Faculty Publications
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Item Synthesis of renewable carbon biorefinery products from susceptor enhanced microwave-assisted pyrolysis of agro-residual waste: A review(Institution of Chemical Engineers, 2022) Rajasekhar Reddy, B.R.; Sridevi, V.; Kumar, T.H.; Sankar Rao, C.S.; Palla, V.C.S.; Suriapparao, D.V.; Undi, G.S.Valuable renewable carbon biorefinery products can be obtained by using agro-residual biomass as a feedstock. Bio-oil, gas, and char products can be obtained from Microwave-assisted pyrolysis (MAP) by converting agro-residual waste. In MAP, the process variables like microwave power, temperature, heating rate, raw materials, susceptors, and catalysts play an important role to alter the product spectrum. The temperature, heating rate, and pyrolysis time can be tuned to obtain the desired products during biomass decomposition. The obtained carbonaceous products can be used as intermediated feedstocks to synthesize a variety of end products. Hence, in this review, the application of MAP for the conversion of agro-residual waste is discussed. Special focus is given to the interaction of microwaves with susceptors. This manuscript provides background, current status, progress, and future scope of MAP technology for waste valorization. The objectives of the review are to address (i) The necessity of environmental protection, (ii) The role of biorefinery in the biomass conversion, (iii) The advancements in the MAP for the resource recovery, (iv) The mechanism of heat generation from microwaves, (v) The effects of process parameters, susceptors, and catalysts in MAP, (vi) The interactions of biomass and susceptors during the pyrolysis, (vii) The formation of valuable renewable carbon products and (viii) The future scope and challenges for the integration of MAP in solid waste management. © 2022 The Institution of Chemical EngineersItem Novel strategies for glucose production from biomass using heteropoly acid catalyst(Elsevier Ltd, 2020) Nayak, A.; Pulidindi, I.N.; Sankar Rao, C.S.Bioethanol and direct glucose fuel cells pledged clean energy to the world. Cellulose depolymerization for glucose production has been a successful approach in bioethanol production. Heteropoly acids (HPAs) are strong Brønsted solid acid catalysts for biomass hydrolysis. Keggin type HPAs, namely, Silicotungstic acid (HSiW), Phosphotungstic acid (HPW), and Phosphomolybdic acid (HPMo), were used for the hydrolysis of lignocellulosic biomass to glucose. Five different biomass feedstocks, namely, miscanthus, sugarcane leaves, switchgrass, sunflower seeds, and bamboo leaves, were examined for the feasibility of total reducing sugar (TRS) yield through the composition analysis and catalytic biomass hydrolysis. Sunflower seeds contained the maximum holocellulose with 90.6%, and switchgrass contained the least i.e., 77.63%. Among the five biomass tested, switchgrass resulted in the highest TRS (5.77 wt/dry wt. %) with HPMo catalyst at a catalyst to biomass ratio of 30:100 (wt./wt. %), a reaction temperature of 120 °C for 3 h. The reaction parameters for depolymerization were optimized for all three HPAs, and the optimized conditions were 3 h and 120 °C. HPMo showed maximum TRS yield (5.77 wt/dry wt.%) among the three HPAs at 30:100 catalyst to biomass ratio. However, a catalyst to biomass ratio of 20:100 (wt./wt.%) was economical (5.25 wt/dry wt.%) for commercial application. © 2020 Elsevier LtdItem A review on analysis of biochar produced from microwave-assisted pyrolysis of agricultural waste biomass(Elsevier B.V., 2023) Ramesh, R.; Surya, D.V.; Sankar Rao, C.S.; Yadav, A.; Sridevi, V.; Remya, N.Every year the agricultural product processing industries produce large quantities of agricultural waste biomass (AWB). Whose disposal has become a serious issue concerning solid waste management due to environmental and health issues. Microwave-assisted pyrolysis (MAP) is an intriguing technology for producing valuable products from waste feedstocks. AWB is converted into a valuable product like biochar by using MAP. The conversion of AWB into biochar by MAP is influenced by several factors such as type of feedstock, pyrolysis temperature, residence time, pressure, heating rate, susceptor, particle size, and microwave power. However, no review article is available to understand the role of MAP on biochar production from AWB. The current review focused on understanding the fundamentals of biochar production. It also reviews the challenges in producing biochar process by compatible, acceptable, and sustainable and its future directions to gain economic benefits even at small-scale applications. The generation of biochar from MAP and its uses in agriculture are discussed. The current review would address the knowledge gap and highlight the critical implications in biochar production and applications. © 2023 Elsevier B.V.Item Utilizing support vector regression modeling to predict pyro product yields from microwave-assisted catalytic co-pyrolysis of biomass and waste plastics(Elsevier Ltd, 2023) Ramesh, P.; Sankar Rao, C.S.; Surya, D.V.; Kumar, A.; Basak, T.The rise in plastic waste production has led to the development of co-pyrolysis of waste plastics and biomass as a potential solution. This process converts waste into valuable resources, including chemicals and pollutant-absorbing materials. Accurately predicting product yields is crucial and involves considering feedstock characteristics and pyrolysis conditions. No previous work on machine learning (ML) predicts pyro-products considering catalyst and blend as input features. This study used a support vector machine (SVM) to predict pyro-product yields from microwave-assisted co-pyrolysis of biomass and plastics. SVM models were trained, validated, and then applied to new data. The results showed high predictive accuracy, with R2 values of 0.96, 0.93, and 0.91 for bio-oil, biochar, and biogas, respectively. The SVM model demonstrated strong predictive capabilities, indicating effective generalization ability based on statistical parameters. Additionally, SVM models incorporating all features performed better than those based on 'elementary analysis (EA)' and 'proximate analysis (PA)' alone. The pearson correlation coefficient (PCC) approach assessed the correlation between input features to remove highly correlated variables. The partial dependence analysis reveals the individual effects of influential factors and their interactions in the co-pyrolysis process, highlighting significant features like carbon, hydrogen, ash, volatile matter, and nitrogen content that influence oil, char, and gas yields, thereby providing valuable insights for optimization strategies in co-pyrolysis. © 2023 Elsevier LtdItem A review on the role of various machine learning algorithms in microwave-assisted pyrolysis of lignocellulosic biomass waste(Academic Press, 2024) Mafat, I.H.; Surya, D.V.; Sankar Rao, C.S.; Kandya, A.; Basak, T.The fourth industrial revolution will heavily rely on machine learning (ML). The rationale is that these strategies make various business operations in many sectors easier. ML modeling is the discovery of hidden patterns between multiple process parameters and accurately predicting the test values. ML has provided a wide range of applications in Chemical Engineering. One major application of ML can be found in the microwave-assisted pyrolysis (MAP) of lignocellulose bio-waste. MAP is an energy-efficient technology to obtain high-saturated hydrogen-rich liquid fuels. The main focus of this review study is understanding the utilization of various types of ML algorithms, including supervised and unsupervised techniques in microwave-assisted heating techniques for diverse biomass feedstocks, including waste materials like used tea powder, wood blocks, kraft lignin, and others. In addition to developing effective ML-based models, alternative traditional modeling approaches are also explored. In addition to various thermochemical conversion processes for biomass, MAP is also briefly reviewed with several case studies from the literature. The conventional modeling methodology for biomass pyrolysis with microwave heating is also discussed for comparison with ML-based modeling methodologies. © 2024 Elsevier Ltd