AI Article Synopsis
- The article highlights problems of inefficient water scheduling and high costs in coal mine shafts and proposes a smarter hierarchical optimization strategy.
- It uses data from Inner Mongolia to create a model aimed at maximizing the reuse efficiency and minimizing costs of mine water resources, while also considering quality and quantity constraints.
- An improved algorithm is implemented to avoid local optima, resulting in significant cost reductions in both heating and non-heating seasons, along with decreased recovery times.
Article Abstract
This article addresses the issues of unreasonable water scheduling and high costs in coal mine shafts, proposing a hierarchical optimization scheduling strategy. Taking the water quality and quantity of a certain mining area in Inner Mongolia as the research object, it designs the objective function with the highest reuse efficiency and the lowest reuse cost of mine water resources, and establishes the constraint conditions of water quality and quantity for each water-using unit. In response to the problem that traditional genetic algorithms are prone to local optima, an adaptive autobiographical operator is proposed and improved based on Metropolis principle of simulated annealing algorithm. The improved algorithm is applied to the calculation of the scheduling model, and the results show that the recovery cost in the heating season is reduced by 66779.36 CNY/month, a decrease of 10.34%; the recovery cost in the non-heating season is reduced by 61469.28 CNY/month, a decrease of 9.91%. At the same time, the heating season and the non-heating season have reduced by 136.99 h/month and 154.52 h/month respectively, significantly reducing the recovery cost and time.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10950494 | PMC |
| http://dx.doi.org/10.1016/j.heliyon.2024.e27289 | DOI Listing |
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