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Author: search.filters.author.Sridevi, V.Subject: search.filters.subject.Biofuels

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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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    Effect of solvent pre-treatment on microwave assisted pyrolysis of Spirulina (Algal Biomass) and Ficus benghalensis (Lignocellulosic Biomass) for production of biofuels: comparative experimental studies
    (Springer Science and Business Media Deutschland GmbH, 2025) Varma, J.V.; Sridevi, V.; Musalaiah, M.; King, P.; Hamzah, H.T.; Tanneru, H.K.; Ramesh, R.; Malleswari, G.B.
    The study focuses on the comparison of microwave-assisted pyrolysis (MAP) of Spirulina, (algal biomass), and aerial roots of Ficus benghalensis (lignocellulosic biomass) as feedstocks for biofuel production. Solvent ethanol was used to pretreat feedstocks. The experiments were carried out using a microwave power of 450 W, considering both fresh and solvent-pretreated feedstocks. Solvent-pretreated Spirulina demonstrated a high bio-oil yield of 56.1 wt. % and a biochar yield of 13.5 wt. %, whereas for solvent-pretreated Ficus benghalensis, the corresponding yields were 35.4 wt. % and 12.3 wt.%. Both solvent-treated and fresh algal biomass feedstocks showed higher yields than lignocellulose biomass. Pre-treatment of feedstocks showed positive results on microwave energy consumption and pyrolysis index. The average heating values were 27.3 0C/min for pretreated Spirulina and 46.2 0C/min for pretreated Ficus benghalensis. Fourier Transform Infrared (FTIR) characterized the obtained bio-oils and biochar. The FTIR results indicated the presence of distinctive functional groups such as N=C=O, O=C=O, N-O, and S=O in MAP of Spirulina bio-oil, and C=C and C-I stretching in MAP of Ficus benghalensis bio-oil. The FTIR results for biochar were consistent across both feedstocks, showing common functional groups such as C-Cl, C=C, C-H, O-H, C-F, and S=O. However, in the case of Spirulina, an extra functional group, C=N, was also detected. Pre-treatment of microalgal biomass is essential for the maximal recovery of biofuel precursors packed inside the complex microalgal cell wall. It was concluded that pre-treatment is an efficient way to improve the yield and composition of bio-oil with low microwave power and short microwave irradiation time. Efforts are still required to develop an economical and environmentally benign pre-treatment approach to facilitate 100% biomass conversion to added-value products. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
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    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