Prediction of phenolic compounds and glucose content from dilute inorganic acid pretreatment of lignocellulosic biomass using artificial neural network modeling

Bioresources and Bioprocessing

Yongjiang Shi¹  Lei Gao¹  Fang Xie¹  Muhammad Bilal¹  Zheng Liu¹  Rongling Yang²  Hongzhen Luo³  Mohammad J. Taherzadeh⁴ 

[1] School of Life Science and Food Engineering, Huaiyin Institute of Technology, 1 Meicheng East Road, 223003, Huaian, China;School of Life Science and Food Engineering, Huaiyin Institute of Technology, 1 Meicheng East Road, 223003, Huaian, China;Faculty of Applied Technology, Huaiyin Institute of Technology, 223003, Huaian, China;School of Life Science and Food Engineering, Huaiyin Institute of Technology, 1 Meicheng East Road, 223003, Huaian, China;Jiangsu Provincial Engineering Laboratory for Biomass Conversion and Process Integration, Huaiyin Institute of Technology, 223003, Huaian, China;Swedish Centre for Resource Recovery, University of Borås, 50190, Borås, Sweden;

关键词: Lignocellulosic biomass;    Dilute acid pretreatment;    Enzymatic hydrolysis;    Phenolic compounds;    Artificial neural network;    Modeling;   

DOI  :  10.1186/s40643-021-00488-x

来源: Springer

【 摘 要 】

Dilute inorganic acids hydrolysis is one of the most promising pretreatment strategies with high recovery of fermentable sugars and low cost for sustainable production of biofuels and chemicals from lignocellulosic biomass. The diverse phenolics derived from lignin degradation during pretreatment are the main inhibitors for enzymatic hydrolysis and fermentation. However, the content features of derived phenolics and produced glucose under different conditions are still unclear due to the highly non-linear characteristic of biomass pretreatment. Here, an artificial neural network (ANN) model was developed for simultaneous prediction of the derived phenolic contents (CPhe) and glucose yield (CGlc) in corn stover hydrolysate before microbial fermentation by integrating dilute acid pretreatment and enzymatic hydrolysis. Six processing parameters including inorganic acid concentration (CIA), pretreatment temperature (T), residence time (t), solid-to-liquid ratio (RSL), kinds of inorganic acids (kIA), and enzyme loading dosage (E) were used as input variables. The CPhe and CGlc were set as the two output variables. An optimized topology structure of 6–12-2 in the ANN model was determined by comparing root means square errors, which has a better prediction efficiency for CPhe (R2 = 0.904) and CGlc (R2 = 0.906). Additionally, the relative importance of six input variables on CPhe and CGlc was firstly calculated by the Garson equation with net weight matrixes. The results indicated that CIA had strong effects (22%-23%) on CPhe or CGlc, then followed by E and T. In conclusion, the findings provide new insights into the sustainable development and inverse optimization of biorefinery process from ANN modeling perspectives.Graphical Abstract

【 授权许可】

CC BY