基于ANN的SBBR短程硝化过程仿真研究  

Simulation based on artificial neural network for SBBR shortcut nitrification treatment

作　　者：曹明[1] 杨朝晖[1] 曾光明[1] 许朕[1] 徐峥勇[1] 谭文化 

机构地区：[1]湖南大学环境科学与工程学院,湖南长沙410082 [2]常德市环境卫生管理处,湖南常德415000

出　　处：《中国环境科学》2008年第8期694-698,共5页China Environmental Science

基　　金：国家“973”项目(2005CB724203);国家自然科学基金资助项目(50478053)

摘　　要：利用人工神经网络对实验室中短程硝化过程进行仿真模拟,采用误差反向传播算法,并结合自适应学习率,在MATLAB语言环境下建立了进水NH4+-N﹑DO﹑温度以及外加碳源与出水NH4+-N和NO2--N之间的非线性映射函数关系,确立了相关的动态模型.结合最优化网络模型运行参数,对样本进行仿真学习,仿真输出值与实际值的拟合程度相当高,最大误差仅有13.8955%.通过权重分析,探究了各输入因素与输出结果之间的价值贡献关系,进水NH4+-N和温度对短程硝化过程表现出较大的影响.The feasibility of dynamic simulation of shortcut nitrification process based on artificial neural network （ANN） was studied. With Back-Propagation algorithm and the adaptive study rate, a dynamic simulation model was established by MATLAB software, which could reflect the nonlinear function relationship between NH4^＋-N, DO, temperature, external carbon source of influent and NH4^＋-N and NO2-N of effluent. The numerical outputs and the experimental values matched well, with a highest error of 13.8955%. The value contribution relationships between each input factor and output results were investigated by weighted average analysis, which indicated that NH4＋-N and temperature had tremendous influence on the shortcut nitrification process. The results suggested that the ANN could reflect the nonlinear function between influent and effluent parameters, and was suitable for the dynamic monitoring of the shortcut nitrification bio-process for wastewater.

关 键 词：人工神经网络 误差反向传播算法 自适应学习率 短程硝化 

分 类 号：X703[环境科学与工程—环境工程]