Online prediction of the enzymatic hydrolysis efficiency of crop straw

Abstract

The extent of removal of lignin and hemicellulose are crucial indicators for evaluating the efficiency of enzymatic hydrolysis of crop straw. Numerous factors influence these two indices. Establishing a quantitative model that correlates these factors with hydrolysis efficiency is essential, as it can guide efficient hydrolysis. In this study, a predictive method for enzymatic hydrolysis efficiency in crop straw was proposed using Grey relational analysis (GRA), Kernel principal component analysis (KPCA), and a least squares support vector machine (LSSVM). The authors collected a dataset from actual production data and developed an efficiency predictive model using GRA for variable selection, KPCA for dimensionality reduction, and LSSVM for model training. This model allows for the direct estimation of the final enzymatic hydrolysis efficiency based on production condition variables, which can include enzyme amount, temperatures, pH, time, agitation, and straw dimensions. Extensive experimental testing validated the effectiveness of the proposed method, resulting in minimal errors, a high degree of fit, and exceptional performance. The methodology described in this study can serve as a foundation for optimising the design of efficient enzymatic hydrolysis production processes for crop straw. Additionally, it offers valuable soft measurements to support efficient control of the enzymatic hydrolysis process.