Applying machine learning and genetic algorithms accelerated for optimizing ethanol production

被引：1

作者：

Yang, Xu ^{[1
]}

Chen, Nianhua ^{[1
]}

Yu, Hui ^{[1
]}

Liu, Xinyue ^{[1
]}

Feng, Yujie ^{[2
]}

Xing, Defeng ^{[2
]}

Tian, Yushi ^{[1
]}

机构：

[1] School of Resource and Environment, Northeast Agriculture University, Harbin,150030, China

[2] State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, No.73 Huanghe Road, Nangang District, Harbin,150090, China

来源：

Science of the Total Environment | 2024年 / 955卷

关键词：

D O I：

10.1016/j.scitotenv.2024.177027

中图分类号：

学科分类号：

摘要：

Corn straws can produce bioethanol via simultaneous saccharification and co-fermentation (SSCF). However, identifying optimal combinations of operating parameters from numerous possibilities through a cost-effective strategy to improve SSCF efficiency and yield remains challenging. The eXtreme Gradient Boost (XGB) and deep neural network (DNN) models were constructed to accurately predict ethanol yield from only five input variables, achieving >83 % accuracy. Subsequently, the XGB and the DNN models were merged with the genetic algorithm (GA) as the new optimization strategies. Experimental validation showed that the new strategy optimize the efficiency and yield of the SSCF ethanol production system quickly and accurately. Moreover, the potential optimization mechanism was investigated through the comprehensive interpretability analysis for XGB and the microbial ecology analysis. Enzyme Solution Volume (61.7 %) dominated, followed by time (12.9 %), substrate concentration (10.4 %), temperature (7.7 %), and inoculum volume (7.3 %). This efficient and accurate algorithm design strategy can significantly reduce the time required to optimize biochemical systems. © 2024 Elsevier B.V.

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