Smart Catalysis via AI-Driven Reaction Optimization and Real-Time Spectroscopy

Thesis Abstract

The study addresses the critical challenge of achieving sustainable and selective catalytic processes in industrial chemistry by integrating artificial intelligence-driven reaction optimization with real-time spectroscopy to accelerate discovery, scale-up, and control. Despite advances in machine learning and in-line analytical techniques, there remains a gap in seamlessly coupling predictive models with real-time spectroscopic feedback to dynamically steer catalytic reactions toward optimal activity, selectivity, and energy efficiency. The aim is to develop a governance framework and experimental platform that synergizes AI optimization with operando spectroscopy to enable autonomous catalyst screening, parameter tuning, and process control. Specific objectives include (1) constructing a multi-modal data pipeline that fuses in situ Raman, FTIR, and NIR spectra with reaction condition telemetry (temperature, pressure, concentration) for supervised and unsupervised learning; (2) developing AI models for reaction optimization that predict yield, selectivity, and catalyst life using ensemble learning and transfer learning across catalyst families (zeolites, metal-organic frameworks, and supported metal catalysts); (3) establishing a real-time feedback loop that translates model outputs into actionable control interventions such as temperature ramps, feed-rate adjustments, and activation energies; (4) validating the approach on benchmark gas- and liquid-phase reactions commonly used in fine-chemical synthesis and green chemistry demonstrations; and (5) assessing economic and environmental impacts through life cycle and risk analyses. The methodology employs a mixed-methods, iterative design. The research design combines experimental screening with data-driven modeling and process simulation. The population comprises catalytic reactions performed in a continuously stirred tank reactor and a microreactor platform equipped with in-line spectroscopic probes (Raman, FTIR, NIR) and a compact gas chromatograph with mass spectrometric detection. A sample of 120 reaction runs will be generated across three catalyst classes, with 10–12 replicates per class to capture variability. Data collection instruments include in-line spectrometers (Raman and FTIR), inline UV-Vis for plasmonic catalysts, real-time calorimetry, online GC-MS for product quantification, and reactor telemetry (temperature, pressure, flow rates). Instrument calibration and validation will be performed following ISO/IEC 17025-compatible protocols. The data-driven component integrates (i) supervised learning for yield and selectivity prediction using random forest, gradient boosting, and neural networks; (ii) unsupervised clustering to identify operando state regions corresponding to favorable reaction pathways; and (iii) reinforcement learning to optimize operational parameters under safety and throughput constraints. Model validation will involve k-fold cross-validation, hold-out test sets, and external validation on unseen catalyst-reaction combinations. Feature engineering will incorporate spectral deconvolution, time-resolved spectral fingerprints, and kinetic descriptors. Analytical techniques include regression analyses (multiple linear and non-linear), ANOVA for comparing catalyst performance groups, multivariate curve resolution for spectral data, and Bayesian optimization for parameter tuning. Theoretical grounding will leverage concepts from heterogeneous catalysis, operando spectroscopy, and control theory, with relevant theories including the Le Chatelier framework for reaction equilibria under dynamic conditions and the Theory of Product Selectivity under competitive adsorption. A conceptual model will map operando spectral features to reaction kinetics and catalyst deactivation pathways, enabling interpretable AI-driven decisions. Ethical considerations address data integrity, reproducibility, and safety in autonomous experimentation. Expected findings include (i) robust AI-driven predictive models that accurately forecast yield and selectivity with mean absolute error within 3–6 percentage points for target reactions; (ii) demonstrable improvements in process energy efficiency (up to 15% reduction in heat duty) and catalyst lifetime (20–25% longer operational cycles) through real-time optimization; (iii) validated operando spectral markers corresponding to key reaction intermediates and active sites that generalize across catalyst classes; and (iv) a transferable framework for AI-augmented catalysis that integrates spectroscopic feedback into automatic control. The study contributes to knowledge by delivering a reproducible, scalable methodology for intelligent catalysis that bridges AI, spectroscopy, and process engineering; it offers a formalized data pipeline, model architectures, and decision rules applicable to diverse catalytic systems. The main conclusion anticipates that AI-driven reaction optimization, grounded in real-time spectroscopic data, can achieve superior catalytic performance with enhanced sustainability, while recommendations emphasize standardizing data-sharing protocols, expanding spectral libraries, and extending the framework to flow chemistry and industrial reactor networks.