Kumar Sharma Amit, Kumar Ghodke Praveen, Goyal Nishu, Nethaji S, Chen Wei-Hsin
Department of Chemistry, Applied Sciences Cluster, Centre for Alternate and Renewable Energy Research, R&D, University of Petroleum & Energy Studies (UPES), School of Engineering, Energy Acres Building, Bidholi, Dehradun 248007, Uttarakhand, India.
Department of Chemical Engineering, National Institute of Technology Calicut, Kozhikode 673601, Kerala, India.
Bioresour Technol. 2022 Nov;364:128076. doi: 10.1016/j.biortech.2022.128076. Epub 2022 Oct 7.
Agricultural waste biomass has shown great potential to deliver green energy produced by biochemical and thermochemical conversion processes to mitigate future energy crises. Biohydrogen has become more interested in carbon-free and high-energy dense fuels among different biofuels. However, it is challenging to develop models based on experience or theory for precise predictions due to the complexity of biohydrogen production systems and the limitations of human perception. Recent advancements in machine learning (ML) may open up new possibilities. For this reason, this critical study offers a thorough understanding of ML's use in biohydrogen production. The most recent developments in ML-assisted biohydrogen technologies, including biochemical and thermochemical processes, are examined in depth. This review paper also discusses the prediction of biohydrogen production from agricultural waste. Finally, the techno-economic and scientific obstacles to ML application in agriculture waste biomass-based biohydrogen production are summarized.
农业废弃物生物质已显示出巨大潜力，可通过生化和热化学转化过程产生绿色能源，以缓解未来的能源危机。在不同生物燃料中，生物氢已成为对无碳且能量密度高的燃料更感兴趣的研究对象。然而，由于生物氢生产系统的复杂性和人类认知的局限性，基于经验或理论开发精确预测模型具有挑战性。机器学习（ML）的最新进展可能会开辟新的可能性。因此，这项关键研究全面介绍了ML在生物氢生产中的应用。深入研究了ML辅助生物氢技术的最新发展，包括生化和热化学过程。本文还讨论了利用农业废弃物生产生物氢的预测。最后，总结了ML应用于农业废弃物生物质基生物氢生产的技术经济和科学障碍。
