Experimental study of the co-pyrolysis of sewage sludge and wet waste via TG-FTIR-GC and artificial neural network model: Synergistic effect, pyrolysis kinetics and gas products

Abstract This study investigated the co-pyrolysis characteristics and kinetics of sewage sludge (SS) and wet waste (WW) using thermogravimetric-Fourier transform infrared spectrometry-gas chromatography/mass spectrometry (TG-FTIR-GC/MS) and artificial neural network (ANN). The proportion of WW is 0, 10, 30, 50, 70, and 100%, respectively. These mixtures were heated from 30 to 900 °C at three heating rates (10, 20, and 40 °C/min). The change of gas functional groups with different blends (-OH, –CH, CO2, C C, phenol, CO, and NH3) was detected by FTIR. S3W7 has a synergistic effect on the pyrolysis in all temperature ranges and can also greatly suppress CO2 emission (−35.25%), which is of practical significance to carbon neutrality. S3W7 was recommended as the best ratio. The gas products of S3W7 were obtained by GC/MS, which were mainly nitrides (C5H5N, C4H11N, etc.), hydrocarbons containing C O (C3H6O2, C7H8O2, etc.), and furans (C5H6O2, C6H8O, etc.). The apparent activation energy (E) was measured using Flynne-Walle-Ozawa (FWO) and Kissinger-Akahira-Sunose (KAS) methods. Machine learning methods were used to analyse the pyrolysis. ANN19 was found the best prediction model of 21 models. The equation to predict TG data was established.