人工湿地作为污水处理领域的一种新型生态技术，逐渐得到应用并倍受关注。人工湿地污水净化过 程呈多参数影响的复杂非线性状态，采用传统的数学方法难以建立准确的数学模型并进行多因素分析，也无法 精确定出湿地系统最佳运行工况。基于大量可靠的试验数据，首次采用遗传神经网络方法模拟湿地除污系统， 详细论述了网络拓扑结构优化和训练数据预处理等关键问题，建立了可靠的GA－BP模型，并采用该模型仿真湿 地系统正交试验，依据正交试验结果对影响因素进行分级，确定了最佳运行工况，并在此基础上有针对性地提 出可行的强化除污措施。

As a new type of ecological technique in wastewater treatmen t field, Constructed Wetland was gradually put into application and received growing attention. Since the wastewater purification process in constructed wetland was a complex and nonlinear state affected by many interactional factors, it was hard to establish exact mathematics model to carry out multi-factor analysis and determine best operation condition by traditional means. Based on plenty of reliable experimental data, genetic neural network was first tentatively utilized to simulate the pollutant removal system of wetland, and some key problems were discussed, such as how to determine optimally the topological structure, sample scale and unitary method for training data, etc. Optimized GA-BP network was established to simulate orthogonal test of wetland system. According to the results of orthogonal test, best operation condition was decided and the factors(e. g.water level, hydraulic retaining time, etc)were classified. Therefore, proper feasible methods of pollutant removal were put forward.

黄娟（1980－），女，江苏南通市人，东南大学博士研究生，主要研究方向为水污染控制及水处理技术