Abstract

The co-combustions of major waste streams such as textile dyeing sludge (TDS) and waste tires of shared bikes may reduce the dependence on fossil fuels, as well as enhance their circular management and the recovery of their value-added products. In this study, the ash-to-gas products, interaction effects, and reaction mechanisms of the co-combustions of TDS and waste tires were characterized. The mono-combustions included the three stages of water evaporation, volatiles release, and mineral decomposition for TDS and the five stages for both rubber (RT) and polyurethane (PUT) tires. The three substages of the main stage of volatiles release for TDS had the activation energy of 124.5, 144.9, and 167.5 kJ/mol and were best explained by the reaction mechanism models of D3, D5, and F2, respectively. The (co-)combustion performance indices rose with the increased heating rate. The blend of 25% TDS with 75% RT (TR) and 75% PUT (TP) led to the best co-combustion performance according to comprehensive combustion index, with TP outperforming TR. The co-combustions of TP and TR reduced the activation energy required for the main devolatilization stage reaction. There was no significant difference in the main reaction mechanisms between the co-combustions. The interaction between TDS and waste tires reduced the applied energy required for the main devolatilization stage. The co-combustions at the low temperature produced O-H, CH4, CO2, CO, SO2, NO, carbonyl products, olefin products, and ketones. The co-combustions increased the production of C-H, reduced SO2 release and the viscosity of their ashes, promoted the complete combustion of substances, and alleviated the scale and sintering issues regardless of TP versus TR and caused the early release of NO from TP. According to the thermodynamic equilibrium simulations, the TR co-combustion promoted the retentions of Ca, S, Si, and Fe, in particular, the fixation of S. The addition of PUT enhanced the combination of Ca and Si into CaSiO3. The optimization based on the artificial neural networks pointed to the temperature range of 400–800 oC and the TR co-combustion as the optimal operational conditions.

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