Smart Catalytic Synthesis via AI-Driven Process Optimization in Industrial Chemistry

Thesis Abstract

In the face of rising energy costs, stringent emission constraints, and the growing demand for sustainable chemical production, the inefficiencies of conventional catalytic synthesis processes constrain scalability and economic viability. This study addresses the challenge of achieving robust, high-selectivity catalytic synthesis by integrating artificial intelligence (AI) with real-time process optimization to enhance catalytic performance, yield, and environmental footprint in industrial chemistry settings. The aim is to develop a data-driven framework that combines AI-enabled predictive modeling, adaptive control, and mechanistic insight to optimize catalyst design, reaction conditions, and process parameters in tandem. Specific objectives include (1) constructing a multi-fidelity digital twin of a representative catalytic reactor with integrated spectral, kinetic, and process data; (2) developing and validating machine learning models (including gradient boosting, neural networks, and Gaussian processes) for real-time prediction of conversion, selectivity, and catalyst deactivation; (3) implementing reinforcement learning-based control strategies to optimize reaction pathways under dynamic feedstock and disturbance scenarios; (4) elucidating the relationships between catalyst structure–activity–stability using explainable AI (XAI) techniques and density functional theory (DFT) informed descriptors; and (5) assessing techno-economic and life-cycle implications of AI-driven optimization versus conventional operation. The methodology comprises a mixed-methods design anchored in a pragmatic research paradigm. The population includes industrially relevant heterogeneous catalysts and fixed-bed/reactor configurations at pilot to semi-industrial scale. A purposive sampling approach selects three catalytic systems representative of hydrogenation, hydrocracking, and oxidation processes, each with available process data and operable sensor networks. Data collection combines archival process data (kinetic data, temperature, pressure, flow rates, product distributions), online spectroscopic measurements (in situ Diffuse Reflectance Infrared Fourier Transform Spectroscopy, DRIFTS; Raman spectroscopy), and off-line characterizations (BET surface area, X-ray diffraction, transmission electron microscopy). Instrumentation includes high-frequency process sensors (kHz–Hz sampling), chromatography (GC-MS), and online mass spectrometry for product profiling. Model development uses multi-omics-inspired data fusion to integrate catalytic descriptors, reactor state, and spectral signals. Analytical methods include regression analysis, Gaussian process regression for uncertainty quantification, recurrent and feedforward neural networks for time-series prediction, and adaptive PID/Model Predictive Control (MPC) with reinforcement learning for optimization. DFT calculations provide descriptors tied to active sites and reaction energetics to inform feature engineering and mechanistic interpretation. The theoretical framework combines the Theory of Constraints in process optimization and the Green Chemistry principle on waste minimization, with Explainable AI (XAI) to ensure interpretable model decisions. Model evaluation employs cross-validation, out-of-sample testing, and statistical significance testing (ANOVA, t-tests) to compare AI-driven optimization against baseline operating strategies. Ethical considerations address data stewardship, model transparency, and risk assessment in automated control. Expected findings include (i) improved reactor conversion and selectivity by 5–15% across the three catalytic systems under comparable energy input, (ii) reduced catalyst deactivation rates through predictive maintenance and optimized feed composition, (iii) robust, interpretable AI models with attention mechanisms that map model decisions to catalyst descriptors, (iv) demonstration of a functional digital twin enabling real-time optimization with adaptive learning, and (v) favorable techno-economic metrics showing reduced operating costs and lower global warming potential per unit of product. The study contributes to knowledge by providing a transferable AI-enabled optimization framework that links catalyst design space with process-level control, extending the scope of AI in chemical manufacturing from data-driven tuning to integrated decision-support for catalyst selection, process intensification, and sustainable operation. The conclusion anticipates that AI-driven process optimization will outperform conventional strategies in dynamic industrial environments while maintaining traceability and regulatory compliance. Recommendations include deployment in pilot-scale facilities with robust data governance, scaling strategies for multi-reactor networks, and the development of standardized benchmarks for AI-driven catalytic optimization to facilitate wider adoption across chemical industries.