Smart Catalytic Reactors for Sustainable Chemical Synthesis Using AI-Driven Process Control

Thesis Abstract

The global chemical industry faces pressures to improve energy efficiency, reduce emissions, and enhance product quality while expanding the range of feasible chemical syntheses; traditional fixed-parameter reactors often underperform under feedstock variability and evolving process conditions, leading to suboptimal selectivity and higher solvent and energy footprints. This study addresses the need for adaptive, AI-enabled control of catalytic reactors to achieve sustainable synthesis with minimized environmental impact and maximized process efficiency. The aim is to develop and validate a smart catalytic reactor framework that integrates real-time AI-driven process control with advanced sensing, modeling, and decision-making to optimize reaction pathways, heat management, and catalyst utilization under dynamic operating conditions. Specific objectives include (1) designing an integrated sensor network and digital twin of a representative gas- or liquid-phase catalytic reactor, (2) developing machine learning models for real-time prediction of conversion, selectivity, and heat release, (3) formulating a model-predictive control (MPC) strategy incorporating catalyst deactivation, fouling, and feed variability, (4) evaluating environmental and economic performance via life cycle and techno-economic assessments, and (5) validating the framework experimentally using a pilot-scale reactor with a palladium-on-alumina or zeolite catalyst under controlled perturbations. The research adopts a mixed-methods design combining quantitative modeling and experimental validation. The population comprises industrially relevant catalytic reactions with well-characterized kinetics, and a pilot-scale test bed configured to run representative hydrogenation and oxidation reactions under variable feed compositions (binary and ternary feeds). A sample of n=3 to 5 experimental campaigns is planned, each comprising 30–50 reactor runs with deliberate perturbations in temperature, pressure, feed ratio, and catalyst bed aging to reflect real-world disturbances. Data collection instruments include in-situ FTIR and Raman spectroscopy for real-time surface and reactant monitoring, inline GC for product quantification, calorimetry for heat release profiling, and high-resolution flow meters and pressure transducers. The data set will also incorporate historical plant data from collaborating manufacturers for comparative benchmarking. Analytical methods encompass regression analysis and partial least squares (PLS) for initial correlate-and-predict modeling, Gaussian process regression (GPR) for non-linear surrogate modeling of reactor behavior, and model-predictive control (MPC) optimization with a receding horizon. Theoretical underpinnings draw on ISO 50001 energy management principles and the theory of cyber-physical systems, with explicit reference to the Dynamic Systems Theory for stability analysis and to control-theoretic concepts such as robust and adaptive MPC. Expected findings include (i) a digital twin capable of accurately forecasting reactor performance within 5% of experimental data across perturbations, (ii) a robust MPC scheme that reduces energy consumption by 12–18% and improves selectivity by 6–10% relative to baseline operation, (iii) quantified reductions in solvent usage and waste generation through optimized reaction pathways, and (iv) demonstrated resilience to catalyst deactivation and feed variability with acceptable performance degradation over 200–400 hours of continuous operation. The study contributes to knowledge by operationalizing AI-driven process control in smart catalytic reactors, bridging digital twin technology, machine learning-enabled prediction, and model-based optimization within a sustainable chemistry framework, and providing a transferable methodology for scale-up in the chemical industry. The main conclusion is that integrated AI-driven control substantially enhances performance, sustainability metrics, and economic viability of catalytic processes under real-world disturbances. Recommendations include extending the framework to heterogeneous catalysis families with site-specific deactivation dynamics, incorporating reinforcement learning for long-horizon optimization under uncertain markets, and developing standardized benchmarking protocols for digital twin–assisted reactor validation to accelerate industrial adoption.