Browsing by Author "Chinthala, M."

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    Kinetic analysis and machine learning insights in the production of biochar from Artocarpus heterophyllus (jackfruit) through pyrolysis
    (Elsevier Ltd, 2025) Tiwari, A.; Sankar Rao, C.; Jammula, K.; Balasubramanian, P.; Chinthala, M.
    According to International Energy Agency (IEA) Task 40, biomass contributes approximately 10 % of global energy production. This includes waste from agriculture and forestry, generating around 140 billion tons of biomass each year—posing a major challenge for efficient management and disposal. The Food and Agriculture Organization (FAO) reports that global jackfruit production reached 3.7 million tons between 2015 and 2017, while 2.96 million tons of bioenergy feedstock were produced in 2018. Utilizing jackfruit waste as a renewable bioenergy source not only adds economic value to agricultural residues but also helps reduce overall waste generation. The bark of the jackfruit tree (Artocarpus heterophyllus (AHB)) possesses considerable economic importance and exhibits an enormous distribution throughout several regions in Asia. This study involves the production of biochar from AHB biomass through fast pyrolysis at temperatures between 400 and 600 °C. The biochar produced has a carbon content of 66.69 wt% and a calorific value of 27.15 MJ/kg, respectively, which have similar properties to coal. The kinetic analysis of biomass employed three distinct models (OFW, KAS, and TANG) to determine the activation energy. The current study employed machine learning (ML) models to forecast the mass loss of biomass during pyrolysis, which is challenging because of the intricate characteristics of biomass and the extensive range of operating circumstances. Temperature and heating rate were used as input data, while mass loss was the desired output, to train a variety of machine learning models, including ensemble learning, support vector regression, Gaussian process regression, and neural network models. Among these models, the Gaussian process regression model showed superior performance compared to others, achieving a perfect R2 of 1 and minimal errors on both the validation and test sets, making it the best model to predict mass loss of biomass. © 2025 Elsevier Ltd
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    Performance assessment of waste heat/solar driven membrane-based simultaneous desalination and liquid desiccant regeneration system using a thermal model and KNN machine learning tool
    (Elsevier B.V., 2021) Kiran Naik, B.; Chinthala, M.; Patel, S.; Ramesh, P.
    In this work, the waste heat/solar heat-driven membrane-based liquid desiccant regenerator performance, as well as desalinated water extraction rate, are predicted and analyzed by developing a thermal model and KNN–ML tool. In the membrane-based liquid desiccant regenerator, water is used as a working fluid instead of scavenging air for desalinated water extraction purpose. The proposed thermal model and KNN-ML tool are validated with the literature data and found in good agreement. Optimal inlet conditions were determined for the given operating range using the thermal model and KNN–ML tool. With vapor flux and energy exchange as the performance indicators, the water extraction rate and thermal performance of the membrane-based liquid desiccant regenerator are predicted for the optimal inlet condition using the KNN-ML tool. Also, the heat and mass transfer characteristics such as Lewis number and NTUm across the membrane in the liquid desiccant regenerator are assessed using the developed thermal model. Further, for the optimal inlet conditions, utilizing waste heat from thermal power plants (Method–I) and solar energy from solar heater (Method–II), the thermal performance and water extraction rate across the membrane in the liquid desiccant regenerator are assessed based on the developed thermal model. © 2021 Elsevier B.V.