Faculty Publications
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Item 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 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