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

作　　者：蒙西 张寅 乔俊飞 MENG Xi;ZHANG Yin;QIAO Jun-fei(Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China;Beijing Laboratory of Smart Environmental Protection,Beijing 100124,China;Engineering Research Center of Intelligent Perception and Autonomous Control,Ministry of Education,Beijing 100124,China)

机构地区：[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

出　　处：《控制理论与应用》2024年第12期2383-2392,共10页Control Theory & Applications

基　　金：国家自然科学基金项目(61903012,622731013,61890930–5,62021003);科技创新2030—“新一代人工智能”重大项目(2021ZD0112301,2021ZD0112302);国家重点研发计划项目(2019YFC1906004–2)资助。

摘　　要：针对城市污水处理过程出水氨氮(NH_(4)^(+)-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NH_(4)^(+)-N和出水TN的在线精准检测.Aiming at the real-time and accurate measurements of the effluent ammonium nitrogen(NH_(4)^(+)-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 NH_(4)^(+)-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 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] X703[自动化与计算机技术—控制科学与工程]