Prediction of Hydrogen Production Rate in Anaerobic Fermentation Using Grey Relation Analysis and Machine Learning

作     者：Wang, Yifan Wu, Jinghui Tseng, Yu-Yao Zhao, Chunliang Li, Keqing Wang, Xianze Wang, Ming-Hung Lay, Chyi-How Huo, Mingxin 

作者机构：Engineering Research Center of Low-Carbon Treatment Green Development of Polluted Water in Northeast China Ministry of Education Changchun130117 China Science and Technology Innovation Center for Municipal Wastewater Treatment and Water Quality Protection Northeast Normal University Changchun130117 China Key Laboratory of Songliao Aquatic Environment Ministry of Education Jilin Jianzhu University Changchun130118 China Master's Program of Green Energy Science and Technology Feng Chia University Taichung40724 Taiwan Department of Computer Science and Information Engineering National Chung Cheng University Chiayi County Taiwan 

出 版 物：《SSRN》 

年 卷 期：2023年

核心收录：

主　　题：Machine learning 

摘      要：Anaerobic fermentation for hydrogen production has many environmental factors that limit microbial activity, but machine learning has enormous potential in handling the complexity of biological processes. This study explores the potential of machine learning in predicting hydrogen production rates (HPR) from anaerobic fermentation of biomass energy. Grey relation analysis was conducted to determine the correlation between operational parameters and HPR. Five machine learning algorithms, such as decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), and K-nearest neighbor (KNN), were then paired with operating conditions and water quality performance as features and HPR as a label, with mean squared error (MSE) and R2 as evaluation indexes. Butyric acid, oxidation-reduction potential (ORP), and volatile suspended solids (VSS) were found to play crucial roles in hydrogen production from sucrose anaerobic fermentation. XGBoost had the highest accuracy with R2 of 0.91 and MSE of 0.0052. © 2023, The Authors. All rights reserved.