Investigation of co-combustion characteristics of sewage sludge and coffee grounds mixtures using thermogravimetric analysis coupled to artificial neural networks modeling

Abstract

Artificial neural network (ANN) modeling was applied to thermal data obtained by non-isothermal thermogravimetric analysis (TGA) from room temperature to 1000°C at three different heating rates in air to predict the TG curves of sewage sludge (SS) and coffee grounds (CG) mixtures. A good agreement between experimental and predicted data verified the accuracy of the ANN approach. The results of co-combustion showed that there were interactions between SS and CG, and the impacts were mostly positive. With the addition of CG, the mass loss rate and the reactivity of SS were increased while charring was reduced. Measured activation energies (E_a) determined by the Kissinger-Akahira-Sunose (KAS) and Ozawa-Flynn-Wall (OFW) methods deviated by <5%. The average value of E_a (166.8kJ/mol by KAS and 168.8kJ/mol by OFW, respectively) was the lowest when the fraction of CG in the mixture was 40%.

Keywords: Artificial neural network; Co-combustion; Coffee grounds; Sewage sludge; Thermogravimetric analysis.