Faculty Publications
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Item Advances in Computational Fluid Dynamics Modeling for Biomass Pyrolysis: A Review(Multidisciplinary Digital Publishing Institute (MDPI), 2023) Kulkarni, A.; Mishra, G.; Palla, S.; Ramesh, P.; Surya, D.V.; Basak, T.Pyrolysis, a process for extracting valuable chemicals from waste materials, leverages computational fluid dynamics (CFD) to optimize reactor parameters, thereby enhancing product quality and process efficiency. This review aims to understand the application of CFD in pyrolysis. Initially, the need for pyrolysis and its role in biomass valorization are discussed, and this is followed by an elaboration of the fundamentals of CFD studies in terms of their application to the pyrolysis process. The various CFD simulations and models used to understand product formation are also explained. Pyrolysis is conducted using both conventional and microwave-assisted pyrolysis platforms. Hence, the reaction kinetics, governing model equations, and laws are discussed in the conventional pyrolysis section. In the microwave-assisted pyrolysis section, the importance of wavelength, penetration depth, and microwave conversion efficiencies on the CFD are discussed. This review provides valuable insights to academic researchers on the application of CFD in pyrolysis systems. The modeling of pyrolysis by computational fluid dynamics (CFD) is a complex process due to the implementation of multiple reaction kinetics and physics, high computational cost, and reactor design. These challenges in the modeling of the pyrolysis process are discussed in this paper. Significant solutions that have been used to overcome the challenges are also provided with potential areas of research and development in the future of CFD in pyrolysis. © 2023 by the authors.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