Intelligent Decision-Making Methods for Tailings Filling in High-Cold Open-Pit Coal Mines

Due to the specific requirements of tailings filling in high-cold open-pit coal mines, an intelligent decision-making method is proposed in this study. The method combines the Adaptive Simulated Annealing (ASA) and Artificial Bee Colony (ABC) algorithms. Firstly, the physical properties of the tailings, rheological behavior, and filling effect under high-cold conditions were analyzed. A filling model was then developed, taking into account key variables such as concentration, pressure, and temperature. Subsequently, the ASA-ABC algorithm was utilized to optimize the model, enhancing both global and local search capabilities while avoiding local optima. Experimental results have shown that this method significantly improves the optimization of filling parameters as well as the quality and stability of the filling process. Furthermore, field applications have confirmed that the algorithm effectively adapts to complex conditions in high-cold environments, thereby enhancing efficiency and effectiveness of tailings filling operations. This research provides an efficient and reliable intelligent decision-making tool for mine environmental management in high-cold regions and offers scientific support for future technology optimization and application promotion.