Optimization and prediction of catalysts for precise synthesis of methyl glycolate from dimethyl oxalate using machine learning coupled with particle swarm optimization algorithm

Abstract

To optimize and predict the catalyst for precise synthesis of methyl glycolate (MG) from dimethyl oxalate, this study proposed a six-step machine learning framework coupled with a particle swarm optimization algorithm. The random forest (RF) model has the highest prediction accuracy after optimizing its hyperparameters. This preferred model has been rigorously validated using experimental data, showcasing a remarkable level of consistency with the observed trends. Then the catalytic performance of the dimethyl oxalate to methyl glycolate (DtMG) process is evaluated by the feature importance analysis and partial dependence plot approaches. It is recommended to operate at lower temperatures and pressures, higher space velocities, and hydrogen-to-ester ratio for MG production. Finally, the RF model coupled with the particle swarm optimization algorithm is employed to predict the optimal catalyst for maximizing MG yield and minimizing cost of the DtMG process, which successfully predicts seven new catalysts with higher yields and lower costs.