Co-combustion characteristics and reaction mechanism of coal slime and sewage sludge using a novel multilateral double volumetric parallel model and dendrite neural network

Abstract

The co-combustion of coal slime (CS) and sewage sludge (SS) presents an effective method for significantly reducing solid waste on large-scale. This study conducted experimental research to investigate the combustion and co-combustion characteristics of CS and SS, focusing on exploring the synergistic effects and mechanisms during co-combustion processes. Novel multilateral double parallel models (Multi-DPM) were proposed to analyze the contributions of volatile and char components in CS/SS mixtures across different ratios during co-combustion. The effects of temperature, mixing ratio, and heating rate on the co-combustion process were evaluated using a new novel dendrite neural network (DD-NN) model. The results revealed varyied degrees of interaction between CS and SS during co-combustion. Kinetic and thermodynamic analyses revealed that the most significant enhancement in the CS/SS mixing ratio of 6:4. Both the double parallel volumetric model (DPVM) and the newly proposed multilateral double parallel volumetric models (Multi-DPVM) demonstrated the best fit with experimental conversion and proved their suitability for analyzing the co-combustion process of CS and SS. These models were further employed to predict the combustion kinetic parameters and conversion profiles of total volatiles and total char of single and mixed materials, as well as the volatiles and char of the individual CS and SS materials within the mixture. The obtained conversion data and activation energy values from the DPVM and Multi-DPVM were used to assess the best fit reaction mechanism using the master-plots method. This validation underscored the reliability of the DPVM and Multi-DPVM based on integral fitting model solution. Moreover, the application of the new DD-NN model demonstrated good predictive performance for the co-combustion process of CS and SS, identifying key transfer functions influencing the co-combustion process. Overall, this study provides valuable insights into comprehensively understanding the co-combustion mechanism of CS and SS, providing crucial information for industrial product design.