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Licensed Unlicensed Requires Authentication Published by De Gruyter July 31, 2020

Modelling and multi-objective optimization for simulation of hydrogen production using a photosynthetic consortium

  • Dulce J. Hernández-Melchor , Beni Camacho-Pérez , Elvira Ríos-Leal , Jesus Alarcón-Bonilla and Pablo A. López-Pérez EMAIL logo

Abstract

This study was aimed at finding the optimal conditions for hydrogen production based on statistical experiments and using a simulation approach. A Plackett–Burman design and steepest ascent were used to screen the key factors to obtain the best hydrogen concentration. According to the regression analysis, cysteine, acetate, and aeration had the best effect. The optimal conditions, using the method of steepest ascent, were aeration (0.125 L/min), acetate (200 mg/L), cysteine (498 mg/L). Once this was determined, an experiment with more than two factors was considered. The combinations: acetate + cysteine without aeration and cysteine without aeration increased hydrogen concentration. These last two criteria were used to validate the dynamic model based on unstructured kinetics. Biomass, nitrogen, acetate, and hydrogen concentrations were monitored. The proposed model was used to perform the multi-objective optimization for various desired combinations. The simultaneous optimization for a minimum ratio of cysteine-acetate improved the concentration of hydrogen to 20 mg/L. Biomass optimized the concentration of hydrogen to 11.5 mg/L. The simultaneous optimization of reaction time (RT) and cysteine improved hydrogen concentration to 28.19 mg/L. The experimental hydrogen production was 11.4 mg/L at 24 h under discontinuous operation. Finally, the proposed model and the optimization methodology calculated a higher hydrogen concentration than the experimental data.


Corresponding author: Pablo A. López-Pérez, Universidad Autónoma del Estado de Hidalgo, Escuela Superior Apan, Carretera Apan-Calpulalpan Km.8, Col. Chimalpa, 43920, Apan, Hgo, México, E-mail:

Funding source: Red Temática de Bioenergía del CONACYT, Universidad Tecnológica de Tecámac

Acknowledgments

PALP is grateful for the support provided by the Red Temática de Bioenergía del CONACYT, Universidad Tecnológica de Tecámac, water-treatment plant “Sierra Hermosa” Organismo Descentralizado de Agua Potable, Alcantarillado y Saneamiento (ODAPAS) of Tecámac, Mexico.

  1. Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: None declared.

  3. Conflict of interest statement: On behalf of all authors, the corresponding author states that there is no conflict of interest.

Nomenclature

β0

constant

βi

linear coefficient

kH2(cys)=e(((Cys0t)χ+Qairδ))
Cys0

initial concentration of cysteine

μ(t)

specific growth rate, and is a function of the concentrations of metabolites and biomass, among others

µmax, j

maximum specific rate for j concentrations

Qair

0.125 L/min

t

time

χi

coded factor levels

y

response (mg H2/L)

Y

simulated value of the variable at time ti (Eq. (9))

Y

observed value of the same variable at time ti

Y¯

mean value of the observed variable

References

Akgul, O., N. Shah, and L. Papageorgiou. 2012. “An Optimisation Framework for a Hybrid First/Second Generation Bioethanol Supplychain.” Computers and Chemical Engineering 42 (11): 101–14, https://doi.org/10.1016/j.compchemeng.2012.01.012.Search in Google Scholar

Alalayah, W. M., A. Al-Zahrani, G. Edris, and A. Demirbas. 2017. “Kinetics of Biological Hydrogen Production from Green Microalgae Chlorella Vulgaris Using Glucose as Initial Substrate.” Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 39 (12): 1210–15, https://doi.org/10.1080/15567036.2017.1315755.Search in Google Scholar

Alavi, M., R. Eslamloueyan, and M. Rahimpour. 2017. “Multi Objective Optimization of a Methane Steam Reforming Reaction in a Membrane Reactor: Considering the Potential Catalyst Deactivation due to the Hydrogen Removal.” International Journal of Chemical Reactor Engineering 16 (2), https://doi.org/10.1515/ijcre-2017-0066.Search in Google Scholar

Albarello, A., D. Simionato, T. Morosinotto, and F. Bezzo. 2019. “Model-Based Optimization of Microalgae Growth in a Batch Plant.” Industrial and Engineering Chemistry Research 58 (13): 5121–30, https://doi.org/10.1021/acs.iecr.9b00270.Search in Google Scholar

Álvarez, P., L. Pérez, J. L. Salgueiro, Á. Cancela, Á. Sánchez, and L. Ortiz. 2017. “Bioenergy Use from Pavlovalutheri Microalgae.” International Journal of Environmental Research 11 (3): 281–89, https://doi.org/10.1007/s41742-017-0026-2.Search in Google Scholar

Bakonyi, P., N. Nemestóthy, É. Lövitusz, and K. Bélafi-Bakó. 2011. “Application of Plackett-Burman Experimental Design to Optimize Biohydrogen Fermentation by E. coli (XL1-BLUE).” International Journal of Hydrogen Energy 36 (21): 13949–54, https://doi.org/10.1016/j.ijhydene.2011.03.062.Search in Google Scholar

Bao, M. D., H. J. Su, and T. W. Tan. 2013. “Dark Fermentative Bio-Hydrogen Production: Effects of Substrate Pre-Treatment and Addition of Metal Ions or L-cysteine.” Fuel 112: 38–44, https://doi.org/10.1016/j.fuel.2013.04.063.Search in Google Scholar

Ben Said, S., and D. Or. 2017. “Synthetic Microbial Ecology: Engineering Habitats for Modular Consortia.” Frontiers in Microbiology 8: 1125, https://doi.org/10.3389/fmicb.2017.01125.Search in Google Scholar PubMed PubMed Central

Buitrón, G., J. Carrillo-Reyes, M. Morales, C. Faraloni, and G. Torzillo. 2017. “Biohydrogen Production from Microalgae.” In Microalgae-Based Biofuels and Bioproducts, edited by C. Gonzalez-Fernandez, and R. Muñoz, 209–34. USA: Woodhead Publishing Series in Energy.10.1016/B978-0-08-101023-5.00009-1Search in Google Scholar

Castillo, O. S., S. G. Torres-Badajoz, C. A. Nuñez-Colin, V. Peña-Caballero, C. H. Herrera-Mendez, and J. R. Rodriguez-Nuñez. 2017. “Biodiesel Production from Microalgae: Progress and Biotechnological Prospects.” Hidrobiológica 27 (3): 337–52, https://doi.org/10.24275/uam/izt/dcbi/hidro/2017v27n3/Rodriguez.10.24275/uam/izt/dcbs/hidro/2017v27n3/RodriguezSearch in Google Scholar

Chen, X., W. Zhong, T. Wang, F. Liu, and Z. Zhang. 2014. “Genetic Optimization of Energy Consumption of Pellet Shaft Furnace Combustor Based on Support Vector Machine (SVM).” International Journal of Chemical Reactor Engineering 12 (1): 205–14, https://doi.org/10.1515/ijcre-2013-0117.Search in Google Scholar

Chiandussi, G., M. Codegone, S. Ferrero, and F. E. Varesio. 2012. “Comparison of Multi-Objective Optimization Methodologies for Engineering Applications.” Computers and Mathematics with Applications 63 (5): 912–42, https://doi.org/10.1016/j.camwa.2011.11.057.Search in Google Scholar

Cho, S., and J. Kim. 2019. “Multi-Site and Multi-Period Optimization Model for Strategic Planning of a Renewable Hydrogen Energy Network from Biomass Waste and Energy Crops.” Energy 185: 527–40, https://doi.org/10.1016/j.energy.2019.07.053.Search in Google Scholar

Coello, C. A. 2002. “Theoretical and Numerical Constraint-Handling Techniques Used with Evolutionary Agorithms: A Survey of the State of the Art.” Computer Methods in Applied Mechanics and Engineering 191: 1245–87,https://doi.org/10.1016/S0045-7825(01)00323-1.Search in Google Scholar

Colín-Luna, J. A., E. G. Zamora-Rodea, M. M. González-Brambila, E. Barrera-Calva, R. Rosas-Cedillo, A. K. Medina-Mendoza, and J. C. García-Martínez. 2018. “Biodiesel Production Using Immobilized Lipase Supported on a Zirconium-Pillared Clay. Effect of the Immobilization Method.” International Journal of Chemical Reactor Engineering 16 (11): 20170260, https://doi.org/10.1515/ijcre-2017-0260.Search in Google Scholar

Deb, K. 2001. Multi-Objective Optimization Using Evolutionary Algorithms. New York: John Wiley & Sons.Search in Google Scholar

Dibaba, O. R., S. K. Lahiri, S. T’Jonck, and A. Dutta. 2016. “Experimental and Artificial Neural Network Modeling of a Upflow Anaerobic Contactor (UAC) for Biogas Production from Vinasse.” International Journal of Chemical Reactor Engineering 14 (6): 1241–54, https://doi.org/10.1515/ijcre-2016-0025.Search in Google Scholar

Faizollahzadeh Ardabili, S., B. Najafi, S. Shamshirband, B. Minaei Bidgoli, R. C. Deo, and K. W. Chau. 2018. “Computational Intelligence Approach for Modeling Hydrogen Production: A Review.” Engineering Applications of Computational Fluid Mechanics 12 (1): 438–58, https://doi.org/10.1080/19942060.2018.1452296.Search in Google Scholar

Fakhimi, N., and O. Tavakoli. 2019. “Improving Hydrogen Production Using Co-Cultivation of Bacteria with Chlamydomonas reinhardtii Microalga.” Materials Science for Energy Technologies 2 (1): 1–7, https://doi.org/10.1016/j.mset.2018.09.003.Search in Google Scholar

Fivga, A., L. G. Speranza, C. M. Branco, M. Ouadi, and A. Hornung. 2019. “A Review on the Current State of the Art for the Production of Advanced Liquid Biofuels.” AIMS Energy 7 (1): 46–76, https://doi.org/10.3934/energy.2019.1.46.Search in Google Scholar

Garnier, A., and B. Gaillet. 2015. “Analytical Solution of Luedeking–Piret Equation for a Batch Fermentation Obeying Monod Growth Kinetics.” Biotechnology and Bioengineering 112: 2468–74, https://doi.org/10.1002/bit.25669.Search in Google Scholar PubMed

Haupt, R. L., and S. E. Haupt. 2004. Practical Genetic Algorithms. New Jersey: John Wiley & Sons.10.1002/0471671746Search in Google Scholar

Hecke, V. T. 2017 “The Levenberg-Marquardt Method to Fit Parameters in the Monod Kinetic Model.” Journal of Statistics and Management Systems 20 (5): 953–63, https://doi.org/10.1080/09720510.2017.1325090.Search in Google Scholar

Hernández-Hernández, A., E. Vallejo, F. Martínez-Farías, J. J. Pelayo, L. A. Hernández-Hernández, J. A. Pescador-Rojas, L. Tamayo-Rivera, A. Morales-Peñaloza, P. A. López-Pérez, and E. Rangel Cortes. 2018. “Changes to the Dissociation Barrier of H2 Due to Buckling Induced by a Chemisorbed Hydrogen on a Doped Graphene Surface.” Journal of Molecular Modeling 24 (9): 244, https://doi.org/10.1007/s00894-018-3763-z.Search in Google Scholar PubMed

Hernández-Melchor, D. J., P. A. López-Pérez, S. Carrillo-Vargas, A. Alberto-Murrieta, E. González-Gómez, and B. Camacho-Pérez. 2018. “Experimental and Kinetic Study for Lead Removal via Photosynthetic Consortia Using Genetic Algorithms to Parameter Estimation.” Environmental Science and Pollution Research 25: 21286–95, https://doi.org/10.1007/s11356-017-0023-1.Search in Google Scholar PubMed

Jagadevan, S., A. Banerjee, C. Banerjee, C. Guria, R. Tiwari, M. Baweja, and P. Shukla. 2018. “Recent Developments in Synthetic Biology and Metabolic Engineering in Microalgae Towards Biofuel Production.” Biotechnology for Biofuels 11 (1): 185, https://doi.org/10.1186/s13068-018-1181-1.Search in Google Scholar PubMed PubMed Central

Jayaraman, S. K., and R. R. Rhinehart. 2015. “Modeling and Optimization of Algae Growth.” Industrial & Engineering Chemistry Research 54 (33): 8063–71, https://doi.org/10.1021/acs.iecr.5b01635.Search in Google Scholar

Karimi, S., N. Mostoufi, and R. Sotudeh-Gharebagh. 2013. “Application of Honey-Bee Mating Optimization to Naphtha Reforming Reactor.” International Journal of Chemical Reactor Engineering 11 (1): 293–308, https://doi.org/10.1515/ijcre-2013-0035.Search in Google Scholar

Khetkorn, W., A. Rastogi, P. Incharoensakdi, D. Lindblad, A. Madamwar, C. Pandey, and R. P. Larroche. 2017. “Microalgal Hydrogen Production – A Review.” Bioresource Technology 243: 1194–206, https://doi.org/10.1016/j.biortech.2017.07.085.Search in Google Scholar PubMed

Li, P., L. Chen, S. Xia, and L. Zhang. 2019. “Maximum Hydrogen Production Rate Optimization for Tubular Steam Methane Reforming Reactor.” International Journal of Chemical Reactor Engineering 17 (9), https://doi.org/10.1515/ijcre-2018-0191.Search in Google Scholar

Link, H., and D. Weuster-Botz. 2006. “Genetic Algorithm for Multi-Objective Experimental Optimization.” Bioprocess and Biosystems Engineering 29 (5–6): 385–90, https://doi.org/10.1007/s00449-006-0087-7.Search in Google Scholar PubMed PubMed Central

Long, C., J. Cui, Z. Liu, Y. Liu, M. Long, and Z. Hu. 2010. “Statistical Optimization of Fermentative Hydrogen Production from Xylose by Newly Isolated Enterobacter sp. CN1.” International Journal of Hydrogen Energy 35 (13): 6657–64, https://doi.org/10.1016/j.ijhydene.2010.04.094.Search in Google Scholar

López, R. A, B. R. CamachoNeria-González, O. Santos, and P. A López-Pérez. 2017. “State Estimation Based on Nonlinear Observer for Hydrogen Production in a Photocatalytic Anaerobic Bioreactor.” International Journal of Chemical Reactor Engineering 15 (5), https://doi.org/10.1515/ijcre-2017-0004.Search in Google Scholar

López-Pérez, P. A., R. Neria-González, and L. Aguilar. 2015. “Increasing the Bio-Hydrogen Production in a Continuous Bioreactor via Nonlinear Feedback Controller.” International Journal of Hydrogen Energy 40 (48): 17224–30, https://doi.org/10.1016/j.ijhydene.2015.09.106.Search in Google Scholar

López-Pérez, P. A., J. A. Cuervo-Parra, V. J. Robles-Olvera, G. D. Rodriguez Jimenes, V. H. España, and T. Romero-Cortes. 2018. “Development of a Novel Kinetic Model for Cocoa Fermentation Applying the Evolutionary Optimization Approach.” International Journal of Food Engineering 14 (5–6): 20170206, https://doi.org/10.1515/ijfe-2017-0206.Search in Google Scholar

López-Pérez, P. A., R. Aguilar-Lopez, O. S. Castillo-Baltazar, E. Vallejo Castañeda, and V. Peña Caballero. 2019. “Virtual Sensors for Bio-Fuels Production: A Brief Mathematical Description for Synthesis of Algorithms.” Comptes Rendus De L Academie Bulgare Des Sciences 72 (10): 1383–92, https://doi.org/10.7546/CRABS.2019.10.11.10.7546/CRABS.2019.10.11Search in Google Scholar

López Pérez, P. A., R. Aguilar López, and R. Femat. 2020. Control in Bioprocessing: Modeling, Estimation and the Use of Soft Sensors Part I: Overview of the Control and Monitoring of Bioprocesses and Mathematical Preliminaries. Chichester, UK: John Wiley & Sons Ltd.10.1002/9781119296317Search in Google Scholar

Mariano, A., Costa, C., de Angelis, D., Pires Atala, D., Maugeri Filho, F., Wolf Maciel, M., and R. Maciel Filho. 2010. “Genetic Algorithms (Binary and Real Codes) for the Optimisation of a Fermentation Process for Butanol Production.” International Journal of Chemical Reactor Engineering 8 (1), https://doi.org/10.2202/1542-6580.2333 (accessed June 18, 2020).Search in Google Scholar

Marsullo, M., A. Mian, A. V. Ensinas, G. Manente, A. Lazzaretto, and F. Marechal. 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, https://doi.org/10.3389/fenrg.2015.00041.Search in Google Scholar

Márquez-Reyes, L. A., M. del P. Sánchez-Saavedra, and I. Valdez-Vazuez. 2015. “Improvement of Hydrogen Production by Reduction of the Photosynthetic Oxygen in Microalgae Cultures of ChlamyDomonas Gloeopara and Scenedesmus Obliquus.” International Journal of Hydrogen Energy 40 (23): 7291–300, https://doi.org/10.1016/j.ijhydene.2015.04.060.Search in Google Scholar

Melis, A., L. Zhang, M. Forestier, M. L. Ghirardi, and M. Seibert. 2000. “Sustained Photobiological Hydrogen Gas Production upon Reversible Inactivation of Oxygen Evolution in the Green Alga Chlamydomonas reinhardtii.” Plant Physiology 122: 127–35, https://doi.org/10.1104/pp.122.1.127.Search in Google Scholar PubMed PubMed Central

Nasr, N., H. Hafez, M. H. El Naggar, and G. Nakhla. 2013. “Application of Artificial Neural Networks for Modeling of Biohydrogen Production.” International Journal of Hydrogen Energy 38 (8): 3189–95, https://doi.org/10.1016/j.ijhydene.2012.12.109.Search in Google Scholar

Oncel, S., and A. Kose. 2014. “Comparison of Tubular and Panel Type Photobioreactors for Biohydrogen Production Utilizing Chlamydomonas reinhardtii Considering Mixing Time and Light Intensity.” Bioresource Technology 151: 265–70, https://doi.org/10.1016/j.biortech.2013.10.076.Search in Google Scholar PubMed

Padmaperuma, G., R. V. Kapoore, D. J. Gilmour, and S. Vaidyanathan. 2018. “Microbial Consortia: A Critical Look at Microalgae Co-Cultures for Enhanced Biomanufacturing.” Critical Reviews in Biotechnology 38 (5): 690–03, https://doi.org/10.1080/07388551.2017.1390728.Search in Google Scholar PubMed

Palys, M., S. Ivanov, and A. Ray. 2016. “Conceptual Approach in Multi-Objective Optimization of Packed Bed Membrane Reactor for Ethylene Epoxidation Using Real-coded Non-Dominating Sorting Genetic Algorithm NSGA-II.” International Journal of Chemical Reactor Engineering 15 (1), https://doi.org/10.1515/ijcre-2016-0041.Search in Google Scholar

Pan, C. M., Y. T. Fan, Y. Xing, H. W. Hou, and M. L. Zhang. 2008. “Statistical Optimization of Process Parameters on Biohydrogen Production from Glucose by Clostridium sp. Fanp2.” Bioresource Technology 99 (8): 3146–54, https://doi.org/10.1016/j.biortech.2007.05.055.Search in Google Scholar PubMed

Pankratz, S., A. O. Oyedun, and A. Kumar. 2019. “Novel Satellite Based Analytical Model Developed to Predict Microalgae Yields in Open Pond Raceway Systems and Applied to Canadian Sites.” Algal Research 39: 101431, https://doi.org/10.1016/j.algal.2019.101431.Search in Google Scholar

Park, J. H., Y. B. Sim, G. Kumar, P. Anburajan, J. H. Park, H. D. Park, and S. H. Kim. 2018. “Kinetic Modeling and Microbial Community Analysis for High-Rate Biohydrogen Production Using a Dynamic Membrane.” Bioresource Technology 262: 59–64, https://doi.org/10.1016/j.biortech.2018.04.070.Search in Google Scholar PubMed

Perera, I. A., S. Abinandan, S. R. Subashchandrabose, K. Venkateswarlu, R. Naidu, and M. Megharaj. 2019. “Advances in the Technologies for Studying Consortia of Bacteria and Cyanobacteria/Microalgae in Wastewaters.” Critical Reviews in Biotechnology 39 (5): 709–31, https://doi.org/10.1080/07388551.2019.1597828.Search in Google Scholar PubMed

Ribeiro, L. A., P. P. da Silva, T. M. Mata, and A. A. Martins. 2015. “Prospects of Using Microalgae for Biofuels Production: Results of a Delphi Study.” Renewable Energy 75: 799–804, https://doi.org/10.1016/j.renene.2014.10.065.Search in Google Scholar

Salkuyeh, Y. K., B. A. Saville, and H. L. MacLean. 2018. “Techno-Economic Analysis and Life Cycle Assessment of Hydrogen Production from Different Biomass Gasification Processes.” International Journal of Hydrogen Energy 43 (20): 9514–28, https://doi.org/10.1016/j.ijhydene.2018.04.024.Search in Google Scholar

Sánchez-García, D., H. Hernández-García, H. O. Méndez-Acosta, A. Hernández-Aguirre, H. Puebla, and E. Hernández-Martínez. 2018. “Fractal Analysis of pH Time-Series of an Anaerobic Digester for Cheese Whey Treatment.” International Journal of Chemical Reactor Engineering 16 (11), https://doi.org/10.1515/ijcre-2017-0261.Search in Google Scholar

Sangyoka, S., A. Reungsang, and C. Y. Lin. 2016. “Optimization of Biohydrogen Production from Sugarcane Bagasse by Mixed Cultures Using a Statistical Method.” Sustainable Environment Research 26 (5): 235–42, https://doi.org/10.1016/j.serj.2016.05.001.Search in Google Scholar

Shetty, P., I. Z. Boboescu, B. Pap, R. Wirth, K. L. Kovács, T. Bíró, Z. Futó, R. A. WhiteIII, and G. Maróti. 2019. “Exploitation of Algal-Bacterial Consortia in Combined Biohydrogen Generation and Wastewater Treatment.” Frontiers in Energy Research 7: 52, https://doi.org/10.3389/fenrg.2019.00052.Search in Google Scholar

Singh, R., J. Ryu, and S. W. Kim. 2019. “Microbial Consortia Including Methanotrophs: Some Benefits of Living Together.” Journal of Microbiology 57 (11): 939–52, https://doi.org/10.1007/s12275-019-9328-8.Search in Google Scholar PubMed

Staffell, I., D. Scamman, A. V. Abad, P. Balcombe, P. E. Dodds, P. Ekins, N. Shah, and K. R. Ward. 2019. “The Role of Hydrogen and Fuel Cells in the Global Energy System.” Energy and Environmental Science 12 (2): 463–91, https://doi.org/10.1039/c8ee01157e.Search in Google Scholar

Subashchandrabose, S. R., B. Ramakrishnan, M. Megharaj, K. Venkateswarlu, and R. Naidu. 2011. “Consortia of Cyanobacteria/Microalgae and Bacteria: Biotechnological Potential.” Biotechnology Advances 29 (6): 896–907, https://doi.org/10.1016/j.biotechadv.2011.07.009.Search in Google Scholar PubMed

Sun, Y., G. Yang, J. Zhang, C. Wen, and Z. Sun. 2019. “Optimization and Kinetic Modeling of an Enhanced Bio-Hydrogen Fermentation with the Addition of Synergistic Biochar and Nickel Nanoparticle.” International Journal of Energy Research 43: 983–99, https://doi.org/10.1002/er.4342.Search in Google Scholar

Tokos, H., Z. N. Pintarič, and Y. Yang. 2013. “Bi-Objective Optimization of a Water Network via Benchmarking.” Journal of Cleaner Production 39: 168–79, https://doi.org/10.1016/j.jclepro.2012.07.051.Search in Google Scholar

Vatcheva, I., H. de Jong, O. Bernard, and N. J. I. Mars. 2006. “Experiments Election for the Discrimination of Semi-Quantitative Models of Dynamical Systems.” Artificial Intelligence 170 (4–5): 472–506, https://doi.org/10.1016/j.artint.2005.11.001.Search in Google Scholar

Velázquez-Sánchez, H. I., and R. Aguilar-López. 2019. “Multi-Objective Optimization of an ABE Fermentation System for Butanol Production as Biofuel.” International Journal of Chemical Reactor Engineering 17 (7): 20180214, https://doi.org/10.1515/ijcre-2018-0214.Search in Google Scholar

Veziroğlu, T. N., and S. Şahi. 2008. “21st Century’s Energy: Hydrogen Energy System.” Energy Conversion and Management 49 (7): 1820–831, https://doi.org/10.1016/j.enconman.2007.08.015.Search in Google Scholar

Wang, S., Z. Ma, T. Zhang, M. Bao, and H. Su. 2017. “Optimization and Modeling of Biohydrogen Production by Mixed Bacterial Cultures from Raw Cassava Starch.” Frontiers of Chemical Science and Engineering 11 (1): 100–6, https://doi.org/10.1007/s11705-017-1617-3.Search in Google Scholar

Weuster-Botz, D., R. Puskeiler, A. Kusterer, K. Kaufmann, G. T. John, and M. Arnold. 2005. “Methods Milliliter Scale Devices for High-Throughput Bioprocess Design.” Bioprocess and Biosystems Engineering 28 (2): 109–19, https://doi.org/10.1007/s00449-005-0011-6.Search in Google Scholar PubMed

Xie, G., B. Liu, D. Xing, J. Nan, J. R. Ding, and N. Qi. 2013. “Photo-Fermentative Bacteria Aggregation Triggered by L-Cysteine During Hydrogen Production.” Biotechnology for Biofuels 6 (64): 1–14, https://doi.org/10.1186/1754-6834-6-64.Search in Google Scholar PubMed PubMed Central

Yuan, Z., H. Yang, X. Zhi, and J. Shen. 2008. “Enhancement Effect of L-Cysteine on Dark Fermentative Hydrogen Production.” International Journal of Hydrogen Energy 33 (22): 6535–40, https://doi.org/10.1016/j.ijhydene.2008.07.065.Search in Google Scholar

Received: 2020-01-31
Accepted: 2020-05-24
Published Online: 2020-07-31

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