Artificial Neural Network Modeling to Predict the Efficiency of Phosphoric Acid-Hydrogen Peroxide Pretreatment of Wheat Straw

Authors

  • Qing Wang College of Grassland Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan 611130, P. R. China; Institute of Ecological and Environmental Sciences, Sichuan Agricultural University, Chengdu, Sichuan 611130, P. R. China; Department of Chemical and Petroleum Engineering, Schulich School of Engineering, the University of Calgary, Calgary T2N 4H9, Canada https://orcid.org/0000-0002-2538-4798
  • Jinxiang Hua Institute of Ecological and Environmental Sciences, Sichuan Agricultural University, Chengdu, Sichuan 611130, P. R. China
  • Jinguang Hu Department of Chemical and Petroleum Engineering, Schulich School of Engineering, the University of Calgary, Calgary T2N 4H9, Canada
  • Li Zhao Institute of Ecological and Environmental Sciences, Sichuan Agricultural University, Chengdu, Sichuan 611130, P. R. China
  • Mei Huang Institute of Ecological and Environmental Sciences, Sichuan Agricultural University, Chengdu, Sichuan 611130, P. R. China
  • Dong Tian Institute of Ecological and Environmental Sciences, Sichuan Agricultural University, Chengdu, Sichuan 611130, P. R. China
  • Yongmei Zeng Institute of Ecological and Environmental Sciences, Sichuan Agricultural University, Chengdu, Sichuan 611130, P. R. China
  • Shihuai Deng Institute of Ecological and Environmental Sciences, Sichuan Agricultural University, Chengdu, Sichuan 611130, P. R. China
  • Fei Shen Institute of Ecological and Environmental Sciences, Sichuan Agricultural University, Chengdu, Sichuan 611130, P. R. China
  • Xinquan Zhang College of Grassland Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan 611130, P. R. China

Keywords:

Lignocellulosic biomass, ANN model, Pretreatment efficiency, Prediction, Relative importance

Abstract

Phosphoric acid-hydrogen peroxide (PHP) pretreatment is an effective method to obtain a cellulose-enriched fraction from biomass. In this study, artificial neural network (ANN) was used to predict PHP pretreatment efficiency of cellulose content (C-C), cellulose recovery (C-Ry), hemicellulose removal (H-Rl), and lignin removal (L-Rl) under various conditions of pretreatment time (t), temperature (T), H3PO4 concentration (Cp), and H2O2 concentration (Ch). The final optimized topology structure of the ANN models had 1 hidden layers with 9 neurons for C-C and 10 neurons for C-Ry, 10 neurons for H-Rl, and 12 neurons for L-Rl. The actual testing data fit the predicted data with R2 values ranging from 0.8070 to 0.9989. The relative importance (RI) revealed that Cp and Ch were significant factors influencing the efficiency of PHP pretreatment with total RI values ranging from 12% to 62.6%. However, their weights for the three components of biomass were different. The value of T dominated hemicellulose removal effectiveness with an RI value of 78.6%, while t did not seem to be a main factor dominating PHP pretreatment efficiency. The results of this study provide insights into the convenient development and optimization of biomass pretreatment from ANN modeling perspectives.

Downloads

Published

2023-11-15

Issue

Vol. 19 No. 1 (2024)

Section

Research Article or Brief Communication