Pyrolytic characteristics of fine materials from municipal solid waste using TG-FTIR, Py-GC/MS, and deep learning approach: Kinetics, thermodynamics, and gaseous products distribution

Fine materials (FM) from municipal solid waste (MSW) classification require disposal, and pyrolysis is a feasible method for the treatments. Hence, the behavior, kinetics, and products of FM pyrolysis were investigated in this study. A deep learning algorithm was firstly employed to predict and verify the TG data during the process of FM pyrolysis. The results showed that FM pyrolysis could be divided into drying (< 138 ?), de-volatilization (138-570 ?), and decomposition stage (>= 570 ? above). The de-volatilization can further be divided into stage 2 and stage 3, with values of activation energy estimated by Flynn-Wall-Ozawa and Kissinger-AkahiraSunose methods as 123.35 and 172.95 kJ/mol, respectively. The gas products like H2O, CO2, CH4, and CO, as well as functional groups like phenols and carbonyl (C=O), were all detected during the process of FM pyrolysis by thermogravimetric-fourier transform infrared spectrometry at a heating rate of 10 C/min. The main species detected by pyrolysis-gas chromatography-mass spectrometry analyzer included acid (41.98%) and aliphatic hydrocarbon (22.44%). Finally, the 1D-CNN-LSTM algorithm demonstrated an outstanding generalization capability to predict the relationship between FM composition and temperature, with R2 reaching 93.91%. In sum, this study provided a reference for the treatment of FM from MSW classification as well as the feasibility and practicability of deep learning applied in pyrolysis.