Research on Multi-Level Scheduling of Mine Water Reuse Based on Improved Whale Optimization Algorithm

被引:19
作者
Bo, Lei [1 ]
Li, Zhihan [1 ]
Liu, Yang [1 ]
Yue, Yuangan [1 ]
Zhang, Zihang [1 ]
Wang, Yiying [2 ]
机构
[1] China Univ Min & Technol Beijing, Sch Mech Elect & Informat Engn, Beijing 100083, Peoples R China
[2] Hebei Univ Engn, Sch Mech & Equipment Engn, Handan 056038, Peoples R China
关键词
mine water reuse; reuse efficiency; whale optimization algorithm; opposition-based learning; Levy flight; nonlinear convergence factor; adaptive inertia weight; efficient utilization of deployment; SYSTEM;
D O I
10.3390/s22145164
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
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.
引用
收藏
页数:20
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