摘要
Abstract
Dissolved oxygen concentration is an important process parameter in activated sludge wastewater treatment.Accu-rate dissolved oxygen concentration measurement is the premise to ensure effluent quality to meet the standards and energy-saving production.Therefore,a soft sensor model of dissolved oxygen concentration based on an optimized neural network is proposed.Firstly,the adaptive step size strategy and learning strategy are introduced into the standard sparrow search algorithm to improve the search capability and search accuracy of the algorithm.Secondly,to improve the prediction accuracy and efficiency of dissolved oxy-gen,the improved sparrow search algorithm(ISSA)is used to optimize the BP neural network parameters,and the soft sensor mod-el of dissolved oxygen is constructed with the best combination of automatically selected parameters.Finally,the soft sensor model is used to predict the dissolved oxygen concentration of the benchmark simulation model No.1(BSM1)and the actual wastewater treatment process.The simulation results show that the ISSA-BP prediction model has higher prediction accuracy and faster conver-gence compared with BP,RBF,ELM,JS-BP and PSO-BP prediction models,and it is more suitable for practical application.关键词
污水处理/溶解氧预测/改进麻雀搜索算法/神经网络/软测量Key words
wastewater treatment/dissolved oxygen prediction/improved sparrow search algorithm/neural network/soft measurement分类
信息技术与安全科学
