Co-pyrolysis of coal slime and cattle manure by TG–FTIR–MS and artificial neural network modeling: Pyrolysis behavior, kinetics, gas emission characteristics

Chunlong Jiang, Wenliang Zhou, Haobo Bi, Zhanshi Ni, Hao Sun and Qizhao Lin

Energy, 2022, vol. 247, issue C

Abstract: In this study, Thermogravimetric-Mass spectrometry-Fourier transform infrared spectrometry (TG-MS-FTIR) were used to study the co-pyrolysis behavior and gaseous products of coal slime (CS) and cattle manure (CM). By establishing different artificial neural network (ANN) prediction models, it was found that MLP13 model is the best prediction model. It was determined that the pyrolysis process of CM and CS can be divided into three stages, of which the second stage has the largest mass loss. Due to the different mixing ratio, there will be synergistic interaction or inhibitory effect during co-pyrolysis of CM and CS. Adding CM to CS will improve the pyrolysis performance of CS. The Kissinger-Akahira-Sunose (KAS) and Flynn-Wall-Ozawa (FWO) methods were used to calculate the activation energy. The activation energy was the lowest when the mixing ratio is CM:CS = 7:3, which was 195.377 kJ/mol (FWO) and 195.008 kJ/mol (KAS), respectively.

Keywords: TG-MS-FTIR; Artificial neural network; Co-pyrolysis; Coal slime; Cattle manure (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:247:y:2022:i:c:s0360544222001062

DOI: 10.1016/j.energy.2022.123203

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