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

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 (C-phe) and glucose yield (C-GK) in corn stover hydrolysate before microbial fermentation by integrating dilute acid pretreatment and enzymatic hydrolysis. Six processing parameters including inorganic acid concentration (C-lA), pretreatment temperature (T), residence time (t), solid-to-liquid ratio (R-SL), kinds of inorganic acids (k(IA)), and enzyme loading dosage (E) were used as input variables. The C-phe and C-Glc 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 C-phe (R-2 =0.904) and CGlC (R-2 = 0.906). Additionally, the relative importance of six input variables on C-phe and C-Glc was firstly calculated by the Carson equation with net weight matrixes. The results indicated that C-IA had strong effects (22%-23%) on C-phe or C-GK, 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.

Automated summary

This study successfully predicted the content of phenolic compounds and glucose yield in corn stover hydrolysate using an artificial neural network model, providing new insights for the sustainable production of biofuels and chemicals from biomass.