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Author: search.filters.author.Kumar, A.Subject: search.filters.subject.Product yields

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    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 Ltd
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    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