Artificial Neural Network Approach for Predicting Enzymatic Hydrolysis of Steam Exploded Pine Wood Chip in Mild Alkaline Pretreatment

Abstract

Lignocellulosic biomass, particularly softwoods such as pine, poses a significant challenge to enzymatic hydrolysis due to its high lignin content and complex structural rigidity. Although the application of steam explosion and alkaline pretreatment has gained widespread popularity for enhancing digestibility, the optimization of process parameters remains a formidable challenge due to the nonlinear interactions among variables. Machine learning is emerging as a promising solution to address these challenges, offering a viable alternative for predictive modeling and process control. In this study, an artificial neural network (ANN) model was developed to predict the enzymatic hydrolysis rate of steam-exploded pine wood subjected to mild alkaline (NaOH) pretreatment. The artificial neural network (ANN) was trained on experimental data encompassing three primary process variables: steam explosion time (1 to 5 min), NaOH concentration (0.5 to 2.0%), and chemical pretreatment time (12 to 24 h). The artificial neural network (ANN) model demonstrated the highest level of accuracy among the models evaluated, including random forest, support vector machine, and extreme gradient boosting. It attained a coefficient of determination (R²) of 0.9805. In conditions that were not optimized (1% NaOH, 24-hour treatment, 5 min steam explosion, without bark), a maximum hydrolysis of 93.9% was obtained.