{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T01:07:26Z","timestamp":1772240846301,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,6,15]],"date-time":"2021-06-15T00:00:00Z","timestamp":1623715200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2018YFC0406404-5"],"award-info":[{"award-number":["2018YFC0406404-5"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper proposes a general hierarchical dispatching strategy of mine water, with the aim of addressing the problems of low reuse rate of coal mine water, and insufficient data analysis. First of all, water quality and quantity data of the Narim River No. 2 mine were used as the research object; the maximum reuse rate of mine water and the system operation rate comprised the objective function; and mine water quality information, mine water standard, and mine water treatment speed were the constraints. A multi-objective optimization scheduling mathematical model of water supply system was established. Then, to address the problems of premature convergence and ease of falling into a local optimum in the iterative process of particle swarm optimization, the basic particle swarm optimization was improved. Using detailed simulation research, the superiority of the improved algorithm was verified. Eventually, the mine water grading dispatching strategy proposed in this paper is compared with the traditional dispatching method. The results show that the hierarchical dispatching system can significantly improve the mine water reuse rate and system operating efficiency.<\/jats:p>","DOI":"10.3390\/s21124114","type":"journal-article","created":{"date-parts":[[2021,6,15]],"date-time":"2021-06-15T21:24:29Z","timestamp":1623792269000},"page":"4114","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Multi-Objective Optimization of a Mine Water Reuse System Based on Improved Particle Swarm Optimization"],"prefix":"10.3390","volume":"21","author":[{"given":"Yang","family":"Liu","sequence":"first","affiliation":[{"name":"School of Mechanical Electronic & Information Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zihang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mechanical Electronic & Information Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9975-1477","authenticated-orcid":false,"given":"Lei","family":"Bo","sequence":"additional","affiliation":[{"name":"School of Mechanical Electronic & Information Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongxu","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Mechanical Electronic & Information Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"125061","DOI":"10.1016\/j.jhydrol.2020.125061","article-title":"Acid mine drainage from coal mining in the United States\u2014An overview","volume":"588","author":"Acharya","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_2","first-page":"34","article-title":"Example analysis based on the application of underground water reuse system in coal mines","volume":"45","author":"Meng","year":"2017","journal-title":"J. Hydrol. Coal Sci. Technol."},{"key":"ref_3","first-page":"274","article-title":"Various types of wastewater treatment and comprehensive utilization methods","volume":"10","author":"Shu","year":"2020","journal-title":"Chem. Eng. Equip."},{"key":"ref_4","first-page":"34","article-title":"Development and Status of the Treatment Technology for Acid Mine Drainage","volume":"38","author":"Tong","year":"2021","journal-title":"Min. Metall. Explor."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Rivera, M.J., Luis, A.T., and Grande, J.A. (2019). Physico-Chemical Influence of Surface Water Contaminated by Acid Mine Drainage on the Populations of Diatoms in Dams (Iberian Pyrite Belt, SW Spain). Int. J. Environ. Res. Public Health, 16.","DOI":"10.3390\/ijerph16224516"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1007\/s10040-019-02101-0","article-title":"GRACE satellite monitoring and driving factors analysis of groundwater storage under high-intensity coal mining conditions: A case study of Ordos, northern Shaanxi and Shanxi, China","volume":"28","author":"Chen","year":"2020","journal-title":"Hydrogeol. J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2232","DOI":"10.1109\/TLA.2015.7273782","article-title":"Monitoring and Control of the Processes Involved in the Capture and Filtering of Biogas Using Fpga-Embedded Fuzzy Logic","volume":"13","author":"Vanti","year":"2015","journal-title":"IEEE Lat. Am. Trans."},{"key":"ref_8","first-page":"72","article-title":"Design of an automated monitoring system for mine water purification and treatment","volume":"9","author":"Gao","year":"2018","journal-title":"Electron. Qual."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1768","DOI":"10.1360\/01yd0437","article-title":"Combination of drainage, water supply and environmental protection as well as rational distribution of waterresource in Zhengzhou mining district","volume":"48","author":"Wu","year":"2005","journal-title":"Sci. China Ser. D Earies Sci."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Psarrou, E., Tsoukalas, I., and Makropoulos, C. (2018). A Monte-Carlo-Based Method for the Optimal Placement and Operation Scheduling of Sewer Mining Units in Urban Wastewater Networks. Water, 2.","DOI":"10.3390\/w10020200"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wei, Z., Quan, Z., Wu, J., Li, Y., and Zhong, H. (2021). Deep deterministic policy gradient-drl enabled multiphysics-constrained fast charging of lithium-ion battery. IEEE Trans. Ind. Electron.","DOI":"10.1109\/TIE.2021.3070514"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"12786","DOI":"10.1109\/TVT.2020.3025627","article-title":"Battery-involved energy management for hybrid electric bus based on expert-assistance deep deterministic policy gradient algorithm","volume":"69","author":"Wu","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_13","first-page":"125178","article-title":"Application of a modified CES production function model based on improved PSO algorithm","volume":"387","author":"Cheng","year":"2020","journal-title":"Appl. Math. Comput."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1007\/s10661-020-8228-z","article-title":"Application of particle swarm optimization to water management: An introduction and overview","volume":"192","author":"Loaiciga","year":"2020","journal-title":"Environ. Monit. Assess."},{"key":"ref_15","first-page":"215","article-title":"Comparison of Three Evolutionary Algorithms: GA, PSO, and DE","volume":"11","author":"Kachitvichyanukul","year":"2012","journal-title":"Ind. Eng. Manag. Syst. Int. J."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1016\/j.ins.2005.02.003","article-title":"A study of particle swarm optimization particle trajectories","volume":"176","author":"Engelbrecht","year":"2006","journal-title":"Inf. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1016\/j.cor.2004.08.012","article-title":"Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization","volume":"33","author":"Chatterjee","year":"2006","journal-title":"Comput. Oper. Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.ins.2020.05.016","article-title":"Adaptive online data-driven closed-loop parameter control strategy for swarm intelligence algorithm","volume":"536","author":"Lu","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4348","DOI":"10.1016\/j.eswa.2010.09.104","article-title":"A hybrid particle swarm optimization with estimation of distribution algorithm for solving permutation flowshop scheduling problem","volume":"38","author":"Liu","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.cor.2015.02.008","article-title":"Multiple strategies based orthogonal design particle swarm optimizer for numerical optimization","volume":"60","author":"Qin","year":"2015","journal-title":"Comput. Oper. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1007\/s12559-016-9447-z","article-title":"Cognitively Inspired Artificial Bee Colony Clustering for Cognitive Wireless Sensor Networks","volume":"9","author":"Kim","year":"2017","journal-title":"Cogn. Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"113353","DOI":"10.1016\/j.eswa.2020.113353","article-title":"A modified particle swarm optimization using adaptive strategy","volume":"152","author":"Liu","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3808\/jei.200600072","article-title":"Particle Swarm Optimization Compared to Other Heuristic Search Techniques for Pipe Sizing","volume":"8","author":"Suribabu","year":"2006","journal-title":"J. Environ. Inform."},{"key":"ref_24","first-page":"1742","article-title":"Energy Saving Schedule of Mine Drainage System Based on Particle Swarm Optimization","volume":"1168","author":"Sang","year":"2018","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1109\/TEVC.2004.826071","article-title":"Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients","volume":"8","author":"Ratnaweera","year":"2004","journal-title":"Trans. Evol. Comput."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1362","DOI":"10.1109\/TSMCB.2009.2015956","article-title":"Adaptive Particle Swarm Optimization","volume":"39","author":"Zhan","year":"2001","journal-title":"Trans. Syst. Man Cybern. Part B Cybern."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2997","DOI":"10.1016\/j.asoc.2012.11.033","article-title":"A review of particle swarm optimization and its applications in Solar Photovoltaic system","volume":"13","author":"Khare","year":"2013","journal-title":"Appl. Soft Comput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1007\/s10489-013-0459-z","article-title":"Cooperative Velocity Updating model based Particle Swarm Optimization","volume":"40","author":"Wang","year":"2014","journal-title":"Appl. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4855","DOI":"10.1007\/s00500-019-04240-8","article-title":"GuASPSO: A new approach to hold a better exploration-exploitation balance in PSO algorithm","volume":"24","author":"Rezaei","year":"2020","journal-title":"Soft Comput."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.asoc.2017.01.032","article-title":"An Adaptive Bumble Bees Mating Optimization algorithm","volume":"55","author":"Marinakis","year":"2017","journal-title":"Appl. Soft Comput."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4120","DOI":"10.1016\/j.eswa.2014.12.046","article-title":"A cautious PSO with conditional random","volume":"42","author":"Chan","year":"2015","journal-title":"Expert Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2759","DOI":"10.1016\/j.asoc.2012.11.011","article-title":"Convergence of nomadic genetic algorithm on benchmark mathematical functions","volume":"13","author":"Siva","year":"2013","journal-title":"Appl. Soft Comput."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.asoc.2009.11.006","article-title":"Velocity Modulated Bacterial Foraging Optimization Technique (VMBFO)","volume":"11","author":"Gollapudi","year":"2011","journal-title":"Appl. Soft Comput."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.energy.2018.03.045","article-title":"Forecasting the output of shale gas in China using an unbiased grey model and weakening buffer operator","volume":"151","author":"Zeng","year":"2018","journal-title":"Energy"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1596","DOI":"10.1109\/TII.2014.2302638","article-title":"An Integrated System for Regional Environmental Monitoring and Management Based on Internet of Things","volume":"10","author":"Fang","year":"2014","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Xiong, G.Y., and Niu, L.L. (2019, January 19\u201321). Research and application of big data fusion management platform in petroleum industry. Proceedings of the 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA 2019), Xi\u2019an, China.","DOI":"10.1109\/ICIEA.2019.8834126"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/12\/4114\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:16:15Z","timestamp":1760163375000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/12\/4114"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,15]]},"references-count":36,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["s21124114"],"URL":"https:\/\/doi.org\/10.3390\/s21124114","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,15]]}}}