th Annual (International) Me- chanical Engineering Conference (Isfahan Univer- sity of Technology, Isfahan, Iran) [9] Mokeev, A. A.; Gubarev, S. A.; Korobko, E. V.; Bedik, N. A.: Microconvection heat transfer in electrorheological fluids in rotating electric field, J. Phys.: Conf. Ser., 2013, 412, 012008 DOI: 10.1088/1742-6596/412/1/012008 [10] Spaggiari, A.: Properties and applications of Mag- netorheological fluids, Frattura ed Integrità Strut- turale, 2012, 7(23), 48–61 DOI: 10.3221/IGF-ESIS.23.06 [11] Wang, R.; Wang, Y.C.; Feng, C.Q.; Zhou, F.: Ex- perimental Research on the Heat Transfer Char- acteristics of Electrorheological Fluid Shock Ab- sorber, in Advances in Industrial and Civil Engi- neering, Advanced Materials Research, 2012, 594– 597 (Trans Tech Publications Ltd), 2836–2839 DOI: 10.4028/www.scientific.net/AMR.594-597.2836 [12] Nakamura, T.; Saga, N.; Nakazawa, M.: Thermal Effects of a Homogeneous ER Fluid Device, J. Int. Mat. Sys. Struct., 2003, 14(2), 87–91 DOI: 10.1177/1045389X03014002003 Hungarian Journal of Industry and Chemistry https://doi.org/10.1016/j.jcis.2012.04.044 https://doi.org/10.1016/S1359-0294(98)80052-6 https://doi.org/10.1016/S1359-0294(98)80052-6 https://doi.org/10.1016/j.colsurfa.2004.08.061 https://doi.org/10.1016/j.colsurfa.2004.08.061 https://doi.org/10.1016/j.matdes.2006.10.009 https://doi.org/10.1177/1045389X13517313 https://doi.org/10.1177/1045389X13517313 https://doi.org/10.1016/j.ijleo.2017.11.055 https://doi.org/10.1016/j.ijleo.2017.11.055 https://doi.org/10.1088/1742-6596/412/1/012008 https://doi.org/10.1088/1742-6596/412/1/012008 https://doi.org/10.3221/IGF-ESIS.23.06 https://doi.org/10.4028/www.scientific.net/AMR.594-597.2836 https://doi.org/10.4028/www.scientific.net/AMR.594-597.2836 https://doi.org/10.1177/1045389X03014002003 https://doi.org/10.1177/1045389X03014002003 Introduction Entomopathogenic nematodes and bacteria Antimicrobial compounds Conclusion Introduction Membrane-aerated bioreactors for the treatment of wastewater Fermentation of itaconic acid Enzymatic removal of glucose Conclusion Introduction Experimental Samples and Measurements Analysis Results and Analysis Experiments Effect of water content Discussion Conclusion Introduction Problem Statement System Configuration Sliding Mode Control Follow-up Control Equations of the System Filter and Load The states of the state-space equation Error Trajectory Control Law Simple Relay Double Relay Double Relay with dead zone and hysteresis Stability Analysis Simulation MATLAB-Simulink model Model validation Analysis of the simulation results Non-linear load Switching on the load Conclusions Introduction Results: Methodology Reference database Laws and regulations pertaining to the investigation Determination of the environmental parameters for the environmental elements studied Weight of environmental parameters Analysis of Parameters Environmental parameter quality indicator (Pi) Immission analysis Analysis of the Environmental element level Load of environmental elements (ELj) Qualification of environmental elements Theoretical Number of Environmental Parameters (ntj) Practical Number of Environmental Parameters (npj) Element Quality Index (IEj) Element Quality Factor (FEj) Environmental Element Informativity Rate (FIj) Environmental analysis Element Quality Indicator (Ej) Total Environmental Load (TL) Qualification of the total environment Environment Informativity Rate (FEnv.I) Informative Environment Qualifying Index (IIEQ) A case study Conclusion Introduction Materials and methods Cultivation conditions Analysis of biomass Analysis of glucose consumption Analysis of biosurfactants Surface tension measurement Emulsifying activity High-performance liquid chromatography (HPLC) Isolation of the biosurfactant Calculation of fermentation parameters Results and Discussion Conclusion Introduction Materials and Methods Beneficiation and calcination of samples Batch Adsorption Results and Discussion Characterization of the adsorbent Chemical composition Fourier-transform infrared spectroscopy Adsorption studies Effect of adsorbent mass Effect of contact time on the uptake of nickel ions Equilibrium isotherms Adsorption kinetics Adsorption thermodynamics Conclusions Introduction Membranes and biofouling in MFCs – The potential role of oxygen mass transfer Conclusions Introduction Simulations and results Conclusion Introduction Experimental Samples and viscosity measurements Measurement Results and Analysis Temperature dependence of the viscosity Electric field strength dependence of viscosity Temperature rise in an ER clutch ER clutch model Viscous energy dissipation in the clutch Conclusion