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2026-02-26
Characterization and pyrolysis kinetic modelling of lignocellulosic waste from rambutan seeds: A machine learning approach

September 30, 2025 | Biomass and Bioenergy |

A study by Universiti Putra Malaysia, Chulalongkorn University, and Prince Mohammad Bin Fahd University investigated the thermal kinetic characteristics of rambutan (Nephelium lappaceum) seed-derived lignocellulosic waste, a readily available seasonal by-product in Southeast Asia. Rambutan seed biomass has potential for bioenergy applications, but its thermal degradation characteristics remain insufficiently understood.

Ultrafine rambutan seed powder was prepared and characterized using functional group analysis, Raman spectroscopy, nitrogen physisorption, X-ray diffraction, elemental analysis, and morphological observations. Thermogravimetric analysis was then performed at multiple heating rates to examine thermal degradation behavior. Kinetic parameters were estimated using both model-free methods (Friedman and modified Vyazovkin) and model-fitting approaches, revealing a complex multistep decomposition mechanism. The average activation energy across conversion degrees of 0.2–0.7 was approximately 282 kJ/mol.

In addition, several machine learning models, including multilayer perceptron, boosted regression trees, classification and regression trees, and multivariate adaptive regression splines (MARS), were applied to predict activation energy values. Among these, the MARS model showed the highest predictive accuracy, with strong agreement with experimentally derived data. The findings demonstrate that combining kinetic analysis with machine learning provides a reliable approach for modeling biomass pyrolysis and supports the utilization of rambutan seed waste as a bioenergy resource.

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Characterization and pyrolysis kinetic modelling of lignocellulosic waste from rambutan seeds: A machine learning approach
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