Intelligent Delignification: Leveraging Explainable AIfor Ozone Transport Modeling and Optimization

Abstract A biomass is mainly composed of cellulose, hemicellulose and lignin where lignin is almost one third of the amount of biomass. Lignin is removed from the materials matrix because its presence does not allow reagents to react with cellulose and hemicellulose for use in any biofuel industry. Several techniques are used to remove lignin from the biomass matrix where ozonation is the most novel, emerging and ecofriendly technique. This study presents the feasibility of employing machine learning models in the initial stages of ozonation-based lignin removal from lignocellulosic matrices. Another step was to train and evaluate several machine learning models using the experimental data. Pycaret was used to compare 19 different machine learning models. The extra trees regressor successfully forecasted the delignification. The SHapley Additive eXplanation is used to compute the contribution of features. The results provide light on the extraction process and might pave the way for creating more effective and selective ozonation-based methods of lignin removal, this has important consequences for the development of biofuels, chemicals, and materials. The results of this work should inspire more researchers into the use of machine learning for delignification. Predictive models may be more easily developed with the help of machine learning when used in chemical engineering. To bridge the gap in delignification experimental data and result prediction using artificial intelligence. Machine learning predictions of complicated physical processes are crucial to chemical engineering applications. This approach can be modified for use in future studies to address more delignification problems in chemical engineering.