A Framework for Green Catalytic Process Intensification in Industry

Thesis Abstract

Industrial catalysis faces mounting pressure to reduce energy intensity, minimize waste, and eliminate hazardous by-products while maintaining production efficiency. This study addresses the gap between theoretical green chemistry principles and their practical integration in large-scale catalytic processes by developing a robust framework for Green Catalytic Process Intensification (GCPI) that harmonizes process design, catalyst engineering, and real-time sustainability assessment. The aim is to formulate a transdisciplinary framework that operationalizes process intensification through green catalysis, enabling scalable, lower-emission, and resource-efficient industrial operations. Specific objectives are (i) to identify and quantify the key drivers and barriers to GCPI adoption in petrochemical and fine-chemical sectors, (ii) to develop a compositional model linking catalyst selection, reactor topology, and energy consumption to environmental performance metrics, (iii) to validate the framework through pilot-scale experiments and retrospective plant data, and (iv) to establish decision-support tools for practitioners incorporating life cycle, technoeconomic, and environmental trade-offs. The research adopts a mixed-methods design anchored in systems thinking and actor-network theory to capture technical and organizational dimensions of GCPI. The population comprises industrial catalytic units in three representative segments (refining, polymer manufacturing, and pharmaceutical intermediates) with a purposive sample of seven plants contributing data from at least 14 distinct catalytic processes. Data collection integrates quantitative plant metrics (conversion, selectivity, space-time yield, energy consumption, emissions, solvent usage) obtained from process historians and on-line sensors, with qualitative inputs from 40 semi-structured interviews of process engineers, safety managers, and sustainability coordinators. Instrumentation includes validated process data acquisition templates, a green metrics checklist, and a catalyst performance logbook. Reliability and validity are ensured through triangulation across measurements, back-calculation checks against mass and energy balances, and peer-reviewed instrument calibration records. A regression-based environmental performance model and a multi-criteria decision analysis (MCDA) framework are employed to analyze trade-offs among yield, energy intensity, waste generation, and capital expenditure. The analytical framework integrates (i) partial least squares structural equation modeling (PLS-SEM) to test hypothesized links between catalyst properties, reactor design, and environmental outcomes, (ii) ANOVA to compare performance across process families, and (iii) a thematic analysis of interview transcripts to elucidate organizational enablers and barriers. The model specification incorporates established theories in technoecological transitions and green chemistry, notably the Theory of Constraints for process optimization and the Ecological Modernisation framework to interpret organizational adaptation, with validation through pilot-scale experiments in a 2 m3 continuously stirred-tank reactor (CSTR) and a micro-packed-bed reactor platform. Expected findings include a quantifiable framework that demonstrates a strong positive association between integrated catalyst design and reactor topology with reductions in energy intensity and solvent use, along with significant improvements in waste minimization when real-time optimization and in-line analytics are deployed. The pilot studies are anticipated to show a minimum 15–25% reduction in total energy consumption and a 20–40% decrease in solvent-related emissions, without compromising product yield or scale-up feasibility. The study is also expected to reveal critical organizational determinants—such as cross-functional collaboration, data governance maturity, and management commitment—that mediate the technical gains of GCPI. The study contributes to knowledge by delivering a replicable GCPI framework that combines catalyst engineering, process intensification strategies, real-time analytics, and sustainability assessment into a single decision-support model suitable for industrial deployment. It advances the theoretical discourse on green catalysis by integrating technoeconomic and life-cycle perspectives within a systems-level model, validated against real-world data, and operationalized through a modular toolkit for practitioners. The main conclusion posits that green catalytic process intensification is maximally effective when catalyst performance optimization is tightly coupled with reactor design and enabled by transparent data ecosystems and cross-disciplinary governance. Policy and practice recommendations include the adoption of standardized green metrics dashboards, investment in digital twins for catalytic plants, and the institutionalization of collaborative innovation platforms that bridge chemistry, process engineering, and sustainability assessment.