Investigation of co-combustion of sewage sludge and coffee industry residue by TG-FTIR and machine learning methods

Abstract

Faced with the problems of the environment and resources, the co-combustion of sewage sludge has gradually become a trend. In this paper, the combination of thermogravimetric and Fourier transform infrared spectroscopy (TG-FTIR) was used to study the co-combustion of sewage sludge (SS) and coffee industry residues (CIR). SS and CIR were mixed according to the mass ratios of 1:0, 9:1, 7:3, 5:5, 3:7, 1:9, and 0:1, and the temperature was programmed to be heated to 800 ℃ in the atmosphere with an air flow rate of 50 mL/min. The percentages of mass loss during combustion of SS and CIR were 55.8% and 96.8%, respectively. As the mass percentage of CIR increased, the comprehensive combustion index (CCI) of the sample improved. The Kissinger-Akahira-Sunose (KAS), Flynn-Wall-Ozawa (FWO), and Starink methods were used to evaluate the activation energy (Eα) of the reaction. Principal component analysis (PCA) and artificial neural network (ANN) were used to determine the main reaction and predict experimental data of co-combustion of SS and CIR, respectively.