{"title":"利用新型蚁群优化（ACOR）算法优化输水管网中的氯消耗量","authors":"M. H. Ahmadi, B. Mansoori, R. Aghamajidi","doi":"10.1007/s13762-024-06008-6","DOIUrl":null,"url":null,"abstract":"<p>Chlorination by maintaining the injected chlorine concentration in the range between the minimum and maximum is among the most inexpensive and common disinfection methods in water distribution networks. The minimum concentration of residual chlorine must be observed to control the microbial quality of water. Besides, the maximum chlorine concentration must be observed to control problems related to water smell and taste and to prevent the production of toxic byproducts. This research has developed a model by combining the EPANET model and the ACO<sub>R</sub> optimization algorithm to optimize the chlorine injection program during the operation period. According to the results, the ACOR algorithm could be used to derive a suitable program for chlorine injection in the water distribution network such that the permissible constraints of chlorine are observed in the consumption nodes of the network and the consumption of chlorine is reduced to the least level in the network. The developed model was applied to determine an optimal chlorine injection program in a classical example (the Branford network), which was also of interest to some previous researchers. Using the optimal injection program obtained by the model, the chlorine concentration was set at an acceptable network level between the permissible range of 0.2–0.8 g/l. This output was more favorable than the response of other methods in terms of the total residual chlorine concentration, which was 5.8% and 4.7% lower in this method than the methods based on PSO and genetic algorithms, respectively. Moreover, a better convergence speed was obtained in this algorithm, and the number of calculation times of the objective function was 49.5 and 64.4 less than the methods based on PSO and genetic algorithms, respectively. Therefore, the ACO<sub>R</sub> algorithm can be used to derive the chlorine injection program to both comply with the permissible constraints of chlorine and reduce chlorine consumption to the minimum level.</p>","PeriodicalId":589,"journal":{"name":"International Journal of Environmental Science and Technology","volume":"63 1","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimization of chlorine consumption in water distribution networks by using the new ant colony optimization (ACOR) algorithm\",\"authors\":\"M. H. Ahmadi, B. Mansoori, R. Aghamajidi\",\"doi\":\"10.1007/s13762-024-06008-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Chlorination by maintaining the injected chlorine concentration in the range between the minimum and maximum is among the most inexpensive and common disinfection methods in water distribution networks. The minimum concentration of residual chlorine must be observed to control the microbial quality of water. Besides, the maximum chlorine concentration must be observed to control problems related to water smell and taste and to prevent the production of toxic byproducts. This research has developed a model by combining the EPANET model and the ACO<sub>R</sub> optimization algorithm to optimize the chlorine injection program during the operation period. According to the results, the ACOR algorithm could be used to derive a suitable program for chlorine injection in the water distribution network such that the permissible constraints of chlorine are observed in the consumption nodes of the network and the consumption of chlorine is reduced to the least level in the network. The developed model was applied to determine an optimal chlorine injection program in a classical example (the Branford network), which was also of interest to some previous researchers. Using the optimal injection program obtained by the model, the chlorine concentration was set at an acceptable network level between the permissible range of 0.2–0.8 g/l. This output was more favorable than the response of other methods in terms of the total residual chlorine concentration, which was 5.8% and 4.7% lower in this method than the methods based on PSO and genetic algorithms, respectively. Moreover, a better convergence speed was obtained in this algorithm, and the number of calculation times of the objective function was 49.5 and 64.4 less than the methods based on PSO and genetic algorithms, respectively. Therefore, the ACO<sub>R</sub> algorithm can be used to derive the chlorine injection program to both comply with the permissible constraints of chlorine and reduce chlorine consumption to the minimum level.</p>\",\"PeriodicalId\":589,\"journal\":{\"name\":\"International Journal of Environmental Science and Technology\",\"volume\":\"63 1\",\"pages\":\"\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2024-09-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Environmental Science and Technology\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.1007/s13762-024-06008-6\",\"RegionNum\":4,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Environmental Science and Technology","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1007/s13762-024-06008-6","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Optimization of chlorine consumption in water distribution networks by using the new ant colony optimization (ACOR) algorithm
Chlorination by maintaining the injected chlorine concentration in the range between the minimum and maximum is among the most inexpensive and common disinfection methods in water distribution networks. The minimum concentration of residual chlorine must be observed to control the microbial quality of water. Besides, the maximum chlorine concentration must be observed to control problems related to water smell and taste and to prevent the production of toxic byproducts. This research has developed a model by combining the EPANET model and the ACOR optimization algorithm to optimize the chlorine injection program during the operation period. According to the results, the ACOR algorithm could be used to derive a suitable program for chlorine injection in the water distribution network such that the permissible constraints of chlorine are observed in the consumption nodes of the network and the consumption of chlorine is reduced to the least level in the network. The developed model was applied to determine an optimal chlorine injection program in a classical example (the Branford network), which was also of interest to some previous researchers. Using the optimal injection program obtained by the model, the chlorine concentration was set at an acceptable network level between the permissible range of 0.2–0.8 g/l. This output was more favorable than the response of other methods in terms of the total residual chlorine concentration, which was 5.8% and 4.7% lower in this method than the methods based on PSO and genetic algorithms, respectively. Moreover, a better convergence speed was obtained in this algorithm, and the number of calculation times of the objective function was 49.5 and 64.4 less than the methods based on PSO and genetic algorithms, respectively. Therefore, the ACOR algorithm can be used to derive the chlorine injection program to both comply with the permissible constraints of chlorine and reduce chlorine consumption to the minimum level.
期刊介绍：
International Journal of Environmental Science and Technology (IJEST) is an international scholarly refereed research journal which aims to promote the theory and practice of environmental science and technology, innovation, engineering and management.
A broad outline of the journal''s scope includes: peer reviewed original research articles, case and technical reports, reviews and analyses papers, short communications and notes to the editor, in interdisciplinary information on the practice and status of research in environmental science and technology, both natural and man made.
The main aspects of research areas include, but are not exclusive to; environmental chemistry and biology, environments pollution control and abatement technology, transport and fate of pollutants in the environment, concentrations and dispersion of wastes in air, water, and soil, point and non-point sources pollution, heavy metals and organic compounds in the environment, atmospheric pollutants and trace gases, solid and hazardous waste management; soil biodegradation and bioremediation of contaminated sites; environmental impact assessment, industrial ecology, ecological and human risk assessment; improved energy management and auditing efficiency and environmental standards and criteria.
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