Coal slime, a byproduct of coal processing with high ash content, poses significant challenges in terms of its efficient separation and resource utilization due to its fine particle size and complex composition. This study aims to optimize the oil agglomeration process for coal slime separation through systematic parameter investigation and predictive modeling. Response surface methodology (RSM) was employed to analyze the individual and interactive effects of pulp density, oil dosage, and agitation rate on three key performance indicators: combustible recovery, efficiency index, and ash rejection. Meanwhile, an artificial neural network (ANN) was developed to establish a robust prediction model for the efficiency index. The novelty of this work lies in the integration of thermodynamic analysis, multi-objective optimization, and machine learning approaches. The key findings include the identification of dodecane as the optimal bridging liquid due to its intermediate carbon chain length that balances interfacial tension and wettability. Under optimized conditions (14% pulp density, 22% oil dosage, and 1600 r/min), the process achieved a combustible recovery of 91.49%, ash rejection of 61.58%, and efficiency index of 53.07%. The ANN model demonstrated superior predictive capability with an overall R² of 0.9659 and RMSE of 1.12. This work provides comprehensive guidelines for the design, optimization, and scale-up of coal slime oil agglomeration processes in industrial applications.

Oil agglomeration, as an efficient technique for coal slime cleaning and upgrading, was employed to separate coal slime with an ash content of 19.08% in this work. The optimum oil type, the pulp density, the oil dosage, and the agitation rate were determined at the dodecane, 12%, 24%, and 1600 r/min, respectively. The response surface methodology (RSM) was adopted to investigate the interactions between various operational factors on the response of combustible material recovery, efficiency index, and ash rejection. By considering the interactions among operational factors, the agglomeration achieved improvement, given small oil consumption, medium agitation rate, and high processing capacity, through optimized operational conditions. Moreover, a prediction model with a higher prediction accuracy for the efficiency index of coal slime oil agglomeration was established based on the artificial neural network (ANN). This work provides an experimental foundation for the operation design and process optimization in the oil agglomeration of coal slimes.