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

Abstract

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_Glc) in corn stover hydrolysate before microbial fermentation by integrating dilute acid pretreatment and enzymatic hydrolysis. Six processing parameters including inorganic acid concentration (C_IA), 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 C_Glc (R ² = 0.906). Additionally, the relative importance of six input variables on C_Phe and C_Glc was firstly calculated by the Garson equation with net weight matrixes. The results indicated that C_IA had strong effects (22%-23%) on C_Phe or C_Glc, 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.

Keywords

Lignocellulosic biomass / Dilute acid pretreatment / Enzymatic hydrolysis / Phenolic compounds / Artificial neural network / Modeling

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Funding

National Natural Science Foundation of China(21808075)

Natural Science Foundation of Jiangsu Province(BK20170459)