Faculty Publications
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Item Recent advancements of CFD and heat transfer studies in pyrolysis: A review(Elsevier B.V., 2023) Dadi, V.S.; Sridevi, V.; Tanneru, H.K.; Busigari, R.R.; Ramesh, P.; Kulkarni, A.; Mishra, G.; Basak, T.There is a pressing need to process the solid waste by using pyrolysis technology due to its uniqueness to produce various solid, liquid and gaseous products. However, further understanding of pyrolysis process is needed. Most importantly, the role of computational fluid dynamics (CFD) in pyrolysis is to be thoroughly investigated. In recent times, there has been significant progress in the research works aligned with evaluating the role of CFD in biomass pyrolysis. Hence, the current review manuscript focusses the current state of the art in the application of CFD tools to multi-scale biomass pyrolysis systems. Modeling of fluid and heat transport in conventional pyrolysis reactors, microwave-assisted pyrolysis reactors, and solar-assisted pyrolysis reactors for the conversion of biomass have been critically analyzed. The theoretical basis and the practical applicability of the CFD models to efficiently emulate and predict the overall complexity of pyrolysis process for the multi-scale and multi-phase nature of biomass have been discussed. However, the validity and accuracy of the CFD models needs to be enhanced. In the future directions, the steps for expanding the applicability of these theoretical and computational models have been outlined. This review would provide detailed understanding of CFD role in pyrolysis process conducted in various reactor systems. © 2023 Elsevier B.V.Item Leveraging explainable AI framework for predictive modeling of products of microwave pyrolysis of lignocellulosic biomass using machine learning(Elsevier B.V., 2025) Kale, R.D.; Lenka, M.; Sankar Rao, C.The accurate prediction of biochar, bio-oil, and biogas yields in biomass pyrolysis is critical for optimizing process efficiency and sustainable biofuel production. In this study, machine learning (ML) models were developed using literature-derived data on biomass composition and pyrolysis conditions to predict product yields. A comparative analysis of multiple ML algorithms revealed that Decision Tree and Extra Trees exhibited the highest predictive accuracy, followed by Random Forest, Gradient Boosting Trees, and Extreme Gradient Boosting. LightGBM, Gaussian Process Regression, and CatBoost provided moderate performance, while AdaBoost demonstrated the lowest accuracy. To enhance interpretability, Explainable Artificial Intelligence (XAI) techniques, specifically SHAP analysis, were employed to identify key factors influencing pyrolysis yields. Temperature, ash content, fixed carbon, and moisture content were the dominant parameters governing biochar yield, whereas heating rate, reaction time, and feedstock properties such as carbon and volatile matter content significantly influenced bio-oil production. The gas yield was primarily driven by temperature, with secondary cracking mechanisms enhancing non-condensable gas formation. These insights provide a data-driven foundation for optimizing biomass pyrolysis processes, enabling targeted valorization strategies for lignocellulosic feedstocks. The integration of ML and XAI in this study establishes a transparent and interpretable modeling framework, facilitating informed decision-making for sustainable biofuel production. © 2025 Elsevier B.V.