A hybrid deep learning framework driven by data and reaction mechanism for predicting sustainable glycolic acid production performance

Abstract Selective oxidation at low temperatures without alkali of biomass is a promising and sustainable avenue to manufacture glycolic acid (GA), a biodegradable functional material to protect the environment. However, producing glycolic acid with high selectivity and yield using the traditional research and development approach is time‐consuming and labor‐intensive. To this context, a hybrid deep learning framework driven by data and reaction mechanisms for predicting GA production was proposed, considering the lack of related reaction mechanisms in the machine learning algorithms. The proposed hybrid deep learning framework involves the kinetic reaction mechanism, catalyst properties, and reaction conditions. Results showed that the fully connected residual network exhibited superior performance (average R 2 = 0.98) for the prediction of conversion rate and product yields. Then, the multi‐objective optimization and experimental verification guided the research are carried out. The experimental verification is comparable to the modeling results, with errors of less than 4% for conversion rate and GA yields. The life cycle assessment further identifies that using the optimized operating parameters, the fossil energy demand and greenhouse emissions have decreased by 2.96% and 3.00%, respectively. This work provides new insight and strategy to accelerate the engineered selective oxidation for desired GA production.