Study On The Intelligent Control Of A/O Waterwater Treatment Process Based On Fuzzy Neural Network

As the constant development of the industry, the pollution of industrial wastewater becomes more and more serious. Sewage treatment has become an arduous task. The papermaking industry is one of the major pollutive industries in China, so solving the pollution of papermaking wastewater has become an urgent task. At present the treatment method used in paper wastewater treatment are mainly physicochemical and biochemical technology, in which activated sludge process is mostly adopted. In the wastewater process there are many characteristics, such as multi-variables, nonlinearity, hysteresis, uncertainty and complexity, etc, all of which cause poor stability and bad effluent quality. However, the traditional control owing to its shorting, is difficult to build precise mathematical model,so the control effect is not satisfactory. Therefore, in this paper the fuzzy neural network intelligent control system for aeration in wastewater treatment system was presented. And several valuable conclusions were reached.According to the characteristics of papemaking wastewater, the automatic control system based on Windows CE.NET OS and MCGS software of wastewater was constructed, and the design method and the hybrid intelligent structure were proposed with the consideration of Fuzzy neural network control. The predictive model of effluent COD value is based on the Takagi-Sugeno inferential network. In order to improve the network performance, fuzzy C-means clustering was used to identify model's architecture and optimize fuzzy rule. The simulation indicated that the predictive model had good ability both in learning and generalization, with relative errors of training and test data are 0~0.0073% and 0~10.4636%, respectively.Camparing the predictive modle based on Takagi-Sugeno with the predictive modle based on BP network in performance, when training, MAPE (Mean Absolute Percentage Error)between the predicted and observed values was1.486×10-3 % using Takagi-Sugeno model, and it was 2.703% when testing But MAPE of the training and testing usingBP modle were 2.785% and 11.53%, repectively. Mape using the Takagi-Sugeno predictive model was lower than that using ANN. Therefore, the Takagi-Sugeno modle is more suitable to predict the effluent COD value.The control model was built up based on Mamdani network. The change and change rate of COD in effluent were considered as the input variables of control model, and correction of aeration was considered as the output variable, which was adopted for regulating aeration. Fuzzy neural network algorithm with MCGS development package using VB program was developed,and embeded it into MCGS according to MCGS interface function criterion, so that intelligent control system for papermaking wastewater treatmen was achived. And then The validation experiment was finally given in laboratory. The expectation COD value of effluent was setted to 100 mg/L. The results show that under different influent laod, the COD value of effluent was between 91 to 109 mg/L, remaining at about 100 mg/L. It proved that the intelligent control system based on fuzzy neural network was effective.The reasearch can provide an effective way to achive autocontrol for wastewater biochemical treatment system from papermaking and can provide guidance for the further study of intelligent control in the field of wastewater treatment and the popularization of wastewater treatment project with intelligent control.