Development of AI-Driven Catalyst Optimization for Sustainable Chemical Production

Thesis Abstract

The increasing demand for sustainable chemical production necessitates the development of innovative catalyst technologies that optimize process efficiency while minimizing environmental impact. Traditional catalyst design methods are often time-consuming, resource-intensive, and limited in their ability to explore the complex relationships between catalyst composition, structure, and performance. Consequently, this study aims to develop an artificial intelligence (AI)-driven framework for the optimization of catalysts used in industrial chemical processes, with a focus on sustainability metrics such as catalyst lifespan, selectivity, and energy consumption. The specific objectives include modeling catalyst performance using machine learning algorithms, identifying key physicochemical features influencing catalytic activity, and proposing an optimized catalyst design prototype for industrial application. This research adopts a mixed-methods approach combining quantitative data analysis with qualitative insights. The quantitative component involves collecting a dataset of 150 catalyst samples from a chemical manufacturing plant specializing in petrochemicals, along with their associated performance parameters, physicochemical properties, and synthesis conditions. Data are obtained through laboratory analyses, including X-ray diffraction (XRD), scanning electron microscopy (SEM), and Fourier-transform infrared spectroscopy (FTIR), complemented by process performance data supplied by industry partners. The qualitative component involves expert interviews with 20 catalysis specialists to gather insights on current challenges and potential AI integration strategies. The primary analytical methods employed include supervised machine learning algorithms such as random forest regression and support vector machines (SVM) to model the relationship between catalyst features and performance outcomes. Additionally, feature importance analysis is conducted to identify the physicochemical variables most impactful on catalytic efficiency. Model validation uses k-fold cross-validation and performance metrics such as R-squared, mean squared error (MSE), and classification accuracy. Statistical techniques such as analysis of variance (ANOVA) are applied to compare performance across different catalyst categories, while thematic analysis is used for qualitative interview data to elucidate industry perceptions and readiness for AI adoption. Expected findings suggest that AI models can accurately predict catalyst performance with a coefficient of determination (R²) exceeding 0.85, enabling identification of key features such as pore size distribution, metal loading, and surface acidity that significantly influence catalytic activity. The integration of machine learning insights is anticipated to facilitate the development of an optimized catalyst prototype, reducing reaction energy consumption by at least 15% and extending catalyst lifespan by 20% relative to current benchmarks. The study also expects to reveal critical barriers and facilitators for AI integration in catalyst development within the industry, informed by expert perspectives. This research contributes novel knowledge by demonstrating the feasibility and effectiveness of applying advanced AI techniques to catalyst design for sustainable chemical production. It provides a transferable methodological framework that combines experimental data with machine learning models, offering a pathway toward more efficient, cost-effective, and environmentally friendly catalysts. The findings offer actionable insights for industry practitioners, policymakers, and researchers aiming to enhance process sustainability through intelligent catalyst design. The study concludes that AI-driven models can significantly augment traditional catalyst development approaches, fostering innovation and sustainability in the chemical industry. Recommendations include capacity building for AI integration in chemical research entities, development of standardized datasets for catalyst performance, and further exploration of deep learning methods to capture complex nonlinear relationships. Future research should consider expanding the dataset scope across different chemical processes and incorporating real-time process monitoring data to refine predictive accuracy and facilitate an adaptive catalyst development cycle.