Thermodynamics, kinetics, gas emissions and artificial neural network modeling of co-pyrolysis of sewage sludge and peanut shell

Abstract

Abstract The co-pyrolysis characteristics of sewage sludge (SS) and peanut shell (PS) under nitrogen atmosphere were studied by thermogravimetric – Fourier transform infrared spectrometry (TG-FTIR) and artificial neural network (ANN). The proportion of PS in the experimental samples is 0, 10, 20, 30, 40, 50, 60, 70, 80, 90 and 100%. The experiments were performed at three heating rates of 5\xa0°C/min, 7\xa0°C/min and 10\xa0°C/min. The variation of sample mass with temperature and gas emission were detected in this paper. SS and PS in the co-pyrolysis experiment have a synergistic effect in terms of mass loss at 335\xa0°C ∼ 500\xa0°C. Gases generated by pyrolysis of the mixture of SS and PS include H2O, CH4, CO2, CO, phenol and NH3. The functional groups detected in the experiment include C O and C O. Apparent activation energy E was obtained by two non-isothermal kinetic analysis methods (Flynn–Wall–Ozawa and Kissinger–Akahira–Sunose). The artificial neural network method was used to predict the relationship between the mass loss of pyrolysis experiments and the change of temperature. The best ANN model (ANN21) for predicting SS and PS pyrolysis was identified.