Machine learning in biohydrogen production: a review

Document Type : Review Paper


1 Department of Mechanical Engineering, GMR Institute of Technology, Rajam-532127, Andhra Pradesh, India.

2 Department of Mechanical Engineering, KPR Institute of Engineering and Technology, Coimbatore 641407, Tamilnadu, India.

3 Department of Chemical and Petroleum Engineering, University of Calgary, 2500 University Drive NW, Calgary, Alberta, T2N 1N4, Canada.

4 Fluid Mechanics, Thermal Engineering and Multiphase Flow Research Lab. (FUTURE), Department of Mechanical Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi, Bangmod, Bangkok 10140, Thailand.

5 National Science and Technology Development Agency (NSTDA), Pathum Thani 12120, Thailand.

6 Department of Bioengineering and Imperial College Centre for Synthetic Biology, Imperial College London, London SW7 2AZ, UK.

7 School of Chemical Engineering and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China.

8 Department of Chemical Engineering, Imperial College London, London SW7 2AZ, UK.

9 Laboratory on Convective Heat and Mass, Transfer, Tomsk State University, 634050 Tomsk, Russia.

10 State Key Laboratory of Multiphase Flow in Power Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi,710049 P.R. China.


Biohydrogen is emerging as a promising carbon-neutral and sustainable energy carrier with high energy yield to replace conventional fossil fuels. However, biohydrogen commercial uptake is mainly hindered by the supply side. As a result, various operating parameters must be optimized to realize biohydrogen commercial uptake on a large-scale. Recently, machine learning algorithms have demonstrated the ability to handle large amounts of data while requiring less in-depth knowledge of the system and being capable of adapting to evolving circumstances. This review critically reviews the role of machine learning in categorizing and predicting data related to biohydrogen production. The accuracy and potential of different machine learning algorithms are reported. Also, the practical implications of machine learning models to realize biohydrogen uptake by the transportation sector are discussed. The review indicates that machine learning algorithms can successfully model non-linear and complex interactions between operational and performance parameters in biohydrogen production. Additionally, machine learning algorithms can help researchers identify the most efficient methods for producing biohydrogen, leading to a more sustainable and cost-effective energy source.

Graphical Abstract

Machine learning in biohydrogen production: a review


  • The role of machine learning (ML) in biohydrogen production is detailed.
  • ML can predict complex data and identify patterns in biohydrogen production.
  • The patent landscape suggests promising potential for biohydrogen to replace fossil fuels.
  • Improving ML predictive performance in biohydrogen production is a future need.


  1. Abraham, R.E., Verma, M.L., Barrow, C.J., Puri, M., 2014. Suitability of magnetic nanoparticle immobilised cellulases in enhancing enzymatic saccharification of pretreated hemp biomass. Biotechnol. Biofuels. 7, 90.
  2. Aghbashlo, M., Hosseinpour, S., Tabatabaei, M., Dadak, A., Younesi, H., Najafpour, G., 2016. Multi-objective exergetic optimization of continuous photo-biohydrogen production process using a novel hybrid fuzzy clustering-ranking approach coupled with Radial Basis Function (RBF) neural network. Int. J. Hydrogen Energy. 41, 18418-18430.
  3. Ahmad, R., Khare, S.K., 2018. Immobilization of Aspergillus niger cellulase on multiwall carbon nanotubes for cellulose hydrolysis. Bioresour. Technol. 252, 72-75.
  4. Ahmad, S.I., Rashid, R., Hashim, Z., Meng, C.C., Lun, C.K., Jumaatuden, D.M.H., Yasin, N.A., Jati, A., Hassim, M.H., 2022. Economic study on biohydrogen production from liquid pineapple waste. Clean Technol. Environ. Policy. 25(2), 703-716.
  5. Akubude, V.C., Okafor, V.C., Oyedokun, J.A., Petinrin, O.O., Nwaigwe, K.N., 2021. Application of Hemicellulose in Biohydrogen Production BT - Sustainable Bioconversion of Waste to Value Added Products, in: Inamuddin, Khan, A. (Eds.), Springer International Publishing, Cham, pp. 315-327.
  6. Asrul, M.A.M., Atan, M.F., Abdul Halim Yun, H., Lai, J.C.H., 2022. A review of advanced optimization strategies for fermentative biohydrogen production processes. Int. J. Hydrogen Energy. 47(38), 16785-16804.
  7. Azwar, M.Y., Hussain, M.A., Abdul-Wahab, A.K., 2014. Development of biohydrogen production by photobiological, fermentation and electrochemical processes: a review. Renew. Sust. Energy Rev. 31, 158-173.
  8. Balachandar, G., Khanna, N., Das, D., 2013. Chapter 6 - Biohydrogen Production from Organic Wastes by Dark Fermentation, in: Pandey, A., Chang, J.S., Hallenbecka, P.C., Larroche, C. (Eds.), Biohydrogen. Elsevier, Amsterdam, pp. 103-144.
  9. Bertolini, M., Mezzogori, D., Neroni, M., Zammori, F., 2021. Machine Learning for industrial applications: a comprehensive literature review. Expert Syst. Appl. 175, 114820.
  10. Cao, L., Yu, I.K.M., Xiong, X., Tsang, D.C.W., Zhang, S., Clark, J.H., Hu, C., Ng, Y.H., Shang, J., Ok, Y.S., 2020. Biorenewable hydrogen production through biomass gasification: a review and future prospects. Res. 186, 109547.
  11. Cardeña, R., Cercado, B., Buitrón, G., 2019. Chapter 7 - Microbial Electrolysis Cell for Biohydrogen Production, in: Pandey, A., Mohan, S.V., Chang, J.-S., Hallenbeck, P.C., Larroche, C. (Eds.), Biohydrogen (Second Edition), Biomass, Biofuels, Biochemicals. Elsevier, pp. 159-185.
  12. Carere, C.R., Sparling, R., Cicek, N., Levin, D.B., 2008. Third generation biofuels via direct cellulose fermentation. Int. J. Mol. Sci. 9(7), 1342-1360.
  13. Chang, R.H.Y., Jang, J., Wu, K.C.W., 2011. Cellulase immobilized mesoporous silica nanocatalysts for efficient cellulose-to-glucose conversion. Green Chem. 13(10), 2844-2850.
  14. Chari, N., Felix, L., Davoodbasha, M., Sulaiman Ali, A., Nooruddin, T., 2017. In vitro and in vivo antibiofilm effect of copper nanoparticles against aquaculture pathogens. Biocatal. Agric. Biotechnol. 10, 336-341.
  15. Chen, C.Y., Liu, C.H., Lo, Y.C., Chang, J.S., 2011. Perspectives on cultivation strategies and photobioreactor designs for photo-fermentative hydrogen production. Bioresour. Technol. 102(18), 8484-8492.
  16. Cheng, J., Li, H., Ding, L., Zhou, J., Song, W., Li, Y.Y., Lin, R., 2020. Improving hydrogen and methane co-generation in cascading dark fermentation and anaerobic digestion: the effect of magnetite nanoparticles on microbial electron transfer and syntrophism. Chem. Eng. J. 397, 125394.
  17. Demirbas, A., 2009. Biofuels from agricultural biomass. Energy Sources, Part A. 31(17), 1573-1582.
  18. Deng, F., Huang, J., Yuan, X., Cheng, C., Zhang, L. 2021. Performance and efficiency of machine learning algorithms for analyzing rectangular biomedical data. Lab Invest. 101(4), 430-441.
  19. Dutta, N., Mukhopadhyay, A., Dasgupta, A.K., Chakrabarti, K., 2014. Improved production of reducing sugars from rice husk and rice straw using bacterial cellulase and xylanase activated with hydroxyapatite nanoparticles. Bioresour. Technol. 153, 269-277.
  20. Dutta, N., Usman, M., Ashraf, M.A., Luo, G., Gamal El-Din, M., Zhang, S., 2022. Methods to convert lignocellulosic waste into biohydrogen, biogas, bioethanol, biodiesel and value-added chemicals: a review. Environ. Chem. Lett. 21(2), 803-820.
  21. Fortes, C.C.S., Daniel-da-Silva, A.L., Xavier, A.M.R.B., Tavares, A.P.M., 2017. Optimization of enzyme immobilization on functionalized magnetic nanoparticles for laccase biocatalytic reactions. Chem. Eng. Process. Process Intensif. 117, 1-8.
  22. Ganguli, A., Bhatt, V., 2023. Hydrogen production using advanced reactors by steam methane reforming: a review.
  23. Ghasemian, M., Taheri, E., Fatehizadeh, A., Amin, M., 2019. Biological hydrogen production from synthetic wastewater by an anaerobic migrating blanket reactor: artificial neural network (ANN) modeling. Environ. Health Eng. Manage. J. 6(4), 269-276.
  24. Ghirardi, M.L., King, P.W., Mulder, D.W., Eckert, C., Dubini, A., Maness, P.C., Yu, J., 2014. Hydrogen Production by Water Biophotolysis BT - Microbial BioEnergy: Hydrogen Production, in: Zannoni, D., De Philippis, R. (Eds.), . Springer Netherlands, Dordrecht, pp. 101-135.
  25. Hallenbeck, P.C., 2013. Chapter 7-Photofermentative Biohydrogen Production, in: Pandey, A., Chang, J.S., Hallenbecka, P.C., Larroche, C. (Eds.), Biohydrogen. Elsevier, Amsterdam, pp. 145-159.
  26. Hosseinzadeh, A., Zhou, J.L., Altaee, A., Li, D., 2022a. Machine learning modeling and analysis of biohydrogen production from wastewater by dark fermentation process. Bioresour. Technol. 343, 126111.
  27. Hosseinzadeh, A., Zhou, J.L., Li, X., Afsari, M., Altaee, A., 2022b. Techno-economic and environmental impact assessment of hydrogen production processes using bio-waste as renewable energy resource. Renew. Sust. Energy Rev. 156, 111991.
  28. Jacob, A., Xia, A., Murphy, J.D., 2015. A perspective on gaseous biofuel production from micro-algae generated from CO2 from a coal-fired power plant. Appl. Energy. 148, 396-402.
  29. Jiménez-Llanos, J., Ramírez-Carmona, M., Rendón-Castrillón, L., Ocampo-López, C., 2020. Sustainable biohydrogen production by Chlorella microalgae: a review. Int. J. Hydrogen Energy. 45(15), 8310-8328.
  30. Jordan, J., Kumar, C.S.S.R., Theegala, C., 2011. Preparation and characterization of cellulase-bound magnetite nanoparticles. J. Mol. Catal. B: Enzym. 68(2), 139-146.
  31. Kaloudas, D., Pavlova, N., Penchovsky, R., 2021. Lignocellulose, algal biomass, biofuels and biohydrogen: a review. Environ. Chem. Lett. 19, 2809-2824.
  32. Kapdan, I.K., Kargi, F., 2006. Bio-hydrogen production from waste materials. Enzyme Microb. Technol. 38(5), 569-582.
  33. Kim, M.S., Cha, J., Kim, D.H., 2013. Chapter 11 - Fermentative Biohydrogen Production from Solid Wastes, in: Pandey, A., Chang, J.S., Hallenbecka, P.C., Larroche, C. (Eds.), Biohydrogen. Elsevier, Amsterdam, pp. 259-283.
  34. Kiran, E.U., Trzcinski, A.P., Ng, W.J., Liu, Y., 2014. Bioconversion of food waste to energy: a review. Fuel. 134, 389-399.
  35. Kumar, G., Cho, S.K., Sivagurunathan, P., Anburajan, P., Mahapatra, D.M., Park, J.H., Pugazhendhi, A., 2018. Insights into evolutionary trends in molecular biology tools in microbial screening for biohydrogen production through dark fermentation. Int. J. Hydrogen Energy. 43(43), 19885-19901.
  36. Kumar, G., Mathimani, T., Rene, E.R., Pugazhendhi, A., 2019. Application of nanotechnology in dark fermentation for enhanced biohydrogen production using inorganic nanoparticles. Int. J. Hydrogen Energy. 44(26), 13106-13113.
  37. Kumar Sharma, A., Kumar Ghodke, P., Goyal, N., Nethaji, S., Chen, W.H., 2022. Machine learning technology in biohydrogen production from agriculture waste: recent advances and future perspectives. Bioresour. Technol. 364, 128076.
  38. Kumar, V., Kothari, R., Singh, S., 2015. Dark Fermentation: a green way to produce hydrogen and methane. Int. J. Sci. Technol. Soc. 1.
  39. LewisOscar, F., MubarakAli, D., Nithya, C., Priyanka, R., Gopinath, V., Alharbi, N.S., Thajuddin, N., 2015. One pot synthesis and anti-biofilm potential of copper nanoparticles (CuNPs) against clinical strains of Pseudomonas aeruginosa. Biofouling. 31(4), 379-391.
  40. LewisOscar, F.L., Vismaya, S., Arunkumar, M., Thajuddin, N., Dhanasekaran, D., Nithya, C., 2016. Algal Nanoparticles: Synthesis and Biotechnological Potentials, in: Thajuddin, N., Dhanasekaran, D. (Eds.), . IntechOpen, Rijeka, p. Ch. 7.
  41. Liu, H., Zhang, Z., Zhang, H., Lee, D.J., Zhang, Q., Lu, C., He, C., 2020. Evaluation of hydrogen yield potential from Chlorella by photo-fermentation under diverse substrate concentration and enzyme loading. Bioresour. Technol. 303, 122956.
  42. Liu, X.Q., Tang, R.Z., 2017. Biological responses to nanomaterials: understanding nano-bio effects on cell behaviors. Drug Delivery. 24(2), 1-15.
  43. Macrelli, S., Galbe, M., Wallberg, O., 2014. Effects of production and market factors on ethanol profitability for an integrated first and second generation ethanol plant using the whole sugarcane as feedstock. Biotechnol. Biofuels. 7, 26.
  44. Mahata, C., Ray, S., Das, D., 2020. Optimization of dark fermentative hydrogen production from organic wastes using acidogenic mixed consortia. Energy Convers. Manage. 219, 113047.
  45. Mahmoodi-Eshkaftaki, M., Mockaitis, G., Rafiee, M.R., 2022. Dynamic optimization of volatile fatty acids to enrich biohydrogen production using a deep learning neural network. Biomass Convers. Biorefinery. 1-12.
  46. Monroy, I., Guevara-López, E., Buitrón, G., 2018. Biohydrogen production by batch indoor and outdoor photo-fermentation with an immobilized consortium: a process model with Neural Networks. Biochem. Eng. J. 135, 1-10.
  47. Monroy, I., Guevara-López, E., Buitrón, G., 2016. A mechanistic model supported by data-based classification models for batch hydrogen production with an immobilized photo-bacteria consortium. Int. J. Hydrogen Energy. 41(48), 22802-22811.
  48. Moreno Cárdenas, E.L., Zapata-Zapata, A.D., Kim, D., 2020. Modeling Dark Fermentation of Coffee Mucilage Wastes for Hydrogen Production: artificial neural network model vs. fuzzy logic model. Energies 13(7), 1663.
  49. Mullai, P., Yogeswari, M.K., Sridevi, K., Ronald Ross, P., 2013. Artificial neural network (ANN) modeling for hydrogen production in a continuous anaerobic sludge blanket filter (ASBF). Singap. J. Sci. Res. 5(1), 1-7.
  50. Nagarajan, D., Lee, D.J., Kondo, A., Chang, J.S., 2017. Recent insights into biohydrogen production by microalgae-from biophotolysis to dark fermentation. Bioresour. Technol. 227, 373-387.
  51. Nasr, N., Hafez, H., El Naggar, M.H., Nakhla, G., 2013. Application of artificial neural networks for modeling of biohydrogen production. Int. J. Hydrogen Energy. 38(8), 3189-3195.
  52. Nath, K., Das, D., 2011. Modeling and optimization of fermentative hydrogen production. Bioresour. Technol. 102(18), 8569-8581.
  53. Nikhil, B.O., Visa, A., Lin, C.Y., Puhakka, J., Yli-Harja, O., 2008. An artificial neural network based model for predicting H2 production rates in a sucrose based bioreactor system. World Acad. Sci. Eng. Technol. 37, 20-25.
  54. Panagiotopoulos, I.A., Bakker, R.R., Budde, M.A.W., de Vrije, T., Claassen, P.A.M., Koukios, E.G., 2009. Fermentative hydrogen production from pretreated biomass: a comparative study. Bioresour. Technol. 100(24), 6331-6338.
  55. Pandey, A.K., Park, J., Ko, J., Joo, H.H., Raj, T., Singh, L.K., Singh, N., Kim, S.H., 2023. Machine learning in fermentative biohydrogen production: advantages, challenges, and applications. Bioresour. Technol. 370, 128502.
  56. Periyasamy, K., Santhalembi, L., Mortha, G., Aurousseau, M., Boyer, A., Subramanian, S., 2018. Bioconversion of lignocellulosic biomass to fermentable sugars by immobilized magnetic cellulolytic enzyme cocktails. Langmuir. 34(22), 6546-6555.
  57. Phanduang, O., Lunprom, S., Salakkam, A., Liao, Q., Reungsang, A., 2019. Improvement in energy recovery from Chlorella biomass by integrated dark-photo biohydrogen production and dark fermentation-anaerobic digestion processes. Int. J. Hydrogen Energy. 44(43), 23899-23911.
  58. Powell, A.S.B., Swartz, J.R., Dunn, A.R., Spormann, A.M., 2012. Activity and oxygen sensitivity of [FeFe] hydrogenases. Stanford University.
  59. Prakasham, R.S., Sathish, T., Brahmaiah, P., 2011. Imperative role of neural networks coupled genetic algorithm on optimization of biohydrogen yield. Int. J. Hydrogen Energy. 36, 4332-4339.
  60. Rachman, M.A., Furutani, Y., Nakashimada, Y., Kakizono, T., Nishio, N., 1997. Enhanced hydrogen production in altered mixed acid fermentation of glucose by Enterobacter aerogenes. J. Ferment. Bioeng. 83(4), 358-363.
  61. Rai, M., dos Santos, J.C., Soler, M.F., Marcelino, P.R.F., Brumano, L.P., Ingle, A.P., Gaikwad, S., Gade, A., da Silva, S.S., 2016. Strategic role of nanotechnology for production of bioethanol and biodiesel. Nanotechnol. Rev. 5(2), 231-250.
  62. Rai, M., Ingle, A.P., Pandit, R., Paralikar, P., Biswas, J.K., da Silva, S.S., 2019. Emerging role of nanobiocatalysts in hydrolysis of lignocellulosic biomass leading to sustainable bioethanol production. Catal. Rev. 61(1), 1-26.
  63. Rasha M.A., 2012. Optimisation of immobilised cellulase onto carbon nanotubes using response surface methodology. Int. J. Phys. Sci. 7, 841-849.
  64. Rodríguez-Hernández, C.F., Musso, M., Kyndt, E., Cascallar, E., 2021. Artificial neural networks in academic performance prediction: systematic implementation and predictor evaluation. Comput. Educ.: Artif. Intell. 2, 100018.
  65. Rosales-Colunga, L.M., García, R.G., De León Rodríguez, A., 2010. Estimation of hydrogen production in genetically modified coli fermentations using an artificial neural network. Int. J. Hydrogen Energy. 35(24), 13186-13192.
  66. Roy, S., Banerjee, D., Dutta, M., Das, D., 2015. Metabolically redirected biohydrogen pathway integrated with biomethanation for improved gaseous energy recovery. Fuel. 158, 471-478.
  67. Safdar Hossain, S.K., Sadiq Ali, S., Cheng, C.K., Ayodele, B.V., 2022. Performance analysis and modeling of bio-hydrogen recovery from agro-industrial wastewater . Front. Energy Res. 10.
  68. Salakkam, A., Sittijunda, S., Mamimin, C., Phanduang, O., Reungsang, A., 2021. Valorization of microalgal biomass for biohydrogen generation: a review. Bioresour. Technol. 322, 124533.
  69. Saratale, G.D., Chen, S.D., Lo, Y.C., Saratale, R.G., Chang, J.S., 2008. Outlook of biohydrogen production from lignocellulosic feedstock using dark fermentation-a review. J. Sci. Ind. Res. (India). 67.
  70. Saravanan, A., Deivayanai, V.C., Senthil Kumar, P., Rangasamy, G., Varjani, S., 2022. CO2 bio-mitigation using genetically modified algae and biofuel production towards a carbon net-zero society. Bioresour. Technol. 363, 127982.
  71. Sewsynker, Y., Kana, E.B.G., Lateef, A., 2015. Modelling of biohydrogen generation in microbial electrolysis cells (MECs) using a committee of artificial neural networks (ANNs). Biotechnol. Biotechnol. Equip. 29(6), 1208-1215.
  72. Shanmugam, S., Ngo, H.H., Wu, Y.R., 2020. Advanced CRISPR/Cas-based genome editing tools for microbial biofuels production: a review. Renewable Energy. 149, 1107-1119.
  73. Shanmugam, S., Sun, C., Chen, Z., Wu, Y.R., 2019. Enhanced bioconversion of hemicellulosic biomass by microbial consortium for biobutanol production with bioaugmentation strategy. Bioresour. Technol. 279, 149-155.
  74. Shanmugam, S., Sun, C., Zeng, X., Wu, Y.R., 2018. High-efficient production of biobutanol by a novel Clostridium sp. strain WST with uncontrolled pH strategy. Bioresour. Technol. 256, 543-547.
  75. Sherpa, K.C., Ghangrekar, M.M., Banerjee, R., 2018. A green and sustainable approach on statistical optimization of laccase mediated delignification of sugarcane tops for enhanced saccharification. Environ. Manage. 217, 700-709.
  76. Show, K.Y., Yan, Y., Ling, M., Ye, G., Li, T., Lee, D.J., 2018. Hydrogen production from algal biomass-advances, challenges and prospects. Bioresour. Technol. 257, 290-300.
  77. Show, K.Y., Lee, D.J., Tay, J.H., Lin, C.Y., Chang, J.S., 2012. Biohydrogen production: current perspectives and the way forward. Int. J. Hydrogen Energy. 37(20), 15616-15631.
  78. Shuttleworth, P.S., De bruyn, M., Parker, H.L., Hunt, A.J., Budarin, V.L., Matharu, A.S., Clark, J.H., 2014. Applications of nanoparticles in biomass conversion to chemicals and fuels. Green Chem. 16, 573-584.
  79. Sivagurunathan, P., Kumar, G., Bakonyi, P., Kim, S.H., Kobayashi, T., Xu, K.Q., Lakner, G., Tóth, G., Nemestóthy, N., Bélafi-Bakó, K., 2016. A critical review on issues and overcoming strategies for the enhancement of dark fermentative hydrogen production in continuous systems. Int. J. Hydrogen Energy. 41(6), 3820-3836.
  80. Sridevi, K., Sivaraman, E., Mullai, P., 2014. Back propagation neural network modelling of biodegradation and fermentative biohydrogen production using distillery wastewater in a hybrid upflow anaerobic sludge blanket reactor. Bioresour. Technol. 165, 233-240.
  81. Srivastava, N., Rawat, R., Sharma, R., Oberoi, H.S., Srivastava, M., Singh, J., 2014. Effect of nickel-cobaltite nanoparticles on production and thermostability of cellulases from newly isolated thermotolerant Aspergillus fumigatus NS (Class: Eurotiomycetes). Appl. Biochem. Biotechnol. 174, 1092-1103.
  82. Srivastava, N., Srivastava, M., Mishra, P.K., Ramteke, P.W., 2016. Application of ZnO nanoparticles for improving the thermal and pH stability of crude cellulase obtained from Aspergillus fumigatus Front. Microbiol. 7. 514.
  83. Srivastava, R.K., Shetti, N.P., Reddy, K.R., Aminabhavi, T.M., 2020. Biofuels, biodiesel and biohydrogen production using bioprocesses. a review. Environ. Chem. Lett. 18, 1049-1072.
  84. Sung, S., Bazylinski, D., Raskin, L., 2003. Biohydrogen production from renewable organic wastes.
  85. Sydney, E.B., Duarte, E.R., Burgos, W.J.M., de Carvalho, J.C., Larroche, C., Soccol, C.R., 2020. Development of short chain fatty acid-based artificial neuron network tools applied to biohydrogen production. Int. J. Hydrogen Energy. 45(8), 5175-5181.
  86. Taheri, E., Amin, M.M., Fatehizadeh, A., Rezakazemi, M., Aminabhavi, T.M., 2021. Artificial intelligence modeling to predict transmembrane pressure in anaerobic membrane bioreactor-sequencing batch reactor during biohydrogen production. J. Environ. Manage. 292, 112759.
  87. Touloupakis, E., Torzillo, G., 2019. Chapter 14 - Photobiological hydrogen production, in: Calise, F., D'Accadia, M.D., Santarelli, M., Lanzini, A., Ferrero, D. (Eds.), Solar Hydrogen Production. Academic Press, pp. 511-525.
  88. Varanasi, J.L., Veerubhotla, R., Pandit, S., Das, D., 2019. Chapter 5.7 - Biohydrogen Production Using Microbial Electrolysis Cell: Recent Advances and Future Prospects, in: Mohan, S.V., Varjani, S., Pandey, A. (Eds.), Microbial Electrochemical Technology, Biomass, Biofuels and Biochemicals. Elsevier, pp. 843-869.
  89. Vasantharaj, S., Sathiyavimal, S., Senthilkumar, P., LewisOscar, F., Pugazhendhi, A., 2019. Biosynthesis of iron oxide nanoparticles using leaf extract of Ruellia tuberosa: antimicrobial properties and their applications in photocatalytic degradation. J. Photochem. Photobiol., B. 192, 74-82.
  90. Vassilev, S.V., Vassileva, C.G., 2016. Composition, properties and challenges of algae biomass for biofuel application: an overview. Fuel. 181, 1-33.
  91. Wang, A.J., Cao, G.L., Liu, W.Z., 2012. Biohydrogen production from anaerobic fermentation. Adv. Biotechnol. China III: Biofuels Bioenergy. 128, 143-163.
  92. Wang, Y., Tang, M., Ling, J., Wang, Y., Liu, Y., Jin, H., He, J., Sun, Y., 2021. Modeling biohydrogen production using different data driven approaches. Int. J. Hydrogen Energy. 46(58), 29822-29833.
  93. Whiteman, J.K., Gueguim Kana, E.B., 2014. Comparative assessment of the artificial neural network and response surface modelling efficiencies for biohydrogen production on sugar cane molasses. BioEnergy Res. 7, 295-305.
  94. Yang, G., Wang, J., 2018. Improving mechanisms of biohydrogen production from grass using zero-valent iron nanoparticles. Bioresour. Technol. 266, 413-420.
  95. Yogeswari, M.K., Dharmalingam, K., Mullai, P., 2019. Implementation of artificial neural network model for continuous hydrogen production using confectionery wastewater. Environ. Manage. 252, 109684.
  96. Yokoi, H., Mori, S., Hirose, J., Hayashi, S., Takasaki, Y., 1998. H2 production from starch by a mixed culture of Clostridium butyricum and Rhodobacter sp. M[h]19. Biotechnol. Lett. 20, 895-899.
  97. Zhang, L., Zhang, L., Li, D., 2015. Enhanced dark fermentative hydrogen production by zero-valent iron activated carbon micro-electrolysis. Int. J. Hydrogen Energy. 40(36), 12201-12208.
  98. Zhang, Y., Shen, J., 2007. Enhancement effect of gold nanoparticles on biohydrogen production from artificial wastewater. Int. J. Hydrogen Energy. 32(1), 17-23.
  99. Zhao, J., Song, W., Cheng, J., Liu, M., Zhang, C., Cen, K., 2017. Improvement of fermentative hydrogen production using genetically modified Enterobacter aerogenes. Int. J. Hydrogen Energy. 42(6), 3676-3681.