Faculty Publications

Now showing 1 - 3 of 3

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 Ltd
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.
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

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