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
蒙西 1张寅 2乔俊飞3
作者信息
- 1. 北京工业大学信息学部,北京 100124
- 2. 智慧环保北京实验室,北京 100124
- 3. 智能感知与自主控制教育部工程研究中心,北京 100124
- 折叠
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.
Abstract
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 param-eters 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 con-structed 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.
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Key words
municipal wastewater treatment process/fuzzy neural network/hierarchical updating/effluent nitrogen concentration/soft-sensing引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据