Co-pyrolysis of coal slime and cattle manure by TG–FTIR–MS and artificial neural network modeling: Pyrolysis behavior, kinetics, gas emission characteristics. (15th May 2022)

• Record Type: Journal Article
• Authors: Jiang, Chunlong; Zhou, Wenliang; Bi, Haobo; Ni, Zhanshi; Sun, Hao; Lin, Qizhao
• Is Part Of: Energy. Volume 247(2022)
• Publication Date: 2022-05-15
• Subjects: TG-MS-FTIR -- Artificial neural network -- Co-pyrolysis -- Coal slime -- Cattle manure; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.123203
• Languages: English
• ISSNs: 0360-5442

Abstract

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. Graphical abstract: Image 1 Highlights: Activation energy of co-pyrolysis were calculated by two different methods. Adding cattle manure to coal slime can improve its pyrolysis performance. Use the ANN model to study the co-pyrolysis of cattle manure to coal slime. The prediction of MLP13 model were good agreement with the thermogravimetric data.