Machine learning based prediction and iso-conversional assessment of oxidatively torrefied spent coffee grounds pyrolysis

Suluh Pambudi, Jiraporn Sripinyowanich Jongyingcharoen and Wanphut Saechua

Renewable Energy, 2024, vol. 237, issue PB

Abstract: This research focused on developing a predictive model for mass loss during the pyrolysis of oxidatively torrefied spent coffee grounds (SCG) using machine learning techniques. Four algorithms were employed: artificial neural networks (ANN), k-nearest neighbors (k-NN), random forest (RF), and decision tree (DT), with the RF model demonstrating superior performance (R2 > 0.9981, RMSE <1.346) for both training and testing sets. The pyrolysis behavior, kinetics, and thermodynamics of SCG were also investigated using thermogravimetric analysis (TGA) under an inert atmosphere at different heating rates. Higher heating rates in TGA cause Tpeak values to shift to higher temperatures with increased DTGpeak values, while also resulting in lower Tonset and higher Toffset. Kinetic analysis, using the Flynn-Wall-Ozawa (FWO) method, was identified as the most suitable approach for determining activation energy (Ea), with values ranging from 192.66 to 288.13 kJ mol−1, indicating differences in energy requirements for pyrolysis across samples. Thermodynamic analysis further revealed that both raw SCG and oxidatively torrefied SCG pyrolysis were endothermic reactions. These findings contribute valuable insights into the optimization of biomass conversion technologies, highlighting the potential of machine learning in improving predictive accuracy and efficiency in thermal behavior modeling. This research advances sustainable bioenergy production by promoting the use of SCG, an abundant waste material, as a renewable feedstock in pyrolysis-based processes.

Keywords: Kinetic; Machine learning; Oxidative torrefaction; Pyrolysis; Thermogravimetric analysis (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:237:y:2024:i:pb:s0960148124017257

DOI: 10.1016/j.renene.2024.121657

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