Computational Insights into Catalytic Pyrolysis: Refining Molecular Composition Estimates Using Kovats Retention Index and Molecular Similarities

Abstract

Catalytic pyrolysis presents a promising avenue for mitigating plastic waste accumulation by converting it into valuable products. In this study, we investigate the application of computational methods integrating molecular similarities and the Kovats retention index to enhance the accuracy of qualitative analysis in catalytic pyrolysis processes. Utilizing gas-chromatography data and high-level measurement results, molecular compositions of pyrolysis products are determined and the consistency of molecular composition across various experimental conditions is evaluated. Despite encountering challenges such as algorithm failures due to high computational costs, our analysis reveals significant insights into the molecular composition of pyrolysis products. Through the utilization of molecular similarity methods, the potential to refine the estimation of molecular compositions is also demonstrated, particularly in scenarios in which retention index database accuracy is uncertain. Our findings underscore the importance of further refining computational methods and formulating additional constraints based on high-level measurements to enhance the accuracy of molecular composition estimates.