Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
5 results
Search Results
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 Recent advancements of CFD and heat transfer studies in pyrolysis: A review(Elsevier B.V., 2023) Dadi, V.S.; Sridevi, V.; Tanneru, H.K.; Busigari, R.R.; Ramesh, P.; Kulkarni, A.; Mishra, G.; Basak, T.There is a pressing need to process the solid waste by using pyrolysis technology due to its uniqueness to produce various solid, liquid and gaseous products. However, further understanding of pyrolysis process is needed. Most importantly, the role of computational fluid dynamics (CFD) in pyrolysis is to be thoroughly investigated. In recent times, there has been significant progress in the research works aligned with evaluating the role of CFD in biomass pyrolysis. Hence, the current review manuscript focusses the current state of the art in the application of CFD tools to multi-scale biomass pyrolysis systems. Modeling of fluid and heat transport in conventional pyrolysis reactors, microwave-assisted pyrolysis reactors, and solar-assisted pyrolysis reactors for the conversion of biomass have been critically analyzed. The theoretical basis and the practical applicability of the CFD models to efficiently emulate and predict the overall complexity of pyrolysis process for the multi-scale and multi-phase nature of biomass have been discussed. However, the validity and accuracy of the CFD models needs to be enhanced. In the future directions, the steps for expanding the applicability of these theoretical and computational models have been outlined. This review would provide detailed understanding of CFD role in pyrolysis process conducted in various reactor systems. © 2023 Elsevier B.V.Item Exploring and understanding the microwave-assisted pyrolysis of waste lignocellulose biomass using gradient boosting regression machine learning model(Elsevier Ltd, 2024) Sinha, S.; Sankar Rao, C.; Kumar, A.; Venkata Surya, D.; Basak, T.The production of bio-oil is a complex process influenced by various parameters. Optimizing these parameters can significantly enhance bio-oil yield, thus improving process efficiency. This study aims to develop a predictive model for bio-oil yield using the Gradient Boosting Regression (GBR) technique. It also seeks to identify the key factors affecting bio-oil yield and determine the optimal conditions for maximizing production. The GBR model was constructed using data collected from the literature. The model's performance was evaluated based on its determination coefficients for training and testing datasets. Optimization studies were conducted to identify the best conditions for bio-oil production. The GBR model demonstrated high precision, with determination coefficients of 0.983 and 0.913 for the training and testing datasets, respectively, indicating its effectiveness in predicting bio-oil yield. The optimal conditions for maximizing bio-oil yield were identified as 20 min of pyrolysis time, a temperature of 771 °C, and 524W of microwave power. The two-way PDP analysis provided valuable insights into the interactive effects of temperature with other factors, enhancing the understanding of the dynamics of the bio-oil production process. This study not only identifies the most impactful variables for bio-oil yield but also offers critical guidance for optimizing the production process. © 2024 Elsevier LtdItem 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