Data-Driven Optimization of Continuous Stirred Tank Reactor Operation: Integration of Statistical Analysis, Stability Classification, and Machine Learning for Reversible Reaction Systems

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 < .001) and reactor volume (β = 0.58, p < .001), with feed rate showing a negative effect (β = –0.51, p < .001). The model achieved R² = 0.684 for conversion and R² = 0.574 for steady-state temperature. Polynomial regression with interaction terms improved prediction (R² = 0.716), revealing significant two-way interactions, particularly between feed rate and feed temperature (β = 2.28, p < .001). Factorial ANOVA on categorized operating variables confirmed the dominant role of Damköhler regime (η² = 0.560, F = 2045.29, p < .001), with significant interactions between residence time and Damköhler regime (η² = 0.003, p = .005). MANOVA demonstrated that operating conditions simultaneously affect all three response variables (conversion, temperature, settling time) with large multivariate effect sizes. Machine learning classification models (detailed in accompanying analyses) successfully predicted operating regime, stability class, and economic viability with high accuracy, enabling real-time decision support. The integrated framework identified optimal operating windows: moderate conditions (Da = 2–8, τ = 15–30 min) achieved conversion of 0.72 ± 0.12 with 88% stability and 79% economic viability, compared to high-conversion conditions (X > 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