Online-learning-aided optimization and interpretation of sugar production from oil palm mesocarp fibers with analytics for industrial applications

Abstract

Conversion of oil palm mesocarp fibers (MF) into fermentable sugar through catalytic pretreatment coupled with enzymatic hydrolysis is a promising solution for biomass valorization. However, lack of understanding of the complicated conversion process and optimization of critical reaction conditions limits the efficacy of the sugar production (SP). In this study, we aim to develop an online framework incorporating orthogonal experimental design and machine learning (ML) algorithm to optimize important conditions and identify their interaction impacts on overall sugar yield from MF. The results show that both solid reduction (SR) and sugar conversion (SC) increase to 73.85% and 51.91% from ML-based optimization with relative errors of 20.58%±0.70% and 7.50%±3.68% from experimental validation, respectively, after two rounds of online learning. Model-based interpretation suggests that substrate and catalyst concentrations are two negatively related conditions with both SR and SC, while the solid concentration has a piecewise linear folded relationship. Moreover, potential implications on environments and economy of the presented efficiency (51.91%) of overall sugar yield were evaluated based on the amount of MF generated in Malaysia. The ML-optimized conversion strategy can save about 70 million tons of sugar cane and achieve a profit of US$ 1,450 million annually with a low operation cost (40 US$/ton MF conversion). It provides insights that help us to identify important conditions for improving conversion efficiency and promoting future industrialization.