Abstract The optimization of continuous stirred tank reactors (CSTRs) requires balancing competing objectives: maximizing conversion, ensuring dynamic stability, and maintaining economic viability. This study presents an integrated data-driven framework combining statistical analysis, mechanistic interpretation, and machine learning to systematically address these challenges for reversible reaction systems (A ⇄ B). Using a synthetic dataset of 3,000 simulations spanning realistic operating ranges (feed rate: 0.5–5.0 L/min, residence time: 2.5–100 min, Damköhler number: 0.3–20), we employed multiple regression, ANOVA/MANOVA, and classification algorithms to answer three fundamental questions: (1) What drives reactor performance? (2) Can we predict operational outcomes? (3) How do we optimize across competing objectives? Multiple regression analysis revealed that conversion is primarily controlled by the Damköhler number (standardized β = 0.58, p 0.85) where only 48% were economically viable due to stability risks. This multi-method approach provides both mechanistic understanding and predictive capability, transforming CSTR operation from single-objective optimization to informed multi-objective decision-making. Keywords: CSTR optimization; reversible reactions; statistical analysis; machine learning; multi-objective optimization; stability analysis; process systems engineering
Anfal Rababah (Sun,) studied this question.