Faculty Publications
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Item Effect of dry torrefaction pretreatment of the microwave-assisted catalytic pyrolysis of biomass using the machine learning approach(Elsevier Ltd, 2022) Ramesh, R.; Suriapparao, D.V.; Sankar Rao, C.S.; Sridevi, V.; Kumar, A.This study employs the Leave-One-Out cross-validation approach to build a machine-learning model using polynomial regression to predict pyro product yield through microwave-assisted pyrolysis of sawdust over KOH catalyst and graphite powder a susceptor. The determination of coefficient (R2) validates the developed models. All the developed models achieved a high prediction accuracy with R2 > 0.93, which signifies that the experimental values are in good agreement with the predicted one. The dependence of the catalyst loading and pretreatment temperature on dominating process parameters such as heating rate, pyrolysis temperature, susceptor thermal energy, and pyro products, namely bio-oil, biochar, and biogas, are explored. The yield of biochar is reduced; however, bio-oil and biogas are enhanced as the catalyst loading increased. On the other hand, increasing the temperature of pretreated sawdust decreased bio-oil and biogas yields while increasing biochar yields. Further, microwave conversion efficiency, and susceptor thermal energy increased with increased catalyst quantity and pretreatment temperatures of sawdust. It was observed that the average heating rate was increased by increasing the catalyst quantity while maintaining the same pyrolysis time until pretreatment temperatures of 150 °C were reached, after which the heating rate dropped due to the continuous microwave energy input to the system. © 2022 Elsevier LtdItem Microwave-assisted in-situ catalytic pyrolysis of polystyrene: Analysis of product formation and energy consumption using machine learning approach(Institution of Chemical Engineers, 2022) Terapalli, A.; Kamireddi, D.; Sridevi, V.; Tukarambai, M.; Suriapparao, D.V.; Sankar Rao, C.S.; Gautam, R.; Modi, P.R.Microwave-assisted catalytic pyrolysis is a prominent technology for the production of high-quality fuel intermediates and value-added chemicals from polystyrene waste. The objectives of this study were to understand the role of catalyst (KOH) on polystyrene (PS) pyrolysis. Pyrolysis experiments were conducted using a microwave oven at a power of 450 W and a temperature of 600 °C. Graphite susceptor (10 g) was used to achieve the required pyrolysis conditions. In addition, the design of experiments (DoE) with machine learning (ML) was used to understand the loading of PS (5 g, 27.5 g, and 50 g), and KOH (5 g, 7.5 g, and 10 g). The products including oil, gas, and char were collected in every experiment. The average heating rates achieved were in the range of 30–50 °C/min. The specific microwave power (microwave power per unit mass of feedstock) decreased with an increase in PS amount from 90 to 9 W/g. However, the specific microwave energy (microwave energy per unit mass of feedstock) (27–73 kJ/g) was in line with the average heating rate. The maximum yield of pyrolysis oil was found to be 95 wt%, which was obtained with a PS:KOH ratio of 27.5 g: 7.5 g. The oil yield increased from 80 to 95 wt% when the mass of the catalyst increased from 5 to 7.5 g. On the other hand, the gas yield (3–18 wt%) varied significantly and char yield (1–2 wt%) was not influenced. The yields predicted by ML matched well with the experimental yields. This study demonstrated the potential of KOH as a catalyst for PS pyrolysis technology as the formation of aliphatic hydrocarbons in the oil fraction was significantly promoted. © 2022 The Institution of Chemical EngineersItem Microwave-assisted In-situ catalytic co-pyrolysis of polypropylene and polystyrene mixtures: Response surface methodology analysis using machine learning(Elsevier B.V., 2023) Kamireddi, D.; Terapalli, A.; Sridevi, V.; Tukaram Bai, M.T.; Surya, D.V.; Sankar Rao, C.S.; Jeeru, L.R.Polypropylene (PP) and Polystyrene (PS) are the major plastic fractions found in mixed plastic waste. Hence, the current study was focused to convert PP and PS into useful products via microwave-assisted pyrolysis (MAP). In addition, the understanding of feedstock conversion, product yields, and energy requirements in pyrolysis, co-pyrolysis, and catalytic co-pyrolysis was investigated. Experiments were conducted at a constant microwave power of 450 W till the reaction temperature reached up to 600 °C. When PS pyrolyzed, a heating rate of 56 °C/min resulted in 80 wt% of oil yield. Whereas PP pyrolysis produced 42 wt% of oil at a heating rate of 76 °C/min. In the PP: PS co-pyrolysis, the heating rate was decreased to 52 °C/min by yielding 51 wt% of oil. In catalytic co-pyrolysis of PP: PS with KOH resulted in variation in product yields and heating rate. An increase in PS quantity at a constant mass of PP resulted in the enhancement of oil yields from 58 to 84 wt% and a decrease in gas yields. The specific microwave power in the catalytic co-pyrolysis (7–18 W/g) is lower compared to the non-catalytic case (22–30 W/g). Whereas, the pyrolysis time in non-catalytic pyrolysis (7–11 min) is lower compared to catalytic co-pyrolysis (14–37 min). The addition of a catalyst resulted in a decrease (23–50%) in microwave conversion efficiency than that of the non-catalytic case (60–85%). The difference in predicted and actual result analysis proved co-pyrolysis synergy in product formation and energy consumption. © 2023 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