A Sustainable Framework for Optimizing Catalytic Reactor Performance and Emission Reduction

Thesis Abstract

In the context of increasing environmental concerns and stringent regulatory standards, the optimization of catalytic reactors for enhanced performance while minimizing harmful emissions remains a critical challenge in chemical engineering. This study addresses the pressing need for a sustainable framework that integrates performance efficiency with environmental responsibility, aiming to develop strategies that optimize catalyst design, reactor operation, and emission control measures holistically. The primary objective is to formulate a comprehensive, scalable framework for selecting, designing, and operating catalytic reactors that maximize conversion efficiency and longevity, reduce greenhouse gas emissions, and promote sustainable chemical processes. Specifically, the study aims to (1) identify key parameters influencing catalyst performance and emission profiles, (2) develop predictive models for reactor optimization based on these parameters, and (3) validate the framework through experimental and simulation approaches. The research adopts a mixed-methods approach, combining quantitative modeling with qualitative assessment. The quantitative component involves collecting experimental data from a laboratory-scale catalytic reactor system operated under varying conditions, utilizing a sample size of 30 different catalyst formulations and operational parameters. These data are analyzed through multiple linear regression and advanced machine learning techniques, including artificial neural networks, to identify significant predictors of performance and emissions. Quality and reliability of data are ensured through calibration of analytical instruments such as gas chromatography (GC), Fourier-transform infrared spectroscopy (FTIR), and chemometric validation. Complementarily, a Delphi method with a panel of 15 experts in catalysis and environmental engineering is employed to gather qualitative insights and refine the framework. Key expected findings anticipate the identification of optimal operational conditions and catalyst formulations that significantly improve conversion efficiency—by at least 15%—while reducing NOx, SOx, and particulate emissions by a target margin of 20%. The models developed are expected to provide predictive capacity for real-time process adjustments, thus ensuring continuous performance optimization. Additionally, the study hypothesizes that integrating sustainability principles into catalyst selection and reactor operation—guided by the Theory of Sustainable Development and Diffusion of Innovations—can accelerate the adoption of eco-efficient catalytic processes in industrial settings. The study contributes to theoretical knowledge by advancing an integrated, data-driven framework that aligns catalytic reactor optimization with sustainability objectives, bridging gaps in existing literature which predominantly focus on performance or environmental aspects separately. It introduces a novel multi-criteria decision-making model tailored for industrial application, incorporating environmental impact assessments alongside technical efficiency metrics. Practically, the framework offers a strategic tool for engineers and policymakers to design, operate, and regulate catalytic processes with sustainability at the core, facilitating compliance with international environmental standards and promoting green technologies. The main conclusion posits that a scientifically grounded, multidisciplinary approach can substantially enhance catalytic reactor performance while minimizing environmental footprints, thus supporting sustainable industrial transformation. Recommendations include adopting the developed framework across different chemical processes, investing in catalyst innovation aligned with sustainability goals, and integrating real-time analytics for dynamic process management. Future research directions suggested involve scaling the framework to pilot and industrial scales, and exploring its applicability across diverse chemical sectors such as petrochemicals, pharmaceuticals, and waste-to-energy systems to foster broader adoption of environmentally sustainable catalytic technologies.