Optimizing water resource allocation in mining areas using a multi-objective interval chance-constrained model

This study focuses on the Luling Coal Mine in North Anhui (China), addressing the challenges of optimizing water allocation while balancing economic, environmental, and social objectives. A multi-objective interval chance-constrained programming model was developed to maximize net economic benefits, minimize carbon emissions, and increase job opportunities. The model accounts for uncertainties in water inflow and demand using interval numbers and was solved using a genetic algorithm, achieving a Pareto optimal solution set with a confidence level of 95%. With respect to optimized water allocation, the total water supply for the mining area is 2.30 million m3/year, with water sources distributed as follows: mine water: 58.7%, groundwater: 26.3%, recycled water: 9.9%, and external surface water: 5.1%. As for economic–environmental trade-offs, it is necessary to prioritize economic benefits, increase the use of mine water and external surface water, while reducing recycled water usage. Every 10 million yuan increase in economic benefits leads to an additional 115 kg of carbon emissions and a significant decline in ecological benefits. For this purpose, enhancing technological innovation in recycled water treatment can lower costs and improve ecological sustainability without sacrificing economic growth

Coal · Coal mining · Inflow · Pareto optimal · Surface water · Sustainability · Water resources · Fuzzy Systems and Optimization · Mining Techniques and Economics · Water resources management and optimization

• A fast and elitist multiobjective genetic algorithm

  Open Access•Kalyanmoy Deb, Amrit Pratap et al.•IEEE Transactions on Evolutionary…•2002

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