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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. [ABSTRACT FROM AUTHOR]

Scrap tire is a typical waste fuel, for which the combustion in the form of pellets is a feasible way. In this work, the ignition behavior, combustion process and pollutant emission of the scrap tire in pellet form are explored. The experiments were performed in the vertical heating tube furnace under different coflow conditions in CO2/O2 atmosphere. The temperature was set to 873, 973, 1073 K and the gas flow rate was set to 5, 10 and 20 L/min. The combustion images of scrap tire were recorded by the high-performance camera. The scrap tire pellet presented the characteristics of homogeneous ignition and the fly ash was captured around the scrap tire pellets. Increasing oxygen concentration and temperature shortened the ignition delay time and reduced the internal ignition temperature. The splash flame occurred during the volatile combustion under the conditions of high temperature, high oxygen concentration, and high gas flow rate. The distinct pores and cracks were found on the surface of char during the char combustion. The yields of pollutant gas increased as the gas flow rate increased from 5 to 20 L/min. With the increase of temperature, the emission of NO and SO2 was increased but NO2 emission was decreased. [ABSTRACT FROM AUTHOR]

Based on mass loss data of sewage sludge (SS) and peanut shell (PS) during combustion and pyrolysis obtained by thermogravimetric analysis (TGA), artificial neural networks (ANN) models were established to predict the experimental results in this paper. The effects of mixing ratio and temperature on the combustion and pyrolysis of SS-PS were studied. Mixing ratio and experimental temperature were taken as input parameters of ANN model, and sample mass was taken as output parameter. The optimal ANN models (ANN12 for combustion and ANN11 for pyrolysis) were determined, which can well predict the thermogravimetric curves of combustion and pyrolysis with the correlation coefficient of 0.99999. The predicted data are in good agreement with the experimental data of the ANN model, which proves the reliability and accuracy of the application of ANN for the thermogravimetric experiments of SS and PS. The established optimal ANN model can be used to predict the weight loss of SS-PS heat treatment without experiment. [ABSTRACT FROM AUTHOR]