基于遗传算法的灰色非线性水环境管理模型及应用
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摘要
以郑州市贾鲁河水环境治理规划为基础,统筹考虑到治理区域内的工程建设、水环境改善策略、污水处理厂建设运行以及配套水库调度等,构建了灰色非线性水环境管理模型,并选择遗传算法作为计算手段,对该模型进行了求解。构建的模型同时考虑到了治理区域内的污水处理厂优化运行、南水北调补水、区域内3个生态湖泊与治理河段的净化能力等。通过优化运行协调,实现了运行费用经济合理、水生态环境改善、水质达标等多项治理目标。研究结果表明,基于遗传算法的灰色非线性规划在区域水环境治理中的应用潜力十分显著。
A gray nonlinear water environment management model was developed for comprehensive water environment improvement plan of Jialu River basin, Zhengzhou, which considered project construction, water environment improving strategy, construction and operation of sewage water treatment plants, and reservoirs regulation. The model was solved by the genetic algorithm(GA). The model took into account the operation optimization of sewage water treatment plants, the water supplement by south-to-north water diversion project, three ecological lakes and self-purifying capacities of the treated river reaches. By a more optimized and coordinated operation, higher attainment rate of multi-objects were achieved such as improvement of water ecology and water quality at a reasonable cost. The results show that the GA-based gray nonlinear programming has high potential applicability in regional water environment management.
A gray nonlinear water environment management model was developed for comprehensive water environment improvement plan of Jialu River basin, Zhengzhou, which considered project construction, water environment improving strategy, construction and operation of sewage water treatment plants, and reservoirs regulation. The model was solved by the genetic algorithm(GA). The model took into account the operation optimization of sewage water treatment plants, the water supplement by south-to-north water diversion project, three ecological lakes and self-purifying capacities of the treated river reaches. By a more optimized and coordinated operation, higher attainment rate of multi-objects were achieved such as improvement of water ecology and water quality at a reasonable cost. The results show that the GA-based gray nonlinear programming has high potential applicability in regional water environment management.
引文
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[2] 陈南祥,李跃鹏,徐晨光.基于多目标遗传算法的水资源优化配置[J].水利学报,2006,37(3):308-313.
[3] 陈根社,陈新海.遗传算法的研究与进展[J].信息与控制,1994,23(4):215-222.
[4] 王宏伟,张鑫,邱俊楠,等.基于多目标遗传算法的西宁市水资源优化配置研究[J].水土保持通报,2012,32(2):150-153.
[5] 史银军,栗晓玲,徐万林,等.基于水资源转化模拟的石羊河流域水资源优化配置[J].自然资源学报,2011,26(8):1423-1434.
[6] 杨侃,刘云波.基于多目标分析的库群系统分解协调宏观决策方法研究[J].水科学进展,2001,11(2):232-236.
[7] 钟登华,熊开智,成立芹.遗传算法的改进及其在水库优化调度中的应用研究[J].中国工程科学,2003(9):22-26.
[8] 周明,孙树栋.遗传算法原理及应用[M].北京:国防工业出版社,1996.
[9] 金菊良,张欣莉,丁晶.水资源最优分配问题的遗传算法[J].水利水运科学研究,2000(4):65-68.
[10] 邹进.自适应逐次逼近遗传算法及其在水库群长期调度中的应用[J].系统工程理论与研究,2013,33(1):267-271.
[11] 姚俊峰,梅炽,彭小奇,等.混沌遗传算法及其应用[J].系统工程,2001,19(1):70-74.
[12] 娄帅,王慧敏,牛文娟,等.基于免疫遗传算法水资源配置多阶段群决策优化模型研究[J].资源科学,2013,35(3):569-576.
[13] 张忠波,张双虎,蒋云钟,等.改进的遗传算法在水库调度中的应用 [J].人民黄河,2012,34(8):54-57.
[14] 黄显峰,邵东国,顾文权,等.基于多目标混沌优化算法的水资源配置研究[J].水利学报,2008,39(2):183-188.
[15] 黄河勘测规划设计有限公司.郑州贾鲁河综合治理工程环境影响报告书[R].郑州:黄河勘测规划设计有限公司,2017,8.
[16] 黄河水利委员会黄河科学研究院.郑州市贾鲁河综合治理工程水资源论证报告书[R].郑州:黄河水利委员会黄河科学研究院,2016,7.
[17] 孙月峰,张胜红,王晓玲,等.基于混合遗传算法的区域大系统多目标水资源优化配置模型[J].系统工程理论与实践,2009,29(1):139-144.
[18] Cai X M.Solving nonlinear water management models using a combined genetic algorithm and linear programming approach[J].Adv.Water Resour.,2001(24):667–676.
[19] Frohlich H,Chapelle O,Scholkopf B.Feature selection for support vector machines by means of genetic algorithm[C]//Proc,15th IEEE Int.Conf.on Tools with Artificial Intelligence,IEEE,New York,2003:142–148.
[20] Mulligan A E,Brown L C.Genetic algorithms for calibrating water quality models[J].Environ.Eng.1998,124(3):202–211.
[21] Ng A W M,Perera B J C.Selection of genetic algorithm operators for river water quality model calibration[J].Eng.Applic.Artif.Intell.,2003(16):529–543.
[22] Tufford D L,McKellar H N.Spatial and temporal hydrodynamic and water quality modeling analysis of a large reservoir on the South Carolina[J].USA.coastal plain.Ecol.Modell,1999,114(2–3):137–173.
[23] Ho C W,Lee K H,Leung K S.A genetic algorithm based on mutation and crossover with adaptive probabilities[C]// Proc.IEEE Int.Conf.on Evolutionary Computation,IEEE,New York,1999:768–774.