Faculty Publications

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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 Ltd
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 Ltd
Prosopis juliflora valorization via microwave-assisted pyrolysis: Optimization of reaction parameters using machine learning analysis
(Elsevier B.V., 2023) Suriapparao, D.V.; Rajasekhar Reddy, B.R.; Sankar Rao, C.S.; Jeeru, L.R.; Kumar, T.H.
Microwave power and pyrolysis temperature are essential parameters in optimizing the bio-oil yield and quality in microwave pyrolysis. This study focused on understanding the interactions between the microwave power/heating rate and pyrolysis temperature in microwave-assisted pyrolysis of Prosopis juliflora. For optimum bio-oil yield, a discrete set of microwave powers (280 W, 420 W, and 560 W) and pyrolysis temperatures (200 °C, 350 °C, and 500 °C) were selected. A central composite design (CCD) was adopted to analyze the effect of microwave power and the pyrolysis temperature on product yields, heating rate, microwave conversion efficiency, and heat losses in pyrolysis. Moreover, the effect of heating rate, reaction time, specific microwave power, specific microwave energy, and conductive heat loss on gas, char, and liquid yields was evaluated using statistical machine learning techniques. Moreover, a new parameter, pyrolysis index, is calculated under different conditions to understand the extent of pyrolysis intensity using pyrolysis time, heating value, feedstock mass and conversion, and microwave energy conversion. The yields of bio-oil, biochar, and gas were 25–40 wt%, 25–35 wt%, and 35–40 wt% at different experimental conditions. Bio-oil consists of a mix of organic compounds with methoxy phenols at high selectivity, and the calorific value of bio-oil was in the range of 26–28 MJ/kg. Carbon number analysis revealed higher presence of C5–C9 compounds. This study shows the role of machine learning in understanding the effect of various parameters effectively and optimizing the experimental conditions accordingly. © 2022 Elsevier B.V.

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