Pyrolysis Kinetics of Pine Waste Based on Ensemble Learning

Alok Dhaundiyal () and Laszlo Toth
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Alok Dhaundiyal: Environmental Physics Department, HUN-REN Centre for Energy Research, Konkoly-Miklos Utca 29-33, 1121 Budapest, Hungary
Laszlo Toth: Institute of Technology, Hungarian University of Agriculture and Life Sciences, Páter Károly Utca 1, 2100 Godollo, Hungary

Energies, 2025, vol. 18, issue 10, 1-20

Abstract: This article aimed to incorporate the coordinated construction of classifiers to develop a model for predicting the pyrolysis of loose biomass. For the purposes of application, the ground form of pine cone was used to perform the thermogravimetric analysis at heating rates of 5, 10, and 15 °C∙min −1 . The supervised machine learning technique was considered to estimate the kinetic parameters associated with the thermal decomposition of the material. Here, the integral as well as differential form of the isoconversional method was used along with the Kissinger method for the maximum reaction rate determination. Python (version 3.13.2), along with PyCharm (2024.3.3) as an integrated development environment (IDE), was used to develop code for the given problem. The TG model obtained through the boosting technique provided the best fitting for the experimental dataset of raw pine cone, with the root squared error varying from ±1.82 × 10 −3 to ±1.84 × 10 −3 , whereas it was in the range of ±1.78 × 10 −3 to ±1.83 × 10 −3 for processed pine cone. Similarly, the activation energies derived through the trained models of Friedman, OFW, and KAS were 176 kJ-mol −1 , 151.60 kJ-mol −1 , and 142.04 kJ-mol −1 , respectively, for raw pine cone. It was seen that the boosting technique did not provide a reasonable fit if the number of features was increased in the kinetic models. This happened owing to an inability to maintain a tradeoff between variance and bias. Moreover, the multiclassification in pyrolysis kinetics through the proposed scheme was not able to capture the distribution pattern of target values of the differential method. With the increase in the heating rates, the noise level in the predicted model was also relatively increased.

Keywords: loose biomass; machine learning; classifier; thermogravimetry; pyrolysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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