Faculty Publications
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Item Role of ZSM5 catalyst and char susceptor on the synthesis of chemicals and hydrocarbons from microwave-assisted in-situ catalytic co-pyrolysis of algae and plastic wastes(Elsevier Ltd, 2022) Suriapparao, D.V.; Tanneru, T.; Rajasekhar Reddy, B.R.; Yerrayya, A.; Bhasuru, B.A.; Pandian, P.; Prakash, S.R.; Sankar Rao, C.; Sridevi, V.; Desinghu, J.The synergetic effect between algae biomass in co-pyrolysis with synthetic plastics (polypropylene (PP), polyethylene (PE), and expanded polystyrene (EPS)) was investigated in this work. Individual feedstock pyrolysis and co-pyrolysis of algae with PP, PE, and EPS were conducted at a constant supply of microwave energy (420 J/s). Pyrolysis char was used as a susceptor in all the experiments. The average heating rate was varied in the range of ∼50–60 °C/min for achieving the final pyrolysis temperature of 600 °C. In catalytic co-pyrolysis, the ZSM-5 catalyst was used for upgrading the physicochemical properties of pyrolysis oil. The use of catalyst promoted the excessive cracking of biomass in co-pyrolysis, leading to higher gas and coke residue comparatively. The viscosity, density, and flash point of oil obtained in catalytic co-pyrolysis were significantly reduced. While the oil obtained from individual pyrolysis of algae is rich in phenolic derivatives, and that of PP, PE has aliphatic hydrocarbons, and EPS has monoaromatic hydrocarbons as major compounds. The synergistic role of plastic and biomass in co-pyrolysis was observed in the formation of products and oil composition. The bio-oil from catalytic co-pyrolysis is composed of aliphatic oxygenates, aliphatic hydrocarbons, cyclic aliphatic hydrocarbons, and phenolics. The chemicals and hydrocarbons present in the oil have a carbon number in the range of C6 to C30. An increase in carbon and hydrogen elemental composition was observed in bio-oil obtained from co-pyrolysis. © 2021 Elsevier LtdItem Exploring machine learning applications in chemical production through valorization of biomass, plastics, and petroleum resources: A comprehensive review(Elsevier B.V., 2024) Mafat, I.H.; Surya, D.V.; Sharma, S.K.; Sankar Rao, C.Machine learning (ML) is a subtype of artificial intelligence that uses a computer's ability to learn from a given set of accessible data. ML is becoming prominent in almost every business, including the domain of chemical engineering, where there have been numerous researches and investigations. This article provides a detailed overview of the use of ML in the production and characterization study of biomass, polymers, and petroleum products. Categories of ML, including classification, regression, and clustering, are also investigated to get a deeper understanding of ML. From this review, it can be concluded that ML has aided in numerous domains, such as the prediction of biomass energy, the stability of crude oil based on NMR spectroscopy, the calculation of gasoline's octane number, the estimation of fuel oil's kinematic viscosity, the classification of waste plastics, and the estimation of drilling efficiency in petroleum reservoirs, among others. Apart from this, ML has also been playing a significant role in the microwave-assisted pyrolysis of biomass, polymers, and petroleum resources. ML substantially influences chemical engineering and is especially useful for enhancing system efficiency and monitoring processes that are difficult to understand manually. Although several obstacles are associated with ML, such as black box behavior, the need for a large amount of data, and the difficulty of understanding the predictions, deploying the model in the future is uncomplicated once the learning program has been trained. © 2024 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.