Investigation on coal/coal gangue mixtures co-combustion via TG-DSC tests, multicomponent reaction model, and artificial neural network

Abstract

Coal gangue (CG) is the main hazardous solid waste produced during coal mining activities; it increases the combustion risk of coal/coal gangue (C/CG) mixtures. In the present work, the combustion characteristics, interactions, combustion reaction model, and kinetic parameters of C/CG co-combustion were investigated under seven different mixing ratios. Thermogravimetric data revealed that the mixture of 70 % coal and 30 % CG exhibited the most strongest synergistic effect and heat release capability, whereas that with 30 % coal and 70 % CG showed the most strongest inhibitory effect. Additionally, a multicomponent combustion reaction model for coal/CG mixture co-combustion was established. We found that the three-component combustion reaction model could appropriately describe the combustion process of C/CG mixtures. Furthermore, as the CG ratio increased to 90 %, the reaction participation of volatile and inorganic matter gradually increased, whereas that of organic matter gradually decreased. The impact of CG content on the activation energy of the reaction system depends on the conversion rate. Finally, the temperature-increasing rate, mixing ratio, and finishing temperature were considered as the import data, artificial neural network (ANN) prediction was performed based on the thermogravimetric data, results show ANN-42 (3 × 9 × 17 × 1) model was the most appropriate for predicting the co-combustion of coal/CG mixtures.