Advancements in biohydrogen production - a comprehensive review of technologies, lifecycle analysis, and future scope

被引:2
|
作者
Sanghvi, Aarnav Hetan [1 ]
Manjoo, Amarjith [2 ]
Rajput, Prachi [2 ]
Mahajan, Navya [2 ]
Rajamohan, Natarajan [3 ]
Abrar, Iyman [2 ]
机构
[1] Birla Inst Technol & Sci, Dept Elect & Elect Engn, Pilani Hyderabad Campus, Hyderabad 500078, Telangana, India
[2] Birla Inst Technol & Sci, Dept Chem Engn, Pilani Hyderabad Campus, Hyderabad 500078, India
[3] Sohar Univ, Fac Engn, Chem Engn Sect, Sohar 311, Oman
关键词
FERMENTATIVE HYDROGEN-PRODUCTION; SEQUENCING BATCH REACTOR; ARTIFICIAL NEURAL-NETWORKS; STIRRED-TANK REACTOR; OIL MILL EFFLUENT; WASTE-WATER; BIO-HYDROGEN; DARK FERMENTATION; BIOH(2) PRODUCTION; TEQUILA VINASSES;
D O I
10.1039/d4ra06214k
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
The global shift towards sustainable energy sources, necessitated by climate change concerns, has led to a critical review of biohydrogen production (BHP) processes and their potential as a solution to environmental challenges. This review evaluates the efficiency of various reactors used in BHP, focusing on operational parameters such as substrate type, pH, temperature, hydraulic retention time (HRT), and organic loading rate (OLR). The highest yield reported in batch, continuous, and membrane reactors was in the range of 29-40 L H2/L per day at an OLR of 22-120 g/L per day, HRT of 2-3 h and acidic range of 4-6, with the temperature maintained at 37 degrees C. The highest yield achieved was 208.3 L H2/L per day when sugar beet molasses was used as a substrate with Clostridium at an OLR of 850 g COD/L per day, pH of 4.4, and at 8 h HRT. The integration of artificial intelligence (AI) tools, such as artificial neural networks and support vector machines has emerged as a novel approach for optimizing reactor performance and predicting outcomes. These AI models help in identifying key operational parameters and their optimal ranges, thus enhancing the efficiency and reliability of BHP processes. The review also draws attention to the importance of life cycle and techno-economic analyses in assessing the environmental impact and economic viability of BHP, addressing potential challenges like high operating costs and energy demands during scale-up. Future research should focus on developing more efficient and cost-effective BHP systems, integrating advanced AI techniques for real-time optimization, and conducting comprehensive LCA and TEA to ensure sustainable and economically viable biohydrogen production. By addressing these areas, BHP can become a key component of the transition to sustainable energy sources, contributing to the reduction of greenhouse gas emissions and the mitigation of environmental impacts associated with fossil fuel use.
引用
收藏
页码:36868 / 36885
页数:18
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