Smart Sensor-Integrated Catalyst Systems for Real-Time Industrial Process Optimization

Thesis Abstract

This study addresses the persistent inefficiencies and off-spec byproducts in high-temperature catalytic processes by advancing smart sensor-integrated catalyst systems that enable real-time optimization of industrial operations. The central aim is to design, deploy, and evaluate an integrated platform that couples in situ sensor networks with adaptive catalysts and a closed-loop control algorithm to minimize energy consumption, maximize selectivity, and reduce emissions. Specific objectives include (1) developing robust, high-temperature sensors (temperature, pressure, gas composition, and surface species) capable of long-term operation in refinery and chemical processing environments; (2) engineering catalyst supports with embedded nanoscale sensors and actuators to enable localized, rapid feedback-controlled adjustments in active sites; (3) formulating a data-driven control framework that integrates first-principles kinetics with machine learning models to predict process shifts and optimize operating set-points in real time; (4) validating the platform in pilot-scale reactors under representative industrial conditions; and (5) assessing economic and environmental impacts through techno-economic and life cycle analysis. A mixed-methods approach is employed. The methodology combines experimental development with numerical modeling. The research design proceeds in three phases sensor-catalyst integration development, laboratory-scale pilot testing, and industrial-scale feasibility assessment. The population includes commercially relevant catalysts for steam reforming, hydrocracking, and selective oxidation, with sample sizes of three catalyst formulations per process. Data collection instruments comprise advanced spectroscopic probes (Raman, FTIR, UV-Vis), electrochemical sensors, fiber-optic and microelectromechanical systems (MEMS) temperature and pressure sensors, inline gas analyzers (tunable diode laser absorption spectroscopy), and high-speed inline chromatographic analysis (GC-MS). The instrument suite is complemented by reactor data logging for temperature, pressure, flow, and conversion metrics. Validity and reliability are ensured through calibration against standard references, repeat measurements (n = 5 replicates per condition), and cross-validation of sensor readings against bench-top analytical results. Data analysis employs a hierarchically integrated framework. Descriptive statistics summarize operational regimes, while multivariate regression and transient-state ANOVA assess sensor-catalyst response under varying feedstocks and temperatures. Process data are modeled using a hybrid kinetic-dynamic model that fuses Arrhenius-based rates with machine learning surrogates to enable real-time predictions of conversion and selectivity. Model specification includes a control-oriented state-space representation, with optimization solved via model predictive control (MPC). The theoretical underpinning draws on materials science perspectives of catalyst resilience and sensing-enabled control theory, referencing the diffusion-reaction-advection framework and the Cybenko theorem-based justification for neural network generalization in process modeling. Ethical considerations concerning industrial data confidentiality and safety protocols in pilot and industrial settings are observed. Expected findings indicate that embedded sensor-enabled catalysts will exhibit faster stabilization of product distributions after feedstock perturbations, with reductions in energy consumption by up to 12–18% and emissions by 10–25%, compared with conventional control schemes. The integrated MPC framework is anticipated to achieve near-optimal operating points for multiple objectives, with improved reactor productivity and reduced catalyst deactivation rates. Sensitivity analyses are expected to reveal key sensor modalities and catalyst features driving performance gains, informing design rules for scalable deployment. The study anticipates identifying practical thresholds for sensor durability, data latency, and communication reliability that influence control performance under industrial noise. The study contributes to knowledge by bridging the gap between catalyst engineering and real-time process optimization through sensorized catalysts and data-driven control. It advances a modular, scalable framework that can be adapted across hydrocarbon processing and specialty chemical synthesis, offering a blueprint for industry partners seeking to realize autonomous, resilient operations. Policy-relevant insights include guidelines for sensor certification, data governance, and safety standards in automated reactors. The main conclusion is that co-design of sensor networks with catalyst architectures, coupled with predictive control, delivers tangible improvements in efficiency and sustainability. Recommendations emphasize extending pilot deployments to varied refinery configurations, refining sensor materials for extreme environments, and pursuing collaboration with industry to develop standardized interoperable interfaces and certification pathways for smart catalyst systems.