A Framework for Sustainable Catalytic Reactor Design Using Multi-Scale Modeling

Thesis Abstract

The increasing demand for sustainable chemical processes necessitates the development of innovative catalytic reactor designs that optimize efficiency while minimizing environmental impact. However, current approaches often lack integrated frameworks capable of capturing the complex multi-scale phenomena that influence reactor performance, thereby limiting the realization of truly sustainable catalytic systems. This study aims to develop a comprehensive framework for sustainable catalytic reactor design employing advanced multi-scale modeling techniques, thereby bridging the gap between microscopic catalytic processes and macroscopic reactor operations. The specific objectives include (1) to identify key multi-scale phenomena impacting catalyst performance and reactor sustainability; (2) to formulate a multi-layered modeling framework integrating atomic-scale simulations, mesoscale kinetics, and macro-scale process dynamics; (3) to validate the framework using case studies on catalytic methanol synthesis reactors; and (4) to propose design guidelines rooted in the model for enhancing sustainability metrics such as energy efficiency, waste reduction, and catalyst lifespan. The research adopts a mixed-methods approach, integrating computational modeling with experimental validation. Quantitative data will be collected through atomistic simulations—specifically density functional theory (DFT)—to determine catalytic activity and stability parameters at the molecular level, complemented by mesoscale kinetic Monte Carlo (kMC) simulations to interpret reaction kinetics and transport phenomena. Macro-scale process data will be obtained from pilot-scale reactor experiments, with a sample size of 10 reactors operated under varying thermodynamic and feedstock conditions. Data collection instruments incorporate high-resolution spectroscopy for catalyst characterization, flow meters and temperature sensors for process monitoring, and gas analyzers for quantifying reaction conversions. Analytical techniques such as regression analysis and analysis of variance (ANOVA) will be employed to discern significant factors affecting sustainability outcomes, while sensitivity analysis will explore the robustness of the model across different operational parameters. The core of the methodology involves developing an integrated multi-scale model that combines atomistic simulations, mesoscale kinetics, and process engineering principles based on the Theory of Catalytic Processes. This composite model will be used to simulate and predict the impact of catalyst properties and operational conditions on environmental sustainability and process efficiency, employing finite element analysis (FEA) and system dynamics modeling. Model validation will proceed through comparison with experimental data from the pilot reactors, utilizing metrics such as catalyst deactivation rates, energy consumption, and waste generation. Ethical considerations include ensuring accurate reporting of data, adhering to safety standards in experimental procedures, and maintaining transparency in modeling assumptions. It is anticipated that the framework will reveal critical insights into the scale-dependent phenomena influencing catalytic reactor sustainability, such as the interplay between atomic catalyst properties and macroscopic heat and mass transfer limitations. Key expected findings include the identification of optimal catalyst formulations, operational conditions that minimize waste and energy use, and design parameters that extend catalyst life. The model will demonstrate how multi-scale interactions govern overall reactor performance, providing a predictive tool for sustainable reactor design. It is expected to contribute substantial advancements in the theoretical understanding of catalyst-reactor systems and to serve as a decision-support tool for chemical engineers seeking eco-efficient solutions. The study concludes that incorporating multi-scale modeling into the design process significantly enhances the capacity to develop sustainable catalytic reactors. The research provides a validated framework that integrates molecular insights with process engineering, thereby facilitating more environmentally benign and economically feasible reactor designs. Recommendations include expanding the framework to other catalytic processes, integrating real-time operation data for adaptive control, and developing user-friendly software interfaces for wider application. Future research directions involve exploring the integration of machine learning techniques to further improve model predictive capabilities and to adapt the framework for emerging catalytic technologies, such as nanostructured catalysts and bio-inspired systems.