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 Understanding of synergy in non-isothermal microwave-assisted in-situ catalytic co-pyrolysis of rice husk and polystyrene waste mixtures(Elsevier Ltd, 2022) Sridevi, V.; Suriapparao, D.V.; Tukarambai, M.; Terapalli, A.; Ramesh, R.; Sankar Rao, C.S.; Gautam, R.; Moorthy, J.V.; Suresh Kumar, C.Rice husk (RH) and polystyrene (PS) wastes were converted into value-added products using microwave-assisted catalytic co-pyrolysis. The graphite susceptor (10 g) along with KOH catalyst (5 g) was mixed with the feedstock to understand the products and energy consumption. RH promoted the char yield (20–34 wt%) and gaseous yields (16–25 wt%) whereas PS enhanced the oil yield (23–70 wt%). Co-pyrolysis synergy induced an increase in gaseous yields (14–53 wt%) due to excessive cracking. The specific microwave energy consumption dramatically decreased in co-pyrolysis (5–22 kJ/g) compared to pyrolysis (56–102 kJ/g). The pyrolysis index increased (17–445) with the increase in feedstock quantity (5–50 g). The obtained oil was composed of monoaromatics (74%) and polyaromatics (18%). The char was rich in carbon content (79.5 wt%) and the gases were composed of CO (24%), H2 (12%), and CH4 (22%). © 2022 Elsevier LtdItem 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 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