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

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.