Combustion performance of fine screenings from municipal solid waste: Thermo-kinetic investigation and deep learning modeling via TG-FTIR

Lu Tian, Kunsen Lin, Youcai Zhao, Chunlong Zhao, Qifei Huang and Tao Zhou

Energy, 2022, vol. 243, issue C

Abstract: The combustion behavior, kinetics, thermodynamics and gas products of fine screenings (FS) classified from municipal solid waste (MSW) in an air atmosphere were explored by TG-FTIR. A deep learning model was established using 1D–CNN–LSTM algorithm to predict thermogravimetric data of FS combustion, with visualization technology (TensorBoard) applied to display the weights and biases in various cells. The thermogravimetric analysis (TG) and differential thermal gravity (DTG) curves indicated that the FS combustion process can be divided into four stages. The average activation energy (Ea) of FS combusted at different stages, exhibited different change tendencies with increasing levels of conversion (α). The highest enthalpy (ΔH) of 206.40 kJ/mol and free Gibbs energy (ΔG) of 55.03 kJ/mol emerged in stage Ⅳ, while the highest changes of entropy (ΔS) of 169.11 J/(mol·K) occurred in stage Ⅱ. The main gas products (CO2, H2O and CO) and functional groups (CO and phenols) were all detected. For the 1D–CNN–LSTM model, the optimal settings for the prediction of thermogravimetric data were a neuron number of 150, dropout of 0.003, epoch number of 200, and batch size of 25. The highest correlation coefficient (R2) of 94.41% was obtained using the optimum model parameters, achieving an excellent prediction performance.

Keywords: Fine screenings; Combustion; Thermo-kinetic; Gas products; Deep learning (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:243:y:2022:i:c:s0360544221030322

DOI: 10.1016/j.energy.2021.122783

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