文献类型：期刊文献

英文题名：Dynamic hybrid modeling of fuel ethanol fermentation process by integrating biomass concentration XGBoost model and kinetic parameter artificial neural network model into mechanism model

作者：Li, Xinzhe[1];Dong, Yufeng[1];Chang, Lu[2];Chen, Lifan[2];Wang, Guan[2];Zhuang, Yingping[2];Yan, Xuefeng[1,3]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, State Key Lab Bioreactor Engn, Shanghai 200237, Peoples R China;[3]POB 293,MeiLong Rd 130, Shanghai, Peoples R China

年份：2023

卷号：205

起止页码：574

外文期刊名：RENEWABLE ENERGY

收录：;EI(收录号：20230713582038);WOS:【SCI-EXPANDED(收录号:WOS:000932605100001)】；

基金：Funding: This work was supported by National Key Research and Development Program of China [grant number 2021YFC2101100] ; National Natural Science Foundation of China [grant number 21878081] ; and Shanghai Rising -Star Program [grant number 21QA1402400] .

语种：英文

外文关键词：Fuel ethanol fermentation process; Hybrid model; Mechanism model; Extreme gradient boosting; Artificial neural network

摘要：Fuel ethanol has drawn extensive attention as renewable energy. However, modeling the fuel ethanol batch fermentation process is still a critical task. The unstructured kinetic model (UKM) is often utilized to model the process, but it encounters two problems due to the large differences in initial glucose concentrations. First, the UKM has poor predictions of the yeast growth, which is a crucial production index. Second, the kinetic pa-rameters of the UKM are time-varying because of the changing environmental conditions. The constant manually set kinetic parameters affect the prediction accuracy. To tackle the problems, we propose a dynamic hybrid model of the fuel ethanol fermentation process. First, a biomass concentration prediction model based on extreme gradient boosting is developed. It predicts the values of biomass concentrations and mycelium growth rate as supplementary mechanism knowledge. Then, we present an artificial neural network-based model to determine the time-varying kinetic parameters. Our model can accurately predict the time series of biomass, ethanol, and glucose concentrations, with RMSEs reaching 0.3323, 1.9295, and 3.0540. Experimental results show that the dynamic hybrid model performs with satisfactory accuracy in modeling the fuel ethanol fermentation process.