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 Synergistic effects and product yields in microwave-assisted in-situ co-pyrolysis of rice straw and paraffin wax(Institution of Chemical Engineers, 2024) Hamzah, H.T.; Sridevi, V.; Surya, D.V.; Ramesh, P.; Sankar Rao, C.; Palla, S.; Abdullah, T.A.Microwave-assisted pyrolysis is one of the most efficient methods for solid waste management. This study employed microwave-assisted catalytic co-pyrolysis to convert Paraffin wax (PW) and rice straw (RS) into valuable char, gas, and oil products. KOH and graphite were used as the catalyst and susceptor, respectively. The RS and PW blend served as the feedstock (with a blend ratio of 0–10 g). The yields of co-pyrolysis at different blending ratios of RS: PW exhibited variations in char content (ranging from 9.8% to 22.6% by wt.), oil production (ranging from 34.1% to 76.9% by wt.), and gas formation (ranging from 13.2% to 47.5% by wt.). The effects of the RS: PW ratio on the average heating rate, feedstock conversion, and product yields were also investigated. Analyses were performed to assess the synergistic impacts on product yields, average heating rates, and conversion factors. Notably, co-pyrolysis synergy led to increased oil and char production. Furthermore, we conducted FTIR analysis on the oil and char produced through the catalytic co-pyrolysis of RS: PW. In conjunction with co-pyrolysis synergy, the catalyst facilitated the formation of amides, alkenes, aliphatic compounds, and aromatic compounds. © 2023 The Institution of Chemical EngineersItem Exploring and understanding the microwave-assisted pyrolysis of waste lignocellulose biomass using gradient boosting regression machine learning model(Elsevier Ltd, 2024) Sinha, S.; Sankar Rao, C.; Kumar, A.; Venkata Surya, D.; Basak, T.The production of bio-oil is a complex process influenced by various parameters. Optimizing these parameters can significantly enhance bio-oil yield, thus improving process efficiency. This study aims to develop a predictive model for bio-oil yield using the Gradient Boosting Regression (GBR) technique. It also seeks to identify the key factors affecting bio-oil yield and determine the optimal conditions for maximizing production. The GBR model was constructed using data collected from the literature. The model's performance was evaluated based on its determination coefficients for training and testing datasets. Optimization studies were conducted to identify the best conditions for bio-oil production. The GBR model demonstrated high precision, with determination coefficients of 0.983 and 0.913 for the training and testing datasets, respectively, indicating its effectiveness in predicting bio-oil yield. The optimal conditions for maximizing bio-oil yield were identified as 20 min of pyrolysis time, a temperature of 771 °C, and 524W of microwave power. The two-way PDP analysis provided valuable insights into the interactive effects of temperature with other factors, enhancing the understanding of the dynamics of the bio-oil production process. This study not only identifies the most impactful variables for bio-oil yield but also offers critical guidance for optimizing the production process. © 2024 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.Item Predicting synergistic effects on biofuel production from microalgae (Spirulina)/Tire Co-pyrolysis using ensemble machine learning(Elsevier B.V., 2025) Sridevi, V.; Al-Asadi, M.; Adnan Abdullah, T.; Nhat, T.; Sankar Rao, C.; Talib Hamzah, H.; Le, P.-C.This study investigates the synergistic effects of microwave-assisted catalytic co-pyrolysis (MACCP) of microalgae and waste tires (WT) under varying parameters such as catalyst weight, microwave power, and susceptor quantity. Optimal reaction conditions yielded a high-quality bio-oil with a maximum yield of 50.46 wt% with low water content, significantly reducing microwave energy consumption from 810 to 540 kJ. The co-pyrolysis of WT and microalgae enhanced denitrogenation and deoxygenation, improving the quality of the resulting bio-oil. Gas chromatography-mass spectrometry (GC-MS) analysis of bio-oil identified an increase in the complex composition of mono- and polyaromatic hydrocarbons and a decrease in oxygenated compounds. An ensemble machine learning approach has been employed to model and predict outcomes, achieving R2 values between 0.7 and 0.98. The models with the best predicted accuracy were Extreme Gradient Boosting (XGB) and Extra Trees (ET), both of which achieved an R2 of 0.98. The models were rigorously validated using the Leave-One-Out Cross-Validation technique, ensuring robust predictions with minimal bias by training on all but one observation iteratively and testing on the excluded data point. The work highlights the possible use of co-pyrolyzing microalgae and WT for sustainable, high-quality bio-oil production with lower energy consumption. It shows that machine learning can optimize MACCP procedures. © 2025 The Energy Institute