DOI: 10.3303/CET2398019 Paper Received: 16 November 2022; Revised: 30 January 2023; Accepted: 21 April 2023 Please cite this article as: Brughitta E., Atzori F., Gamboni E., Foddi S., Casula M., Fais G., Manca A., Pantaleo A., Cao G., Concas A., 2023, Cultivation of Cyanobacteria and Microalgae using Simulated in-situ Available Resources for the Production of useful Bio-compounds on Mars: Modelling of Experiments, Chemical Engineering Transactions, 98, 111-116 DOI:10.3303/CET2398019 CHEMICAL ENGINEERING TRANSACTIONS VOL. 98, 2023 A publication of The Italian Association of Chemical Engineering Online at www.cetjournal.it Guest Editors: Sauro Pierucci, Carlo Pirola Copyright © 2023, AIDIC Servizi S.r.l. ISBN 978-88-95608-97-6; ISSN 2283-9216 Cultivation of Cyanobacteria and Microalgae using Simulated in-situ Available Resources for the Production of useful Bio- compounds on Mars: Modelling of Experiments Eleonora Brughittaa, Federico Atzorib, Emanuela Gambonia, Stefano Foddia, Mattia Casulaa, Giacomo Faisa, Alessia Mancac, Antonella Pantaleoc, Giacomo Caoa,b,d, Alessandro Concasa,d,* a Interdepartmental Centre of Environmental Science and Engineering (CINSA), University of Cagliari, Via San Giorgio 12, 09124 Cagliari, Italy b Center for Advanced Studies, Research and Development in Sardinia (CRS4), Loc. Piscina Manna, Building 1, 09050 Pula (CA), Italy. c Department of Biomedical Science, University of Sassari, Viale San Pietro, 07100 Sassari, Italy. d Department of Mechanical, Chemical and Materials Engineering, University of Cagliari, Via Marengo 2, 09123 Cagliari, Italy. To increase the likelihood of successful long-term manned missions to Mars, it is necessary to explore the potential for utilizing in-situ resources to cultivate microalgae for food and supplement production. This study examines the feasibility of growing Spirulina platensis in a medium consisting of high volume percentages of Martian Medium, which is produced using resources available on Mars, such as regolith, atmospheric CO2, and astronauts' urine. An experimental activity is performed to simulate the microalgae growth process on Mars, demonstrating good productivity. A mathematical model is developed to describe biomass growth dynamics as a function of pH, light intensity, microgravity, and nutrient concentration. The model is validated and then utilized to identify optimal operating conditions for maximizing biomass productivity on Mars and meeting finding the nutritional and supplement needs of a six-member crew. 1. Introduction Earth resources depletion and the climate crisis require the identification of new sustainable strategies to produce the resources humanity needs. A long-term solution to this problem could be the exploitation of extra- terrestrial resources. According to ISRU (In Situ Resource Utilization) paradigm, Mars is the best candidate to host humanity, due to the availability of resources such as atmospheric CO2, water and regolith which might be suitably processed to produce useful crucial consumables such as oxygen, water and food (Fais et al., 2022a). Nevertheless, most ISRU technologies so far proposed rely on physic-chemical methods to produce oxygen and propellants but cannot contribute to food production (De Man et al., 2019). Therefore, further research activity is needed to investigate the potential use of ISRU technologies to obtain dry food and, then, sustain manned missions on Mars. In this regard, the use of microalgae to obtain photosynthetic oxygen and edible biomass on Mars is gaining increasing interest (Billi et al., 2021). In this view, a recent patent describes the possibility to cultivate microalgae in the framework of the process to be implemented on Mars that exploits local natural resources (Fais et al., 2022a). This technology is based on the coupling of a chemical-physical section with a biological one that working in synergy can produce water, oxygen, fuel for extra vehicular activity (EVA), building material, and food (Figure 1a). In-situ available resources and metabolic wastes are used along with small amounts of materials brought from Earth are used. Such features of the process determine low payloads that in turn result in a increased techno-economic feasibility of the mission. The main innovative aspect of this technology is the capability to produce food from microalgae in the biological section according to the simplified process schematized in Figure 1b. As it can be seen here photobioreactors are fed with the Martian Medium (MM) produced by mixing regolith leachate and astronauts’ urine from the environmental control life system 111 (ECLSS) and the fertilizers (ammonia nitrate) produced in chemical physical section. The cultivated microalgae are then used to meet a percentage of the crew food needs as well as oxygen to regenerate cabin air in the ECLSS. Figure 1. Scheme of the ISRU process to produce useful materials on Mars (a) and focus on the production of microalgae in the biological section (b). Part (a) of the Figure is adapted from Concas et al. (2012). In the present work, the results of an experimental and modelling activity aimed to evaluate the real feasibility of the process proposed by Fais et al., 2022., are reported. In particular, the experimental activity consisted in growing Spirulina platensis under operating conditions that simulate the ones theoretically occurring in the process implemented on Mars, i.e. pure CO2 fed to the photobioreactors (PBR), microgravity and MM as growth medium. A mathematical model was then developed to simulate the obtained results and infer useful information about the application of such process on Mars. 2. Materials and methods 2.1 Strain maintenance and preparation of the Martian Medium The cyanobacterium Spirulina platensis was chosen as test organism due to its high nutritional properties that make it a promising candidate to feed the astronauts during long term missions (Fais et al., 2022b). Unialgal culture of cyanobacterium S. platensis was obtained from TOLO Green Srl (Arborea, Italy). The strain was maintained under axenic conditions at the laboratory of the Center for Engineering and Environmental Sciences (CINSA) in Cagliari, Italy. The cultures were kept in 250 mL flask, containing 150 mL Zarrouk-medium (ZM). Synthetic Martian Medium (MM) was prepared by mixing a leachate of Martian regolith simulant (JSC MARS-1) and synthetic human urine (MP-AU) to simulate astronauts’ urine. Briefly, the regolith leachate was prepared within a 250 mL flask with a cap by contacting 15 g of regolith simulant with 150 mL of ultrapure water. The solid liquid mixture was stirred at 200 rpm with an orbital shaker (Stuart SSM1, Bio sigma) for 24 hours at 25°C, then the solution was filtered by gravity by means of filter paper. MP-AU was produced according to available protocols and diluted with ultrapure water at a ratio of 1:10. Finally, the leachate and diluted urine were mixed (1:1 v/v) to produce MM. A more detailed description of the procedure to produce MMand the final growth media is reported elsewhere (Fais et al., 2022a) 2.2 Growth experiments in microgravity and pure CO2 atmosphere conditions Two main growth experiments were carried out. EXP-I: MM40 (MM 40%v/v, ZM 60%v/v), 100% CO2 atmosphere and microgravity (g); EXP-II: ZM, 100% CO2 and g. Both experiments were meant to simulate the operating conditions taking place on Mars within the dome hosting the process patented by Cao et al (2021). However EXP-I Involved 40% of medium produced in-situ while EXP-II involved only Zarrouk’s medium whose components would be brought from Earth. The experiments were carried out in a clinostat (3D Random Positioning Machine, Fokker Space, Netherlands) to simulate g conditions; to simulate Martian atmosphere a jar (2.5 L) containing pure CO2 was mounted, allowing to carry 8 culture flasks. The batch culture experiments were carried out into transparent vented cap flasks filled up to 80 mL. The experiment was set in triplicates with an illumination of I0 =150 μmol m-2 s-1 and the photoperiod fixed at 12:12 hours light and dark periods. The growth was monitored through absorbance spectrophotometric measurements (Genesys 20 spectrophotometer, Thermo Scientific, Walthmanm, USA) of the chlorophyll-a optic<.al density (OD) of the Mars atmosphere Fuel Mars regolith Sun irradiation O2 H2O Ammonia nitrate NH4NO3 Vegetables H2O Dehydrated regolith Main dome EDIBLE MICROALGAL BIOMASS Mars Mars atmosphere regolith CHEMO-PHYSICAL SECTION BIOLOGICAL SECTION Energy production ECLSS STORAGE BUILDINGS ASTRONAUT S GREEN - HOUSES Fuelling of rovers and machines (a) (b) 112 culture at 650 nm wavelength. The biomass concentration X (g L-1), calculated through OD measurements and a calibration curve, obtained by gravimetry. Also in this case, a detailed description of the experimental procedure is reported elsewhere (Concas et al., 2023). 3. Mathematical model The developed model aims to describe microalgae growth by considering the CO2 mass transfer from gas to liquid phase and chemical composition of the medium. According to the literature (Concas et al., 2021b), the mass balance for biomass X, Eq. (1), is written by considering the death term and the growth term that depends on the limiting factors: average light intensity 𝐼𝑎𝑣, medium pH and nutrient’s concentration, [𝐶𝑗] with j representing total inorganic carbon (TIC), nitrogen (TIN), phosphorous (TIP). ( ) 0, 1, 1 0 2 1 , 1 1 2   with 0 [ ] 1 i n j iav max d j av k hj j Hk C k KIdX X X DX X X dt I IC K H H K K K    + + + =     +         = − − =   +   +          + +      (1) The mass balance for TIN and TIP is expressed by Eq (2), where 𝜃(𝐼𝑎𝑣) accounts for the growth process dependency on the light: 𝜃(𝐼𝑎𝑣) = 1 if 𝐼𝑎𝑣 > 0, otherwise 𝜃(𝐼𝑎𝑣) = 0. ( ) ( )0 0          0 ,  ,     j j j j j j j TIC TIN TIP dC dX Y D C C C C dt dt  −   = − + =   = w di ath n (2) with 𝑌𝑗 representing the yield of biomass with respect to the limiting nutrient 𝑗. As reported in Eq (3), the mass balance of TIC has to take into account the carbon dioxide mass transfer from gas to the liquid phase:  ( ) ( ) ( ) 2 2 0 0 , 2                         0TIC r l a CO CO T CTIC TIC CIC TI TI dC dX V k P H CO Y D C C C C dt dt    = − + − + =   −  with (3) where 𝑉𝑟 is the reactor volume and 𝑘𝑙,𝑎 the volumetric transfer coefficient. Moreover, the CO2 dissolution gives rise to the production of 𝐶𝑂3 2− and 𝐻𝐶𝑂3 −, as shown in Table 1. Table 1. Equilibria involving carbon species in the liquid phase and values of equilibrium constants. ID Chemical Equilibrium pK Units Ref. R.1 1 2 2 3 eK CO H O HCO H− +⎯⎯→+ +⎯⎯ 4.15 × 10−7 mol 𝐿−1 Perrin et al., 1969 R. 2 2 2 3 3 Ke HCO H CO− + −⎯⎯→ +⎯⎯ 2.74 × 10−10 mol 𝐿−1 Perrin et al., 1969 R. 3 2 wK H O H OH+ −⎯⎯→ +⎯⎯ 6.84 × 10−15 mol2 𝐿−2 Perrin et al., 1969 Accordingly, the total carbon and the electroneutrality equations (Eq.4-5) are considered. 2 3 3 2 TICC CO HCO CO− −= + + (4) 2 3 30 2Alk H OH CO HCO+ − − −= + − − − (5) where Alk represents the non-carbonatic alkalinity of the medium (Concas et al., 2021a). By deriving Eq (4-5) with respect to the time and using the equilibrium relations to express the species concentration as a function of CO2 and H+, Eq (6) and (7) are obtained:    2 21 1 2 1 2 12 2 2 1TIC e e e e e e d Hd CO COdC K K K K K K dt dt dtH HH H + + ++ +         = + + + +                   (6)    2 1 2 21 2 1 2 2 2 3 42   0 1 1 e ee e w e d Hd CO K K COK K K K CO d Alk dt dt dtH H H H H + + + + + +     −     = + + + + + +                        (7) 113 Eq (6) and (7) represent an algebraic equation system where the time derivatives of [𝐶𝑂2] and [𝐻+] are the unknowns whose solution is expressed with Eq (8) and (9).   ( )       32 e1 2 e1 e2 2 e1 2 e2 [ ]   4 2TIC w d CO dCH d Alk H K H k CO H k k CO k CO H k dt dt dtg H + + + + + +            = + + + + +                (8) ( ) ( ) 2 2 e1 e1 e2 e1 e1 e22 TIC d H H k H k k k H k k dCdAlk H dt dt dtg H g H + + + + + + +           + + +           = +              (9) ( ) ( )( )  ( )2 2 2 e1 e1 e2 e1 e2 2 e1 4wg H K H H k H k k k k CO k H+ + + + +         = + + + + +          (10) Moreover, according to Concas et al. (2021b). the alkalinity variation is described through Eq (10). ( ) ( )0 0  0alk d Alk dX Y D Alk Alk Alk Alk dt dt = − + − =with (11) From the mathematical point of view the model consists of an ODEs system which can be easily solved by mens of specific routines available in MATLAB. It should be noted that the value of the dilution rate D was set equal to 0 when using the model to interpret the experimental data since the latter ones were obtained by operating the lab-scale photobioreactors in batch mode. On the contrary, when extrapolating the possibility to use the reactors on Mars in fed-batch mode different values of D were tested. 4. Results and discussions Figure 2a shows the comparison between the experimental data obtained when cultivating the algae in MM40 or ZM under a CO2 atmosphere and microgravity conditions. In both cases, the biomass concentration starts growing without showing a significant lag phase until the growth stops when the biomass concentration achieves a kind of steady state witnessing the occurred equivalence of growth and death rate in Eq. 1. 2 6 10 14 18 22 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 B io m a s s c o n c e n tr a ti o n ( g L -1 ) Time (days) ZM - CO2 - g MM40 - CO2 - g Model fitting Model prediction (a) MM40-CO2-g ZM-CO2-g 0.00 0.01 0.02 0.03 0.04 0.05 0.06 Model Experimental F in a l b io m a s s p ro d u c ti v it y , B P 2 2 ( g L -1 d a y -1 ) (b) Figure 1: Comparison between experimental and model results in terms of biomass concentration evolution (a) and biomass batch productivity after 22 days (b). The relevant aspect inferable from Figure 2 is that the alga cultivated in MM40 achieves a plateau at higher concentration in comparison to the ones cultivated in ZM. This means that, as shown in Figure 2b, by using in- situ available resources biomass productivity (0.048 g L-1 day-1) higher than the ones obtained with the ZM (0.044 g L-1 day-1), which relies only on compounds brought from Earth, could be achieved, with a relevant positive effect on the payload of the mission. It should be noted that the batch biomass productivity (BP22) is calculated at 22 days since this was the time needed by the cultures to achieve the steady state. This higher biomass concentration at the steady state could be due to the higher concentrations of crucial elements (likely iron) in the MM that are probably consumed during the growth in pure ZM. The experimental data related to the use of ZM were fitted by the proposed model by using literature values for specific model parameters and tuning remaining ones. In particular, the kinetic, thermodynamical and biological parameters of the model equations inferred from literature are those reported in Table 2 along with a specific reference while the yield coefficients 114 YTIP and YAlk were identified by fitting the experimental data (in terms of biomass and pH) through the function fmincon in MATLAB. Table 2. Model parameters Symbol Value Units Refs. 𝑯𝑪𝑶𝟐 3.89 × 10−4 mol Pa−1L−1 Sander et al., 2015 𝑰𝒌,𝒉 6.00 × 101 μmol m−2s−1 Grima et al., 1994 𝑲𝒍,𝒂 2.86 × 101 hr−1 Klöckner et al., 2012 𝑲𝟏 5.00 × 10−8 mol 𝐿−1 Concas et al. 2021b 𝑲𝟐 1.00 × 10−8 mol 𝐿−1 Concas et al. 2021b 𝒌𝟎.𝒊 1.50 × 10−1 hr−1 Concas et al. 2021b 𝒌𝟏.𝒊 1.30 × 10−1 hr−1 Concas et al. 2021b 𝑲𝑻𝑰𝑪 1.00 × 10−6 mol 𝐿−1 Marsullo et al., 2015 𝑲𝑻𝑰𝑵 3.78 × 10−5 mol 𝐿−1 Baldia et al., 2007 𝑲𝑻𝑰𝑷 9.04 × 10−7 mol 𝐿−1 Baldia et al., 2007 𝑿𝟎 1.25 × 10−1 g L−1 Measured 𝝁𝒎𝒂𝒙 1.00 × 10−2 hr−1 Jeffryes et al., 2013 𝝁𝒅 1.00 × 10−5 hr−1 Concas et al., 2013 𝒀𝑻𝑰𝑪 3.70 × 10−2 mol g−1 Cornet et al., 1992 𝒀𝑨𝒍𝒌 − 1.23 mol g−1 Tuned 𝒀𝑻𝑰𝑷 9.27 × 10−5 mol g−1 Tuned A good matching between experimental and model results was obtained as confirmed by the statistical values of R2 adj = 0.954 and MSE =5.1×10-3. Then the experimental data obtained when using MM40 were correctly predicted (R2 adj = 0.9853 and MSE =7.7×10-3) by the proposed model by keeping fixed the parameter values obtained as previously discussed. Such a good predictivity capability of the model is confirmed by the good between the experimental and model results even in terms of biomass productivity shown in Figure 2b. While the results so far obtained are promising, it should be stressed that the possible application of the technology described by Fais et al. (2022a) on Mars needs to be further corroborated by additional research activity aimed to verify the effect of the strong variation of operating conditions, such as temperature, light intensity and medium composition, that might take place on Mars. In this regard, the availability of a mathematical model might be strategic since it can provide an “a – priori” information about the response of the system to changing operating conditions. An example of how the model can be viably exploited to infer useful information is to assume that the algae will be grown in an open pond (Figure 3a) operated in fed-batch mode up until a specific time (tf) on Mars. Indeed, this working mode allows to produce biomass continuously removing the dead-times associated to the operations of growing the culture, reactor emptying and recharging typical of the batch processes. This way higher biomass productivity could be also achieved. For this reason, further simulations were carried out by considering the biomass productivities achievable after different operating for different times (tf) and using different dilution rates (D) in the equations already reported in the mathematical model section. The corresponding productivity was calculated according to the following equation. ( ) ( )  f i f t f t t f i D X t dt X t BP t t + = −  (12) The results obtained by running the model for several values of tf and D are summarized in contour plot of Figure 3b. It can be observed that the best results (5 g m-2 hr-1) are obtained by adopting a dilution rate ranging from 3 to 6 hr-1 and final times greater than 40 days. The corresponding biomass could be used to feed the crew. 5. Conclusions The objective of this paper is to simulate and evaluate the growth of microalgae on Mars accordingly to a process recently patented to achieve this, an experimental study is conducted where the Martian atmosphere is replicated, and a growth medium is synthesized using the planet's resources according to the ISRU paradigm. A mathematical model, capable to well predict the experimental results, is developed to accurately simulate the growth process under different operating conditions. The model is then utilized to predict the potential biomass productivity of an open pond located in a Martian dome and operated in fed-batch mode. Future studies can integrate temperature-dependent functions and assess specific kinetic parameters. 115 Figure 3. Render of a possible open pond on a Martian dome (a) and simulated effect of the cultivation of S. platensis on Mars in the pond fed-batch mode. References Baldia S. F, Evangelista A. D., Aralar E. V., Santiago A. E., 2007, Nitrogen and phosphorus utilization in the cyanobacterium Microcystis aeruginosa isolated 73 from Laguna de Bay, Philippines, Journal of Applied Phycology, 19, 607–613. Billi D., Gallego Fernandez B., Fagliarone C., Chiavarini S., Rothschild L.J., 2021, Exploiting a perchlorate- tolerant desert cyanobacterium to support bacterial growth for in situ resource utilization on Mars, International Journal of Astrobiology, 20.1, 29-35. Concas A., Corrias G., Orrù R., Licheri R., Pisu M., Cao G., 2012, Remarks on ISRU and ISFR technologies for manned missions on moon and Mars, Eurasian Chemo Technological Journal 14. Concas A., Steriti A., Pisu M., Cao G., 2021a, Experimental and theoretical investigation of the effects of iron on growth and lipid synthesis of microalgae in view of their use to produce biofuels, Journal of Environmental Chemical Engineering, 9(4), 105349. Concas A., Lutzu G. A., Dunford N. T., 2021b, Experiments and modeling of Komvophoron sp. growth in hydraulic fracturing wastewater, Chemical Engineering Journal, 426, 131299. Concas A., Pisu M., and Cao G., 2013, Mathematical modelling of Chlorella vulgaris growth in semi-batch photobioreactors fed with pure CO2, Chemical Engineering Transactions, 32. Concas A., Fais G., Enna, M., Zucchelli, S., Caboni, P., Lai, N., Cincotti A., Cao G., 2023, Modeling and experimental assessment of Synechococcus nidulans cultivation using simulated Martian medium and astronauts’ urine, Acta Astronautica, 205, 185-198 Cornet J. F., Dussap,C. G., Cluzel P., Dubertret G., 1992, A structured model for simulation of cultures of the cyanobacterium Spirulina platensis in photobioreactors: II. Identification of kinetic parameters under light and mineral limitations. Biotechnology and Bioengineering, 40(7), 826-834. De Man P., 2019, In-Situ Resource Utilization: Legal Aspect. Promoting Productive Cooperation Between Space Lawyers and Engineers, IGI Global. 211-224. Fais G., Manca A., Concas A., Pantaleo A., Cao G., 2022a, A novel process to grow edible microalgae on Mars by exploiting in situ-available resources: Experimental investigation, Acta Astronautica, 201, 454-463. Fais G., Manca A., Bolognesi F., Borselli M., Concas A., Busutti, M., ... and Giannaccare G., 2022b, Wide range applications of Spirulina: from earth to space missions, Marine Drugs, 20(5), 299. Jeffryes C., Rosenberger J., Rorrer G. L., 2013, Fed-batch cultivation and bioprocess modeling of Cyclotella sp. for enhanced fatty acid production by controlled silicon limitation, Algal Research, 2, 16–27. Klöckner W., Gacem R., Anderlei T., Raven N., Schillberg S., Lattermann C., Büchs, J., 2013, Correlation between mass transfer coefficient kLa and relevant operating parameters in cylindrical disposable shaken bioreactors on a bench-to-pilot scale, Journal of Biological Engineering, 7, 1–14. Marsullo M., Mian A., Ensinas A. V., Manente G., Lazzaretto A., Marechal F., 2015, Dynamic modeling of the microalgae cultivation phase for energy production in open raceway ponds and flat panel photobioreactors, Frontiers in Energy Research, 3, 41. Perrin D. D., 1969, Dissociation constants of inorganic acidsand bases in aqueous solution, Pure and Applied Chemistry, 20, 33–236. Sander R., 2015, Compilation of Henry’s law constants (version 4.0) for water as solvent,” Atmospheric Chemistry and Physics, 15, 4399–4981. 116 268brughitta.pdf Cultivation of Cyanobacteria and Microalgae using Simulated in-situ Available Resources for the Production of useful Bio-compounds on Mars: Modelling of Experiments