基于数据驱动模型的入海污染源排污削减优化方法研究  

Research on sewage reduction optimal method of marine pollution sources by data-driven model

作　　者：李明昌[1] 戴明新[1] 周斌[1] 焦润红[1] 邹斌[2] 崔雷[3,4] 李艳丽[1] 徐楠[1] 

机构地区：[1]交通运输部天津水运工程科学研究院水路交通环境保护技术实验室,天津300456 [2]国家卫星海洋应用中心,北京100081 [3]国家海洋环境监测中心国家海洋局近岸海域生态环境重点实验室,辽宁大连116023 [4]国家海洋环境预报中心国家海洋局海洋灾害预报技术研究重点实验室,北京100081

出　　处：《水资源与水工程学报》2017年第2期9-13,18,共6页Journal of Water Resources and Water Engineering

基　　金：国家自然科学基金项目(51209110);天津市科技兴海项目(KJXH2011-17);中央级公益性科研院所基本科研业务费专项资金项目(TKS160227;TKS160209;TKS160210);水体污染控制与治理科技重大专项(2017ZX07107-004);中国博士后科学基金面上项目(2015M581358);国家海洋局海洋灾害预报技术研究重点实验室开放基金(LOMF1704)

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.

关 键 词：水质模型 数据驱动模型 神经网络 入海污染源 排污削减量 优化方法 

分 类 号：X55[环境科学与工程—环境工程]