Application of modern approaches to the synthesis of biohydrogen from organic waste |
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| Affiliation: | 1. Department of Mechanical Engineering, Delhi Skill and Entrepreneurship University, New Delhi, 110089, India;2. Energy Institute Bengaluru, Centre of Rajiv Gandhi Institute of Petroleum Technology, Bengaluru, Karnataka, India;3. Department of Mechanical Engineering, College of Engineering, Prince Mohammad Bin Fahd University, Al-Khobar, 31952, Saudi Arabia;4. Zhejiang Provincial Key Laboratory for Subtropical Water Environment and Marine Biological Resources Protection, Wenzhou University, Wenzhou 325035, China;5. Department of Chemical Engineering, Khalifa University, P.O. Box 127788, Abu Dhabi, United Arab Emirates;6. School of Renewable Energy, Maejo University, Chiang Mai Province, Thailand;7. Department of Mechanical Engineering, Faculty of Engineering, Düzce University, Düzce, Türkiye;8. Department of Chemical Engineering and Materials Science, Yuan Ze University, Taoyuan, Taiwan;1. School of Renewable Energy, Maejo University, Chiang Mai, 50290, Thailand;2. Department of Biochemical Science & Technology, National Taiwan University, Taipei, 10617, Taiwan;3. Program in Biotechnology, Maejo University, Chiang Mai, 50290, Thailand;4. Institute of Green Products, Feng Chia University, Taichung, 40724, Taiwan;5. Master''s Program of Green Energy Science and Technology, Taichung, 40724, Taiwan;6. Institute of Atmospheric Pollution Research (IIA), CNR, Italy;1. Energy Institute, Bengaluru, Centre of Rajiv Gandhi Institute of Petroleum Technology, Bangalore, 560064, Karnataka, India;2. Department of Mechanical Engineering, Delhi Skill and Entrepreneurship University, New Delhi, 110089, India;3. Department of Mechanical Engineering, College of Engineering, Prince Mohammad Bin Fahd University, Al Khobar, 31952, Saudi Arabia;4. Department of Mechanical Engineering, Jyothi Engineering College, Thrissur, 679531, Kerala, India;5. Department of Mechanical Engineering, Faculty of Engineering, Düzce University, Düzce, Türkiye;1. Key Laboratory of Clean Energy and Carbon Neutrality of Zhejiang Province, State Key Laboratory of Clean Energy Utilization (Zhejiang University), Hangzhou, 310027, China;2. Shenzhen Energy Environment Co., Ltd., Shenzhen, 518048, China;1. Key Laboratory of Carbonaceous Wastes Processing and Process Intensification of Zhejiang Province, University of Nottingham Ningbo, 315100, China;2. National Engineering Laboratory of Cleaner Hydrometallurgical Production Technology, Institute of Process Engineering, Chinese Academy of Sciences, Beijing, 100190, China;3. College of Biosystems Engineering and Food Science, National-Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment, Zhejiang University, Hangzhou 310058, China;4. Department of Chemistry, University College London (UCL), 20 Gordon Street, London WC1H 0AJ, UK;5. School of Computer Science, University of Nottingham Ningbo, 315100, China;6. Edith Cowan University School of Engineering, 270 Joondalup Drive Joondalup WA 6027 Australia;1. Department of Mechanical Engineering, College of Engineering, Prince Mohammad Bin Fahd University, Al Khobar, 31952, Saudi Arabia;2. Department of Forestry, National Chung Hsing University, Taichung, 402, Taiwan;3. School of Renewable Energy, Maejo University, Chiang Mai, 50290, Thailand;4. Program in Biotechnology, Faculty of Science, Maejo University, Chiang Mai, 50290, Thailand;5. Thailand Chiang Mai Branch Center, APEC Research Center for Advanced Biohydrogen Technology (ACABT), Maejo University, Chiang Mai, 50290, Thailand;6. Faculty of Liberal Arts, Maejo University, Chiang Mai, 50290, Thailand;1. Department of Digital Media Technology, Hangzhou Dianzi University, Hangzhou 310018, China;2. Logistics Service Center, Zhejiang Gongshang University, Hangzhou 310018, China;3. School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou 310018, China;4. Air Conditioning and Refrigeration Techniques Engineering Department, Al-Mustaqbal University College, Babylon 51001, Iraq;5. Institute of Research and Development, Duy Tan University, Da Nang, Viet Nam;6. Advanced Functional Materials & Optoelectronic Laboratory (AFMOL), Department of Physics, Faculty of Science, King Khalid University, P.O. Box 9004, Abha 61413, Saudi Arabia;7. Research Center for Advanced Materials Science (RCAMS), King Khalid University, P.O. Box 9004, Abha 61413, Saudi Arabia;8. Physics Department, Faculty of Science, Zagazig University, Zagazig 44519, Egypt;9. College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325100, China;10. School of Engineering & Technology, Duy Tan University, Da Nang, Viet Nam |
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| Abstract: | Hydrogen production with the use of biological processes and renewable feedstock may be considered an economical and sustainable alternative fuel. The high calorific value and zero emission in the production of biohydrogen make it the best possible source for energy security and environmental sustainability. Solar energy, microorganisms, and feedstock such as organic waste and lignocellulosic biomasses of different feedstock are the only requirements of biohydrogen production along with specific environmental conditions for the growth of microorganisms. Hydrogen is also named as ‘fuel of the future’. This study presents different pathways of biohydrogen production. Because of breakthroughs in R&D, biohydrogen has been elevated to the status of a viable biofuel for the future. However, significant problems such as the cost of preprocessing, oxygen-hypersensitive enzymes, a lack of uniform light illumination for photobiological processes, and other expenses requiring intensification process limits are faced throughout the biohydrogen production process. Despite concerns regarding nanoparticle (NP) toxicity at higher concentrations, proper NP concentrations may improve hydrogen production dramatically by dissolving the substrates for bacterial hydrogen transformation. The data-driven Machine Learning (ML) model allows for quick response approximation for fermentative biohydrogen production while accounting for non-linear interactions between input variables. Scaling up biohydrogen production for future commercial-scale applications requires combining cost-benefit evaluations and life cycle effects with machine learning. |
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| Keywords: | Biohydrogen production Hydrogen generation Machine learning Microbial electrolysis cell Nanotechnology |
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