Faculty Publications
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Item Synthesis and Characterization of Biochar Obtained from Microwave-Assisted Copyrolysis of Torrefied Sawdust and Polystyrene(American Chemical Society, 2024) Potnuri, R.; Sankar Rao, C.This study focuses on copyrolyzing pretreated sawdust and polystyrene utilizing microwave-assisted pyrolysis (MAP) with equal mixing to synthesize and characterize biochar. Graphite was used as a susceptor to facilitate precise pyrolysis temperature control. Potassium hydroxide (KOH) powder serves as a catalyst, influencing the char yields and properties. Torrefied raw sawdust at various temperatures (125–175 °C) enhances biochar yields (24–29 wt %). The feedstocks sawdust and polystyrene are characterized by elemental, proximate, and TGA examinations. Furthermore, comprehensive surface, crystallographic, FTIR, and SEM-EDX analyses are performed on microwave copyrolyzed biochar. The developments in BET surface area during copyrolysis show changes concerning pretreatment temperatures: 125 °C (5.6 m2/g) < 150 °C (6.8 m2/g) < 175 °C (8.6 m2/g). Functional groups connected to the alcohols’ O–H bend and C–O stretching vibrations are detected in the biochar samples through FTIR analysis. Sharp peaks with 2θ values between 33.2° and 36.2° appear in the XRD scan of biochar, indicating the presence of crystalline components in the sample. The EDX results demonstrated that the components of biochar included Mg, C, O, and Ca, indicating that it could have plenty of advantageous applications. The study highlights the obstruction of sawdust char’s porous structures by polystyrene, hindering volatile emissions and leading to increased heating rates. These findings underscore the unique contributions of this method to biochar production. © 2024 American Chemical SocietyItem Predictive modeling of product yields in microwave-assisted co-pyrolysis of biomass and plastic with enhanced interpretability using explainable AI approaches(Elsevier B.V., 2025) Rajpurohit, N.S.; Kamani, P.K.; Lenka, M.; Sankar Rao, C.Microwave-assisted co-pyrolysis of biomass and plastic offers a transformative approach to converting waste into valuable resources such as bio-oil, biochar, and biogas, while simultaneously addressing critical environmental challenges associated with plastic disposal. This research employs explainable AI methodologies to enhance the prediction and analysis of product yields in biomass-plastic co-pyrolysis. Advanced machine learning techniques, including Decision Tree, Random Forest, Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks, were utilized to model yield predictions effectively. The models were fine-tuned through hyper-parameter optimization, achieving high accuracy levels. The study emphasizes the scientific importance of integrating explainable AI with pyrolysis processes to optimize waste-to-resource recovery, contributing significantly to sustainable waste management and circular economy initiatives. Among these, the XGBoost model demonstrated superior performance, achieving R² values of 0.91 for biochar yield, 0.92 for bio-oil yield, and 0.82 for biogas yield on testing sets. To enhance model interpretability, SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDPs) were utilized to assess feature importance and examine parameter influences on yield outcomes, offering valuable insights into process optimization and control. Volatile matter and fixed carbon were key predictors for biochar yield, while moisture content and pyrolysis temperature were significant for predicting bio-oil and biogas yields. This study highlights the potential of explainable AI models in advancing sustainable and efficient bio-product recovery from waste materials. © 2025 Elsevier B.V.Item Kinetic analysis and machine learning insights in the production of biochar from Artocarpus heterophyllus (jackfruit) through pyrolysis(Elsevier Ltd, 2025) Tiwari, A.; Sankar Rao, C.; Jammula, K.; Balasubramanian, P.; Chinthala, M.According to International Energy Agency (IEA) Task 40, biomass contributes approximately 10 % of global energy production. This includes waste from agriculture and forestry, generating around 140 billion tons of biomass each year—posing a major challenge for efficient management and disposal. The Food and Agriculture Organization (FAO) reports that global jackfruit production reached 3.7 million tons between 2015 and 2017, while 2.96 million tons of bioenergy feedstock were produced in 2018. Utilizing jackfruit waste as a renewable bioenergy source not only adds economic value to agricultural residues but also helps reduce overall waste generation. The bark of the jackfruit tree (Artocarpus heterophyllus (AHB)) possesses considerable economic importance and exhibits an enormous distribution throughout several regions in Asia. This study involves the production of biochar from AHB biomass through fast pyrolysis at temperatures between 400 and 600 °C. The biochar produced has a carbon content of 66.69 wt% and a calorific value of 27.15 MJ/kg, respectively, which have similar properties to coal. The kinetic analysis of biomass employed three distinct models (OFW, KAS, and TANG) to determine the activation energy. The current study employed machine learning (ML) models to forecast the mass loss of biomass during pyrolysis, which is challenging because of the intricate characteristics of biomass and the extensive range of operating circumstances. Temperature and heating rate were used as input data, while mass loss was the desired output, to train a variety of machine learning models, including ensemble learning, support vector regression, Gaussian process regression, and neural network models. Among these models, the Gaussian process regression model showed superior performance compared to others, achieving a perfect R2 of 1 and minimal errors on both the validation and test sets, making it the best model to predict mass loss of biomass. © 2025 Elsevier LtdItem 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.