CETvol87 DOI: 10.3303/CET2187069 Paper Received: 18 September 2020; Revised: 22 February 2021; Accepted: 3 May 2021 Please cite this article as: Tamborrino A., Catalano F., Berardi A., Bianchi B., 2021, New Modelling Approach for the Energy and Steam Consumption Evaluation in a Fresh Pasta Industry, Chemical Engineering Transactions, 87, 409-414 DOI:10.3303/CET2187069 CHEMICAL ENGINEERING TRANSACTIONS VOL. 87, 2021 A publication of The Italian Association of Chemical Engineering Online at www.cetjournal.it Guest Editors: Laura Piazza, Mauro Moresi, Francesco Donsì Copyright © 2021, AIDIC Servizi S.r.l. ISBN 978-88-95608-85-3; ISSN 2283-9216 New Modelling Approach for the Energy and Steam Consumption Evaluation in a Fresh Pasta Industry Antonia Tamborrinoa, Filippo Catalanob,*, Antonio Berardic, Biagio Bianchia a Department of Agricultural and Environmental Science, University of Bari Aldo Moro, Via Amendola, 70126 Bari, Italy b Department of Biosciences and Territory, University of Molise, C.da Fonte Lappone – Pesche (IS), Italy c Dipartimento di Science Agrarie, Alimenti, Risorse Naturali e Ingegneria (DAFNE), Università degli Studi di Foggia, via Napoli, 25 – 71122 Foggia – Italy f.catalano@studenti.unimol.it The agri-food industry has a fundamental role in the Italian and European economy and is characterized by the need to reduce energy costs and emissions. Therefore, it is essential for food companies to give due consideration to the energy efficiency of the processes, to reduce production costs, without sacrificing the quality of primary production and maintaining adequate levels of competitiveness on the market. In this study, a theoretical and experimental mass and energy balance of the production process of fresh pasta was made, also considering the energy contributions of a cogeneration plant recently built in the company subject of the experimental study. The final aim was to determine scientific values of specific energy consumption for this type of production by mean a new modelling approach. The mass and energy balances were carried out for the production line of fresh semolina pasta, as well as for the cogeneration plant; monitoring the flows of raw materials and steam that characterize the production process. The results of this study can be generalized to all production processes of the same type and, in the specific case, constitute a decisive logical step for the definition of the energy recovery solutions to be adopted in the company studied, in relation to their economic- production needs. 1. Introduction All food processes characterized by a heat exchange with the product being processed have the need to become a competitive system in terms of quality, functionality, reduce emissions and energy savings (Bianchi et al., 2013; Ayr et al., 2015; Bianchi et al., 2015). Energy and steam consumption are essential subjects to evaluate sustainability of food processessing, then energy and water saving allows any factory to be low impact and competitive taking in any case the quality of the product (Galitsky et al., 2003; Leone et al., 2015a; Perone et al., 2017). Therefore, energy and water can be considered in all respects as well as all other raw materials used to obtain a high-quality product (Leone et al., 2015b; Tamborrino et al., 2017). To this aim it is necessary to carry out a mass and energy balance of a food process (Sturm et al., 2012) In particular, modelling energy and mass flows is a not new technique applied to pasta factor (Moraitis et al., 1997; Panno et al., 2007) but never a complete dynamical approach was used. Therefore, in the present paper the analysis outlined above was carried out using a new dynamical modelling approach (Catalano et al., 2020; Tamborrino et al., 2019a; Difonzo et al., 2021) to study the mass and energy balance of a fresh semolina pasta production process equipped with a cogeneration plant. 2. Materials and Methods The factory needs high amount of heat which is produced as pressured steam, and electricity. The steam is mainly used in the pasta production process and generated by a afterburner located downstream of a cogeneration plant (CHP) and, when necessary, by a traditional boiler. On the other hand, the electricity is partly purchased and partly produced by the cogenerator. The cogenerator is powered by a mixture of air and 409 methane and consists of 3 microturbines. The afterburner is powered by a mixture of high temperature burnt gases from the cogenerator plus methane. To achive the results the following quantities were measured during each production phase: temperature; relative humidity; thermal flow; radiation; atmospheric pressure; air speed; gas concentration; water level. The used analytical model was taken from a recent paper (Catalano et al., 2020) where a new dynamical procedure was proposed, using deep and detailed mass and energy analysis of the entire production process of a frozen food production factory. In particular, the model is based on transient mass and energy balance equations of each component of the different equipments in the plant: these equations together determine a system of general linear and nonlinear differential-algebraic equations solved with a semi-implicit stable scheme. The whole scheme, flow charts and equations are not shown for sake of brevity. The method, based on a dynamic simulation, has been adapted to several cases of food industry (Tamborrino et al., 2019b); in this case, considering the specific energetic characteristics of the studied pasta factory, the method allowed to evaluate the energy efficiency of the fresh semolina pasta processing plant involved in the experimental test. Figure 1. Total mass balance of the fresh semolina pasta production line 3. Results Here are shown the results of the energy and mass balance carried out for fresh semolina pasta where 10,000 kg/day are produced which is the most representative production. The machines considered are shown in Figure 1). The amount of mass processed by the mixer is 755 kg/h, with a loss coefficient of 5%. In the pasteurizer, 700 kg/h of wet product are processed with a 2% mixture loss using 343 kg/h of steam. This energy exchange leads to obtain a pasteurized product which is then treated in the dryer for the unpackaged product, using appropriately treated and dehumidified air as an exchange fluid. In this machine 617 kg/h of dehumidified product are obtained to be used for subsequent cooling and packaging and 103 kg/h of water that is transferred from the product to the surrounding air as steam. The afore mentioned values, together with those determined with the same methodology for all the other lines, have been included in the equations relating to the energy calculations carried out for the pasteurizers of each line, in which the maximum consumption of thermal energy occurs. As said before, the equations, describing the mass and energy flows for each equipment of the analyzed process, can be found in Catalano et al. (2020) therefore, for the sake of brevity, are not shown here. The same method, shows also that the heat inputs in all the equipments are much lower than in pasteurizers (Figure 2). 410 Figure 2. Monthly distribution of thermal consumption for steam production divided into; thermal energy for steam used in pasteurizers and steam used in the remaining thermal users of the production lines The monthly amount of steam used for the fresh pasta production process and the relative specific consumption expressed in kgvap/100kgpasta was obtained considering that the process steam is used in a closed circuit and that the losses can be considered negligible. The highest steam value is required by all lines in May, while relatively small amount of steam is used in June. It is not possible to identify an immediate correlation between the consumption of steam and the corresponding production; for example, in the months of February and September there are production values close to the maximum ones, which correspond to steam consumption similar and comparable to the minimum, on the contrary, in the May there is a similar production but a much higher steam consumption. The lowest specific consumptions are found, first of all, when production is close to the maximum values and, secondly, when the use of steam tends to minimum values. When a variation in production does not correspond to a correct adjustment of the steam used, the specific consumption tends to assume the maximum values. From the above results, for this type of systems, it is possible to define a value of 100.0 kgvap/100 kgpaste for the maximum sizing adjustment, considering however that it would be useful to study a specific system for monitoring and measuring the steam flow rate, in in relation to the flow rate of the product being processed, which could allow a saving in steam consumption of up to 24%. From the analysis of the results of the overall thermal energy balance, an average value for the maximum sizing can be defined equal to 134 kWh / 100 kgpasta; this value is obtained by considering the monthly consumption, the trend of which is fairly consistent with the consumption of steam, confirming that in the company studied this is the main vector of thermal energy. The greater thermal energy commitment is attributed to the pasteurization phase: approximately 67% of the total thermal energy required by the production process (Figure 3); this occurs because the heat exchange of this process is characterized by a higher ∆T compared to the other production phases. The remaining 33% of total thermal energy is represented by low temperature thermal energy used in pre-dryers and final pasteurizers. Figure 3. Total monthly electricity consumed divided into: produced by the CHP and purchased from the grid 411 On the other hand, the experimental data relating to thermal consumption for the sole production of process steam do not coincide with the total thermal consumption of the company obtained from the analysis of the methane bills consumed, also considering the efficiency of combustion (Figure 4). Figure 4. Total monthly thermal energy produced consumed by the plant and divided into thermal energy produced in the boiler and thermal energy produced by the CHP + Post Combustor (values expressed in thermal kWh) In fact, the methane consumed is higher than that necessary to produce steam useful for the process which represents the only thermal user of the company. Therefore, a reduction in the overall consumption of thermal energy and in production costs could be obtained, through a study of optimization of the production of steam as well as of its use in relation to the actual thermal demand of the production, as well as to any waste and possible recovery of the thermal waste. As regards the analysis of monthly electricity consumption (Figure 3), in the months of August and February there is a correspondence between the consumption of thermal energy (Figure 4) and electricity. Furthermore, a fairly precise correlation can be defined between the consumption of electricity and production, contrary to what appears for thermal energy; for example, in the months of March, May, August and September, production values are found close to the maximum ones, corresponding to similar and comparable electricity consumption with the maximum. For the maximum sizing, a precautionary value of 37 kWh/100kgpasta can be considered. In Table 1 the real monthly production of electricity and the gas needed by the CHP are shown. These values comes from measures in the plant designed by mean of the classical and standard method that uses stationary conditions for plant management. In table 2 the new method was applied to determine the values in case of dynamicalmanagement of the plant. Comparing the values of the two tables it can be highlighted that there is not only an increase of the energy production but also a decrease in gas consumption showing a net increase in the CHP global efficiency. Table 1. Total monthly electricity produced by CHP, and gas consumed in the real case with classical method analysis Month Electricity produced from CHP Gas consumed from CHP Gas consumed from extra burner of CHP Electricity consumed from grid Gas consumed from grid kWh Smc Smc kWh Sm3 jan 258.373 82.547 84.460 141.293 167.007 feb 238.287 75.027 74.760 107.193 149.787 mar 330.887 105.335 97.412 135.508 202.747 apr 252.543 80.887 78.924 121.482 159.811 may 314.299 100.983 93.084 166.671 194.067 jun 230.971 75.700 69.937 175.431 145.637 jul 260.942 85.942 75.766 198.090 161.708 aug 290.113 96.214 82.525 220.235 178.739 sep 258.955 85.973 74.501 220.406 160.474 412 Table 2. Total monthly electricity produced by CHP, and gas consumed in the simulation with the new method analysis The cogenerator can cover 60% of the total electricity requirement (Figure 3), 84% of the thermal requirement due to the use of steam and 60% of the total thermal requirement and its electrical power has been set to produce, together with the afterburner, the amount of thermal energy required by the production process. The aforementioned rates are integrated, respectively, with electricity purchased from the grid and with thermal energy produced in the boiler. The electricity purchased from the grid has higher values in the months of June, July, August, and September. Therefore, the results show a greater consumption of electricity from the grid mainly in the summer months. Therefore, it can be said that, overall, the cogeneration plant is efficient, however a more in-depth energy analysis would be required: not referring to individual lines but to individual machines. In this way it would be possible to accurately evaluate the most suitable efficiency solutions for the specific company situation; for example, it may be sufficient to integrate the electricity needs with a photovoltaic system and reduce heat consumption with an adequate study of recovery of the related waste, rather than inserting an additional microturbine in the cogeneration plant. 4. Conclusions The aim of this paper is to propose a new method for evaluating the mass and energy balance in a fresh pasta processing factory by using a dynamic simulation analysis. The proposed procedure first of all requires that the designer has to acquire real data about the use of mass and energy and use these data as input to the simulation model. The proposed technique can be used effectively when there is a variability of the electrical and / or heat load. The results prove that the dynamic analysis carried out in this paper can lead to high profitability in terms of both energetic benefits and environmental impact. For a future perspective, the method could be further enhanced to optimize the monitoring of the CHP plant during operations. With the cogeneration plant built, the company has an important annual saving on bills compared with the investment (data not shown as not in the aims of the paper.Based on these results, the company is evaluating how to improve the performance of the cogeneration plant, to increase the self-production of electrical and thermal energy, therefore to reduce the consumption of the afterburner and the electricity purchased from the grid, with the aim of further reducing consumption, costs and the environmental impact of production. The results of this study can be extended to industries that carry out productions similar to the one studied, both in qualitative and quantitative terms, in addition some considerations can be made. The analysis of the energy aspects of a production offers the food technologist the possibility to carry out the general design of the production plants and layouts, considering: • aspects related to the quality of production; • the possibilities of reducing production costs; • the possibilities of enhancing the environmental commitment of production with an appropriate marketing policy. This study also shows that the first logical step to undertake an energy saving and recovery path is to carry out an in-depth energy analysis of the company's machines and systems; the purpose is to implement interventions aimed at reducing consumption, and then move on to a correct sizing / choice of the energy self- production system, possibly resorting to a mix of technical solutions that could go beyond individual solutions, such as cogeneration , proposed by the supplier companies and would lead to greater economic savings. Month Electricity produced from CHP Gas consumed from CHP Gas consumed from extra burner of CHP Electricity consumed from grid Gas consumed from grid kWh Smc Smc kWh Sm3 jan 297.129 89.151 91.217 102.537 180.368 feb 262.115 78.778 78.498 83.364 157.276 mar 370.593 112.708 104.231 95.802 216.939 apr 275.271 84.122 82.081 98.753 166.203 may 370.873 113.101 104.254 110.097 217.355 jun 247.138 77.971 72.035 159.263 150.006 jul 273.989 87.661 77.281 185.043 164.942 aug 333.630 105.835 90.778 176.718 196.613 sep 287.440 92.851 80.461 191.921 173.312 413 Acknowledgments The authors have contributed to the same extent to the present study. References Ayr U., Tamborrino A., Catalano P., Bianchi B., Leone, A., 2015, 3D computational fluid dynamics simulation and experimental validation for prediction of heat transfer in a new malaxer machine Journal of Food Engineering, vol. 154, p. 30-38, ISSN: 0260-8774, DOI: 10.1016/j.jfoodeng.2014.12.022. Bianchi B., Cavone G., Cice G., Tamborrino A., Amodio M., Capotorto I., Catalano P., 2015, CO2 Employment as Refrigerant Fluid with a Low Environmental Impact. Experimental Tests on Arugula and Design Criteria for a Test Bench, SUSTAINABILITY, vol. 7, p. 3734-3752, ISSN: 2071-1050, DOI: 10.3390/su7043734. Bianchi B., Tamborrino A., Santoro F., 2013, Assessment of the energy and separation efficiency of the decanter centrifuge with regulation capability of oil water ring in the industrial process line using a continuous method, Journal of Agricultural Engineering, 44, pp. 278-282, DOI: 10.4081/jae.2013.(s1):e56. Bianchi B., Tamborrino A., Giametta F., Squeo G., Difonzo G., Catalano P., 2020, Modified rotating reel for malaxer machines: assessment of rheological characteristics, energy consumption, temperature profile, and virgin olive oil quality, Foods 2020, 9, 813, DOI:10.3390/foods9060813. Catalano F., Perone C., Iannacci V., Leone A., Tamborrino A., Bianchi B., 2020, Energetic analysis and optimal design of a CHP plant in a frozen food processing factory through a dynamical simulation model, Energy Conversion and Management, 225 (2020), 113444, DOI:org/10.1016/j.enconman.2020.113444. Difonzo G., Fortunato S., Tamborrino A., Squeo G., Bianchi B., Caponio F., 2021, Development of a modified malaxer reel: Influence on mechanical characteristic and virgin olive oil quality and composition, LWT, Volume 135, January 2021, 110290, DOI:org/10.1016/j.lwt.2020.110290. Galitsky C., Martin N., Worrell E., Lehman B., 2003, Energy Efficiency Improvement and Cost Saving Opportunities for Pasta factory: An Energy Star Guide for Energy and Plant Managers, Lawrence Berkeley National Laboratory, Berkeley. Leone A., Romaniello R., Zagaria R., Tamborrino A., 2015a, Mathematical modelling of the performance parameters of a new decanter centrifuge generation, Journal of Food Engineering, 166, pp. 10-20, DOI: 10.1016/j.jfoodeng.2015.05.011. Leone A., Romaniello R., Peri G., Tamborrino A., 2015b, Development of a new model of olives de-stoner machine: Evaluation of electric consumption and kernel characterization, Biomass and Bioenergy, 81, pp. 108-116, DOI: 10.1016/j.biombioe.2015.06.016. Moraitis C.S., Akritidis C.B., 1997, Energy saving in industrial drying plants by partial recovery of the latent heat of the exhaust air, Drying Technology, 15, 1931-1940. Panno D., Messineo A., Dispenza A., 2007, Cogeneration plant in a pasta factory: Energy saving and environmental benefit, Energy 32 (5), 746-754. Perone C., Catalano F., Tamborrino A., Giametta F., Bianchi B., Ayr U., 2017, Study and Analysis of a Cogeneration System with Microturbines in a Food Farming of Dry Pasta, Chemical Engineering Transactions, vol. 58, 2017, 499-504, DOI: 10.3303/CET1758084. Sturm B., Hugenschmidt S., Joyce S., Hofacker W., Roskilly A.P., 2012, Opportunities and barriers for efficient energy use in a industrial pasta factory. Elsevier. Tamborrino A., Squeo G., Leone A., Paradiso V., Romaniello R., Summo C., Pasqualone A., Bianchi B., Caponio F., 2017, Industrial trials on coadjutants in olive oil extraction process: effect on rheological properties, energy consumption, oil yield and olive oil characteristics, Journal of Food Engineering, DOI:10.1016/j.jfoodeng.2017.02.019. Tamborrino A., Romaniello R., Caponio F., Squeo G., Leone A., 2019a, Combined industrial olive oil extraction plant using ultrasounds, microwave, and heat exchange: Impact on olive oil quality and yield, Journal of Food Engineering, 245, pp. 124-130. DOI: 10.1016/j.jfoodeng.2018.10.019. Tamborrino A., Perone C., Catalano F., Squeo G., Caponio F., Bianchi B., 2019b, Modelling energy consumption and energy-saving in high-quality olive oil decanter centrifuge: Numerical study and experimental validation. Energies 2019, 12, 2592, DOI:10.3390/en12132592. 414