Thermal Runaway of Chemical Reactors: An Experimental, Modeling, and Machine-Learning Investigation

Thermal runaway of chemical reactors remains a central challenge in chemical process safety because small imbalances between heat generation and removal can trigger rapid temperature escalation. In this contribution, we combine mechanistic modeling, targeted experiments, and data-driven tools to predict both the onset and intensity of chemical reactor runaway, demonstrated here for batch and fixed-bed systems and formulated to be extensible to other reactor types and reaction networks. Experimentally, we investigate selective methanol oxidation over Pt–Bi/activated carbon in a fixed-bed reactor and measure axial temperature profiles under varied feed temperature and oxygen concentration. A five-reaction pseudohomogeneous plug-flow model, coupled with sensitivity analysis of T max, identifies critical operating thresholds and clarifies how Bi promotion suppresses highly exothermic CO 2 pathways, widening the safe operating window. To generalize prediction beyond single-reaction systems, we formulate data sets of dimensionless groups (γ, B, Da, St, ψ) for batch and flow reactors and introduce a kinetic weighting factor σ n to map multireaction mechanisms onto an effective parameter space. Logistic Regression, Random Forest, and Support Vector models are trained and validated on literature data (acetic anhydride hydrolysis, batch) and our fixed-bed experiments (methanol oxidation), with Random Forest providing the highest accuracy and the lowest miss rate (false negatives) for classifying runaway onset. We propose a continuous, ML-informed criticality metric, η, which aggregates feature importance with proximity to classical critical values to quantify “how close” a system is to runaway. This unified framework links mechanistic insight with fast, reliable prediction, enabling safer operating envelopes, catalyst-informed mitigation strategies, and practical early warning diagnostics for industrial reactors.