基于数据驱动模型的入海污染源排污削减优化方法研究
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摘要
陆源污染物排放是影响近岸海域水环境生态质量最重要的因素。建立了数据驱动模型人工神经网络算法和海域水质模型相耦合的入海污染源排污削减优化方法:基于水质模型污染源项设计工况的数值计算,获得海域内部观测点污染物浓度;以数据驱动模型人工神经网络算法建立状态变量(海域内部观测点污染物浓度)同控制变量(污染源项)之间的非线性关系;以海域内部观测点环境目标数据为输入,模拟推算出目标前提下的各污染源项入海允许排放量;最终结合实际排污量,核算获得削减量。以连云港徐圩海域4个入海污染源无机氮的排污削减研究验证方法的有效性,结果表明:数据驱动人工神经网络方法具有非线性、简洁、灵活的优点,可以为近岸海域水污染控制工作提供基础数据支撑;同时研究中采用分区排污削减的方式更能体现兼顾公平的基本原则,优化入海污染源排污削减工作。
Terrigenous pollutants discharge is one of the most important influencing factor which affects nearshore water ecological environment quality. An optimal sewage reduction method for marine pollution sources was proposed by coupling the marine water quality model and neural network algorithm of data driven model. Based on the mathematical calculation of the marine water quality model for the water quality of pollution sources designed working conditions,the concentrations of pollutants in the marine internal gauge stations were obtained. The nonlinear relationship between the state variables( the concentration of pollutants in the marine internal gauge stations) and the control variables( pollution sources) was constructed by the data driven artificial neural network algorithm. The permitted discharge amounts ofevery pollution source were calculated with the input of the environmental target data in the marine internal gauge stations. Finally,the reduction amount was computed by the amount of actual sewage combining with the permitted discharge. A case study of inorganic nitrogen emission reduction studies for the 4sea pollution sources in the Xuwei marine district of Lianyungang was used for validating the proposed method. The results showed that,the data driven neural network method has advantages of being nonlinear,concise and flexible and can support basic data for nearshore water pollution control work. At the same time,in the study to use the method of partition emission reductions in different regions can reflect the basic principles of fairness,and optimize the sea pollution emission reduction work.
Terrigenous pollutants discharge is one of the most important influencing factor which affects nearshore water ecological environment quality. An optimal sewage reduction method for marine pollution sources was proposed by coupling the marine water quality model and neural network algorithm of data driven model. Based on the mathematical calculation of the marine water quality model for the water quality of pollution sources designed working conditions,the concentrations of pollutants in the marine internal gauge stations were obtained. The nonlinear relationship between the state variables( the concentration of pollutants in the marine internal gauge stations) and the control variables( pollution sources) was constructed by the data driven artificial neural network algorithm. The permitted discharge amounts ofevery pollution source were calculated with the input of the environmental target data in the marine internal gauge stations. Finally,the reduction amount was computed by the amount of actual sewage combining with the permitted discharge. A case study of inorganic nitrogen emission reduction studies for the 4sea pollution sources in the Xuwei marine district of Lianyungang was used for validating the proposed method. The results showed that,the data driven neural network method has advantages of being nonlinear,concise and flexible and can support basic data for nearshore water pollution control work. At the same time,in the study to use the method of partition emission reductions in different regions can reflect the basic principles of fairness,and optimize the sea pollution emission reduction work.
引文
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[3]李红亮,米玉华.河北省水功能区纳污能力及限制排污总量研究[J].水资源保护,2011,27(1):76-79.
[4]刘红刚,陈新庚,彭晓春.基于合作博弈论的感潮河网区污染物排放总量削减分配模型研究[J].生态环境学报,2011,20(3):456-462.
[5]吕志峰,孙然然,王学昌.胶州湾主要污染源削减排污量研究[J].海岸工程,2012,31(4):61-66.
[6]邓义祥,雷坤,富国,等.基于分配指数的渤海TN总量分配研究[J].环境科学研究,2015,28(12):1862-1869.
[7]张静,柯东胜,方宏达,等.汕头港海域氮、磷营养盐环境容量及排放总量控制的研究[J].大连海洋大学学报,2012,27(3):247-254.
[8]王艳,魏爱泓,张丽,等.苏北灌河口邻近海域氮、磷污染物环境容量研究[J].中国海洋大学学报(自然科学版),2015,45(6):78-84.
[9]王晓玮,赵骞,赵仕兰.海洋环境容量及入海污染物总量控制研究进展[J].海洋环境科学,2012,31(5):765-769.
[10]Rumelhart D E,Hinton G E,Williams R J.Learning representations by back propagating errors[J].Nature,1986,323:533-536.
[11]Hornik K,Stinchcombe M,White H.Multilayer feedforward networks are universal approximators.Neural Networks,1988,2(5):359-475.
[12]Nakatsuji K,Yamanaka R,Nishida S.Numerical simulation of seasonal baroclinic circulation and dispersion process of COD in the Bohai Sea.The First Asian and Pacific Coastal Engineering Conference,Dalian,China.Dalian University of Technology Press,2001,369-378.
[13]朱静,陈新永,孙丽.曹妃甸海域水环境容量计算与陆源污染控制研究[J].中国水运,2014,14(6):152-155.
[14]赵俊生,王桂云,陈则实,等.海洋环境有效利用的分析研究——北部湾广西沿岸海域[M].北京:海洋出版社,2002.
[15]林卫清,卢士强,矫吉珍.长江口及毗邻海域水质和生态动力学模型与应用研究[J].水动力学研究与进展,2008,23(5):522-531.
[16]李明昌,张光玉,司琦,等.海域组合单元水质模型多污染源浓度优化反演方法研究[J].应用基础与工程科学学报,2012,20(增):150-158.