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

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.

Keywords

• lignocellulosic biomass
• ann model
• pretreatment efficiency
• prediction
• relative importance