Faculty Publications
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Item 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 LtdItem 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 LtdItem The effect of torrefaction temperature and catalyst loading in Microwave-Assisted in-situ catalytic Co-Pyrolysis of torrefied biomass and plastic wastes(Elsevier Ltd, 2022) Ramesh, R.; Suriapparao, D.V.; Sankar Rao, C.S.; Sridevi, V.; Kumar, A.; Shah, M.In the current study, the effect of torrefaction temperatures (125–175 °C) and catalyst quantity (5–15 g) on co-pyrolysis of torrefied sawdust (TSD) and polystyrene (PS) are investigated to obtain value-added products. The role of torrefaction in co-pyrolysis of TSD: PS was analyzed to understand the product yields, synergy, and energy consumption. As the torrefaction temperature increases, oil yield (48.3–59.6 wt%) and char yield (24.3–29 wt%) increase while gas yield (27.4–11.4 wt%) decreases. Catalytic co-pyrolysis showed a significant level of synergy when compared to non-catalytic co-pyrolysis. For the conversion (%), a positive synergy maximum (-2.6) exists at a torrefaction temperature of 175 °C and 15 g of KOH catalyst. To develop the model, polynomial regression-based machine learning was used to predict pyrolysis product yields and energy usage variables. The developed models showed significant prediction accuracy (R2 > 0.98), suggesting the experimental values and the predicted values matched well. © 2022 Elsevier LtdItem 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.Item 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 Two-step synthesis of biochar using torrefaction and microwave-assisted pyrolysis: Understanding the effects of torrefaction temperature and catalyst loading(Elsevier B.V., 2023) Ramesh, P.; Sankar Rao, C.S.; Surya, D.V.; Sridevi, V.; Kulkarni, A.The study focused on synthesizing the biochar from sawdust using torrefaction followed by pyrolysis. Sawdust was torrefied at different temperatures (125 °C, 150 °C, and 175 °C) using a conventional hot air oven. The obtained torrefied biochar was subjected to Microwave-assisted pyrolysis at a power of 300 W for 10 min. Graphite was used as a susceptor, and KOH was used as a catalyst. The maximum biochar product yield varied from 24 to 48 wt% and increased with torrefaction temperature. The average heating rates ranged from 54.5 to 74.6 °C/min. At 10 g of KOH, higher heating rates were obtained. The pyrolysis index analyzed varied between 97.5 and 111.5 and decreased with the increase in torrefaction temperature. The obtained biochar was analyzed using SEM, BET, XRD, FTIR, ICP-OES, and Raman spectroscopy. Porous structure formation enhanced, and the concentrations of Ca, Al, and Fe decreased with the increase in torrefaction temperature. © 2023 Elsevier B.V.Item Effective electronic waste valorization via microwave-assisted pyrolysis: investigation of graphite susceptor and feedstock quantity on pyrolysis using experimental and polynomial regression techniques(Springer, 2024) Mistry, C.; Surya, D.V.; Ramesh, R.; Basak, T.; Kumar, P.S.; Sankar Rao, C.S.; Gautam, R.; Sridhar, P.; Choksi, H.; Remya, N.Waste printed circuit board (WPCB) was subjected to microwave-assisted pyrolysis (MAP) to investigate the energy and pyrolysis products. In MAP, pyrolysis experiments were conducted, and the effects of WPCB to graphite mass ratio on three-phase product yields and their compositions were analyzed. In addition, the role of the initial WPCB mass (10, 55, and 100 g) and susceptor loading (2, 22, and 38 g) on the quality of product yield was also evaluated. By using design of experiments, the effects of graphite susceptor addition and WPCB feedstock quantity was investigated. A significant liquid yield of 38.2 wt.% was achieved at 38 g of graphite and 100 g of WPCB. Several other operating parameters, including average heating rate, pyrolysis time, microwave energy consumption, specific microwave power used, and product yields, were optimized for the MAP of WPCB. Pyrolysis index (PI) was calculated at the blending of fixed quantity WPCB (100 g) and various graphite quantities in the following order: 2 g (21) > 20 g (20.4) > 38 g (19.5). The PI improved by increasing the WPCB quantity (10, 55, and 100 g) with a fixed quantity of graphite. This work proposes the product formation and new reaction pathways of the condensable compounds. GC–MS of the liquid fraction from the MAP of WPCBs without susceptor resulted in the generation of phenolic with 46.1% relative composition. The addition of graphite susceptor aided in the formation of phenolic and the relative composition of phenolics was found to be 83.6%. The area percent of phenol increased from 42.8% (without susceptor) to 78.6% (with susceptor). Without a susceptor, cyclopentadiene derivative was observed in a very high composition (~ 31 area %). © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.Item 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