基于动态模糊神经网络的出水含氮参数软测量方法

Soft-sensing method for effluent nitrogen parameters based on a dynamic fuzzy neural network

中文关键词  城市污水处理过程  模糊神经网络  分级更新  出水含氮量  软测量

英文关键词  municipal wastewater treatment process  fuzzy neural network  hierarchical updating  effluent nitrogen concentration  soft-sensing

中文摘要

      针对城市污水处理过程出水氨氮(NH4+-N)和出水总氮(TN)难以实时准确检测的问题, 文中提出了一种基于动态模糊神经网络(DFNN)的出水含氮参数软测量方法. 首先, 采用自组织增删机制和快速二阶学习算法构建模糊神经网络(FNN), 以快速获得结构精简的软测量模型; 其次, 引入自适应激活强度阈值设计FNN分级更新策略, 确保软测量模型在非平稳环境下的预测精度; 最后, 通过基准仿真1号模型(BSM1)平台的数据验证了DFNN软测量方法的有效性, 实验结果表明, 所提出的方法能够实现出水NH4+-N和出水TN的在线精准检测.

英文摘要

      Aiming at the real-time and accurate measurements of the effluent ammonium nitrogen (NH4+-N) and the effluent total nitrogen (TN) in municipal wastewater treatment process, a soft-sensing method for effluent nitrogen parameters based on a dynamic fuzzy neural network (DFNN) is proposed in this paper. First, by utilizing a self-organizing growing-and-pruning mechanism and an improved second-order learning algorithm, a fuzzy neural network (FNN) is constructed in order to obtain a soft-sensing model with a simplified structure. Then, by introducing an adaptive firing strength threshold, a hierarchical updating strategy of FNN is designed, which can effectively ensure the prediction accuracy of the soft-sensing model under non-stationary environments. Finally, the effectiveness of the proposed DFNN soft-sensing method is verified based on the simulation data which were provided by the benchmark simulation model No. 1 (BSM1) platform. The simulation results show that the proposed soft-sensing method can achieve online and accurate measurements of the effluent NH4+-N and the effluent TN.