Smart Sensor Network for Real-Time Catalysis Optimization in Industry 4.0

Thesis Abstract

The rapid digitalization of industrial processes has heightened the demand for real-time optimization of catalytic systems to enhance efficiency, selectivity, and sustainability while reducing energy consumption and emissions. Despite advances in process analytics, real-time decision-making in industrial catalysis remains hampered by limited sensor coverage, delayed data integration, and siloed control architectures. This study addresses these gaps by developing and validating a smart sensor network (SSN) integrated with Industry 4.0 ICT frameworks to enable continuous, data-driven optimization of catalytic reactors. The aim is to design, implement, and evaluate an interconnected sensor network that provides high-fidelity, real-time measurements of key catalytic and process variables, and to deploy a hybrid analytics pipeline that informs model-predictive control (MPC) strategies for on-the-fly optimization of operating conditions. Specific objectives are (i) to identify and deploy a suite of embedded and non-intrusive sensors (temperature, pressure, flow, gas composition, catalyst bed activity indicators via in-situ spectroscopy) across pilot-scale and industrial-scale reactors; (ii) to develop robust data fusion and preprocessing workflows, including calibration, drift correction, and anomaly detection, leveraging techniques such as Kalman filtering and fault-tolerant fusion; (iii) to construct predictive models for conversion, selectivity, and catalyst deactivation using machine learning methods (Gaussian process regression, random forest, and gradient boosting) complemented by first-principles constraints; (iv) to implement an MPC framework that optimizes reaction conditions in real-time under uncertainty, integrating digital twin representations of the reactor and catalyst kinetics; (v) to evaluate system performance through a multi-site validation study, comparing baseline control against SSN-enabled optimization in terms of yield, energy intensity, and emissions. A mixed-methods methodology is adopted. The population comprises industrial catalytic reactors in petrochemical and fine chemical plants, with pilot-scale units used for initial testing (n=3) and one full-scale plant for field validation (n=1). Sensor prototypes will be deployed in phased trials, targeting a sample of 30–50 sensors per site, including spectroscopic sensors (FTIR, NIR) and electrochemical sensors for minor species. Data collection instruments include high-frequency process data loggers, edge gateways, and cloud-based data lakes, complemented by operator interviews and workflow observations for human-in-the-loop aspects. Data analysis follows a two-tier approach quantitative analytics employing regression analysis, support vector regression, and dynamic Bayesian networks to quantify relationships between sensor signals and reaction outcomes; model validation using cross-validation, RMSE, R-squared, and uncertainty quantification; and qualitative insights from semi-structured interviews with process engineers, analyzed through thematic analysis to assess acceptance and integration barriers. Ethical considerations involve ensuring data confidentiality, operator safety, and adherence to industry standards for deployable sensing technologies. Key expected findings include (i) a validated, scalable SSN architecture with demonstrated improvements in real-time process observability and fault detection rates; (ii) predictive models achieving R-squared values above 0.8 for major products and catalyst activity indicators, with credible interval estimates suitable for MPC; (iii) a functional digital twin capable of simulating transient reactor behavior under variable feedstock compositions and temperature perturbations; (iv) an MPC control strategy delivering statistically significant gains in conversion and selectivity (at least 3–5% improvement) and reductions in energy consumption (targeting 8–12%) relative to conventional feedback control, across multiple reactions; and (v) insights into organizational and operational factors influencing adoption of Industry 4.0 enabled catalysis optimization. The study contributes to knowledge by (a) bridging real-time sensing, data analytics, and model-based control within industrial catalysis; (b) advancing methodologies for sensor fusion and uncertainty-aware digital twin construction in hazardous, high-temperature environments; and (c) providing a replicable blueprint for deploying SSNs in diverse chemical processing contexts. The principal conclusion anticipates that integrated SSNs can deliver measurable performance enhancements while enhancing process safety and sustainability. Recommendations include standardizing data standards for cross-site interoperability, developing robust cyber-physical security protocols, and extending the framework to heterogeneous catalytic systems and renewable feedstocks to further decarbonize chemical manufacturing.