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The optimal scheduling of mine water is a multi-objective, multi-constraint, nonlinear, multi-stage combination of optimization problems, in view of the traditional solution methods with the increase in decision-making variable dimensions facing a large amount of computation, "dimensional disaster" and other problems, the introduction of a new intelligent simulation algorithm—the Whale Optimization Algorithm to solve the optimal scheduling problem of mine water. Aiming at the problem that the Whale Optimization Algorithm itself is prone to local optimization and slow convergence, it has been improved by improving its own parameters and introducing the inertia weight of the particle swarm and has achieved more obvious results. According to the actual situation of Nalinhe No. 2 Mine, the mathematical model of multi-target optimization of mine water is established based on the function of reuse time and reuse cost of mine water as the target function, and the balance of supply and demand of mine water, the water quality requirements of water use points at all levels, the water quantity requirements of reservoirs and the priority of water supply as the constraints. The improved Whale Optimization Algorithm was used to search optimal solution, and the results showed that the adaptability value of the improved Whale Optimization Algorithm was significantly improved compared with before, of which 8.65% and 7.69% were increased in the heating season and non-heating season, and the rate of cost reduction was 46.80% and 36.92%, and the iteration efficiency was also significantly improved, which improved the decision-making efficiency of optimal scheduling and became more suitable for the actual scheduling needs of Nalinhe No. 2 mine. [ABSTRACT FROM AUTHOR]

Aiming at the problem of the inefficiency of coal mine water reuse, a multi-level scheduling method for mine water reuse based on an improved whale optimization algorithm is proposed. Firstly, the optimization objects of mine water reuse time and reuse cost are used to establish the optimal scheduling model of mine water. Secondly, in order to overcome the defect that the whale optimization algorithm (WOA) is prone to local convergence, the opposition-based learning strategy is introduced to speed up the convergence speed, the Levy flight strategy is used to enhance the ability of the algorithm to jump out of the local optimization, the nonlinear convergence factor is used to balance the global and local search ability, and the adaptive inertia weight is used to improve the optimization accuracy of the algorithm. Finally, the improved whale optimization algorithm (IWOA) is applied to the mine water optimization scheduling model with multiple objects and constraints. The results show that the reuse efficiency of the multi-level scheduling method of mine water reuse is increased by 30.2% and 31.9%, respectively, in the heating and nonheating seasons, which can significantly improve the reuse efficiency of mine water and realize the efficient utilization of mine water reuse deployment. At the same time, experiments show that the improved whale optimization algorithm has higher convergence accuracy and speed, which proves the feasibility and superiority of its improvement strategies. [ABSTRACT FROM AUTHOR]

The waste mine water is produced in the process of coal mining, which is the main cause of mine flood and environmental pollution. Therefore, economic treatment and efficient reuse of mine water is one of the main research directions in the mining area at present. It is an urgent problem to use an intelligent algorithm to realize optimal allocation and economic reuse of mine water. In order to solve this problem, this paper first designs a reuse mathematical model according to the mine water treatment system, which includes the mine water reuse rate, the reuse cost at different stages and the operational efficiency of the whole mine water treatment system. Then, a hybrid optimization algorithm, GAPSO, was proposed by combining genetic algorithm (GA) and particle swarm optimization (PSO), and adaptive improvement (TSA-GAPSO) was carried out for the two optimization stages. Finally, simulation analysis and actual data detection of the mine water reuse model are carried out by using four algorithms, respectively. The results show that the hybrid improved algorithm has better convergence speed and precision in solving the mine water scheduling problem. TSA-GAPSO algorithm has the best effect and is superior to the other three algorithms. The cost of mine water reuse is reduced by 9.09%, and the treatment efficiency of the whole system is improved by 5.81%, which proves the practicability and superiority of the algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a general hierarchical dispatching strategy of mine water, with the aim of addressing the problems of low reuse rate of coal mine water, and insufficient data analysis. First of all, water quality and quantity data of the Narim River No. 2 mine were used as the research object; the maximum reuse rate of mine water and the system operation rate comprised the objective function; and mine water quality information, mine water standard, and mine water treatment speed were the constraints. A multi-objective optimization scheduling mathematical model of water supply system was established. Then, to address the problems of premature convergence and ease of falling into a local optimum in the iterative process of particle swarm optimization, the basic particle swarm optimization was improved. Using detailed simulation research, the superiority of the improved algorithm was verified. Eventually, the mine water grading dispatching strategy proposed in this paper is compared with the traditional dispatching method. The results show that the hierarchical dispatching system can significantly improve the mine water reuse rate and system operating efficiency. [ABSTRACT FROM AUTHOR]