Optimization of photo-fermentation bio-hydrogen production from corncob via genetic algorithm optimized neural network and response surface method model
文献类型： 外文期刊
作者： Ai, Fuke 1 ; Hu, Yuan 1 ; Kang, Kang 2 ; Lam, Su Shiung 4 ; Foong, Shin Ying 4 ; Yong, Cheng 6 ; Li, Yameng 1 ; Zhang, Quanguo 1 ; Zhang, Yang 1 ; Zhu, Shengnan 1 ; Lv, Xianchao 1 ; Cheng, Axing 1 ; Zhang, Zhiping 1 ;
作者机构： 1.Henan Agr Univ, Key Lab New Mat & Facil Rural Renewable Energy, MOA China, Zhengzhou 450002, Peoples R China
2.Lakehead Univ, Biorefining Res Inst BRI, Thunder Bay, ON P7B 5E1, Canada
3.Lakehead Univ, Dept Chem Engn, Thunder Bay, ON P7B 5E1, Canada
4.Univ Malaysia Terengganu, Higher Inst Ctr Excellence HICoE, Inst Trop Aquaculture & Fisheries AKUATROP, Kuala Nerus 21030, Terengganu, Malaysia
5.Saveetha Univ, Saveetha Inst Med & Tech Sci SIMATS, Saveetha Med Coll, Ctr Global Hlth Res CGHR, Chennai, India
6.Jiangsu Acad Agr Sci, Inst Agr Resources & Environm, Nanjing 210014, Peoples R China
关键词： Corncob; Enterobacter hormaechei; HAU-M2; Cumulative hydrogen yield; Root mean square error
期刊名称：INTERNATIONAL JOURNAL OF HYDROGEN ENERGY （ 影响因子：8.3； 五年影响因子：7.7 ）
ISSN： 0360-3199
年卷期： 2025 年 138 卷
页码：
收录情况： SCI
摘要： Photosynthetic hydrogen-producing bacteria play a critical role in photo-fermentation bio-hydrogen production (PFHP) and optimizing the operating conditions is essential for improving hydrogen yield. In this study, corncob was used as raw material for fermentation, Enterobacter hormaechei (EH) was combined with the photosynthetic hydrogen-producing bacterial community HAU-M1 to form a new bacterial community, HAU-M2, for hydrogen production via PFHP. Response surface method (RSM) model and a genetic algorithm optimized neural network (GANN) model were used and compared to optimize the operating conditions of the PFHP process. The results showed that the GANN model showed enhanced optimization abilities. Under optimal conditions, the cumulative hydrogen yield was 51.96 mL/g TS. The energy recovery efficiency in the GANN experimental group (3.59 +/- 0.12%) increased by 55% compared to the control group (2.31 +/- 0.13%). This study provides valuable insights and references for the resource utilization of agricultural waste and the clean production of renewable energy.
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