Abstract
The optimization of an integrated coal gangue system of mining, dressing, and backfilling in deep underground mining is a multi-objective and complex decision-making process, and the factors such as spatial layout, node location, and transportation equipment need to be considered comprehensively. In order to realize the intellectualized location of the nodes for the logistics and transportation system of underground mining and dressing coal and gangue, this paper establishes the model of the logistics and transportation system of underground mining and dressing coal gangue, and analyzes the key factors of the intellectualized location for the logistics and transportation system of coal and gangue, and the objective function of the node transportation model is deduced. The PSO–QNMs algorithm is proposed for the solution of the objective function, which improves the accuracy and stability of the location selection and effectively avoids the shortcomings of the PSO algorithm with its poor local detailed search ability and the quasi-Newton algorithm with its sensitivity to the initial value. Comparison of the particle swarm and PSO–QNMs algorithm outputs for the specific conditions of the New Julong coal mine, as an example, shows that the PSO–QNMs algorithm reduces the complexity of the calculation, increases the calculation efficiency by eight times, saves 42.8% of the cost value, and improves the efficiency of the node selection of mining–dressing–backfilling systems in a complex underground mining environment. The results confirm that the method has high convergence speed and solution accuracy, and provides a fundamental basis for optimizing the underground coal mine logistics system. Based on the research results, a node siting system for an integrated underground mining, dressing, and backfilling system in coal mines (referred to as MSBPS) was developed.
Highlights
To study the problem of optimal selection of nodes in the integrated coal gangue logistics production system of “mining–dressing–backfilling” in underground coal mining, the precondition is that the underground mining, sorting, filling, and transportation system links are analyzed separately from the perspective of logistics rationalization
The particle swarm optimization algorithm (PSO) algorithm and the PSO–quasi-Newton methods (QNMs) algorithm are set in each group, and the values of the output results are analyzed, based on the two solution algorithms
In the process of the PSO algorithm, after several iterative solutions, the fitness value of the node particles after each operation was compared with the population optimal solution in the particle swarm
Summary
Relevant scholars [1] put forward the integration technologies of the mining–dressing–backfilling system, which is to establish coal–gangue separation and selection centers, backfilling preparation centers, and gangue pockets in the underground; the coal and gangue products extracted from the underground working face are not lifted to the shaft, but sorted by the underground dressing system, and the sorted gangue is filled in place by the underground backfilling preparation system to realize the gangue backfilling in the extraction area. This is why China strongly advocates these green mining and scientific mining methods
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.