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In this study, co-pyrolysis experiments were carried out by blending coal slime (CS) and traditional Chinese medicine residue (CMR) in different proportions under nitrogen atmosphere using thermogravimetry-Fourier infrared spectroscopy-mass spectrometry (TG-FTIR-MS). Kissinger-Akahira-Sunose (KAS) and Flynn-Wall-Ozawa (FWO) were used to calculate the activation energy (E) of samples with different conversion rates (α). The PCA method was used to analyze and compare the contribution of samples with different blending ratios to the principal components, and draw its score and load graph. A variety of artificial neural network models composed of different network topologies are selected to simulate and predict the remaining mass of the CS and CMR mixture pyrolysis, and the optimum model is obtained.

Co-Pyrolysis Behavior of Coal Slime and Chinese Medicine Residue by TG-FTIR-MS with Principal Component Analysis and Artificial Neural Network Model