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Author: search.filters.author.Sankar Rao, C.S.Subject: search.filters.subject.powder

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    Synthesis of sustainable chemicals from waste tea powder and Polystyrene via Microwave-Assisted in-situ catalytic Co-Pyrolysis: Analysis of pyrolysis using experimental and modeling approaches
    (Elsevier Ltd, 2022) Suriapparao, D.V.; Sridevi, V.; Ramesh, R.; Sankar Rao, C.S.; Tukarambai, M.; Kamireddi, D.; Gautam, R.; Dharaskar, S.A.; Pritam, K.
    In the current study, catalytic co-pyrolysis was performed on waste tea powder (WTP) and polystyrene (PS) wastes to convert them into value-added products using KOH catalyst. The feed mixture influenced the heating rates (17–75 °C/min) and product formation. PS promoted the formation of oil and WTP enhanced the char formation. The maximum oil yield (80 wt%) was obtained at 15 g:5 g, and the maximum char yield (44 wt%) was achieved at 5 g:25 g (PS:WTP). The pyrolysis index (PI) increased with the increase in feedstock quantity. High PI was noticed at 25 g:5 g, and low PI was at 5 g:5 g (PS:WTP). Low energy consumption and low pyrolysis time enhanced the PI value. Significant interactions were noticed during co-pyrolysis. The obtained bio-oil was analyzed using GC–MS and a plausible reaction mechanism is presented. Catalyst and co-pyrolysis synergy promoted the formation of aliphatic and aromatic hydrocarbons by reducing the oxygenated products. © 2022 Elsevier Ltd
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    The role of solvent soaking and pretreatment temperature in microwave-assisted pyrolysis of waste tea powder: Analysis of products, synergy, pyrolysis index, and reaction mechanism
    (Elsevier Ltd, 2022) Talib Hamzah, H.; Sridevi, V.; Seereddi, M.; Suriapparao, D.V.; Ramesh, R.; Sankar Rao, C.S.; Gautam, R.; Kaka, F.; Pritam, K.
    This study focuses on microwave-assisted pyrolysis (MAP) of fresh waste tea powder and torrefied waste tea powder as feedstocks. Solvents including benzene, acetone, and ethanol were used for soaking feedstocks. The feedstock torrefaction temperature (at 150 °C) and solvents soaking enhanced the yields of char (44.2–59.8 wt%) and the oil (39.8–45.3 wt%) in MAP. Co-pyrolysis synergy induced an increase in the yield of gaseous products (4.7–20.1 wt%). The average heating rate varied in the range of 5–25 °C/min. The energy consumption in MAP of torrefied feedstock (1386 KJ) significantly decreased compared to fresh (3114 KJ). The pyrolysis index dramatically varied with the solvent soaking in the following order: ethanol (26.7) > benzene (25.6) > no solvent (10) > acetone (6). It shows that solvent soaking plays an important role in the pyrolysis process. The obtained bio-oil was composed of mono-aromatics, poly-aromatics, and oxygenated compounds. © 2022 Elsevier Ltd
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    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