Abstract
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.
Highlights
Since the concept of green mine was put forward, the mineral industry has responded positively [1,2,3]
In order to understand the present situation of mine water reuse in detail, this paper investigates the current situation of water inrush and water use in Dahaize Coal mine
In terms of convergence speed, it can be seen from the figure that TSA-GAPSO, GAPSO and particle swarm optimization (PSO) algorithms reach the optimum at about 12 times, while genetic algorithm (GA) algorithm reaches the optimum at about 75 times
Summary
Since the concept of green mine was put forward, the mineral industry has responded positively [1,2,3]. A single optimization algorithm sometimes cannot meet the actual needs, so some researchers propose a hybrid algorithm of particle swarm optimization and genetic algorithm and apply the optimization results to recursive neural network analysis [34], but the speed and precision of convergence need to be improved. Some researchers proposed a hybrid GAPSO algorithm based on GA and PSO to improve the accuracy of the scheduling strategy of FMS and improve the global optimization capability of PSO by using the crossover and variation characteristics of GA [36] and verified the superiority. The hybrid scheme of the two algorithms proposed in this paper has not been applied in mine water scheduling, so it is worth testing and studying.
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