Kinetic Modeling and Multiobjective Optimization of Ibuprofen Synthesis Using Machine Learning.

ABSTRACT: This study presents a comprehensive application of integrated machine learning tools for modeling and optimizing the ibuprofen synthesis process. Initially, a database of 39,460 input combinations is created using chemical reaction theory and validated with experimental data. The CatBoost meta-model, optimized by the snow ablation optimizer, outperforms conventional algorithms in predicting reaction time, conversion rate, and production cost. Importance analyses through SHAP values identify critical input variables, notably, the concentration of the catalyst precursor (L2PdCl2), hydrogen ions (H+), and water (H2O), validating known catalytic principles and providing quantitative parameter guidance through data-driven analysis. Multiobjective optimization using NSGA-II generates a Pareto fr

SUBMITTER: Xiang L 

PROVIDER: S-EPMC12368723 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature