| Recently wastewater pollution is very severe and the treating is still a heavy task. In order to treat wastewater effectively and realize recycling, it is important to develop computer operational decision support systems (computer technology and automation technology) that are able to play a similar role to the expert in wastewater treatment process control, in which it can ensure the operation safety, stable and reliable, and improve operational performance of wastewater treatment processes, so as to realize the wastewater treatment continuous, economic and benign operation. Previously, applications of control theory to wastewater treatment mainly focused on issues of nonlinearity, uncertainty and posterity, where difficulties in establishing accurate mathematical models and designing reliable controllers existed. Intelligent control is recently developed from conventional control theory. It consists of several control theories, such as fuzzy control (FC), artificial neural network control (ANNC), expert control, etc. The intelligent control of wastewater treatment system is a focus in wastewater treatment research field.Base on the all-around review and analysis of the progress of wastewater treatment study, a hybrid-intelligent control method was presented in line with neural network and fuzzy logic theory, in which FC, PID and GA were all taken into consideration. The intelligent-intelligent control was used in highly effective integration wastewater treatment control system; using intelligent methods, the degradation mechanism of typical organic matter was modeled and the operation status was assessed; and several valuable conclusions were reached.In this paper, according to the characteristics of wastewater treatment process, 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 -PID, GA- BP and Fuzzy neural network control.The model construction techniques of ANN and fuzzy mathematics were combined to describe the wastewater treatment process, then a soft-computation model was built for water quality prediction and the influent water indexes were evaluated. Advanced neuro-fuzzy modeling, namely an adaptive network-based fuzzy inference system (ANFIS), was employed to develop models for the prediction of suspended solids (SS) and chemical oxygen demand (COD) removal of a full-scale wastewater treatment plant treating process wastewaters from a paper mill. In order to improve the network performance, fuzzy subtractive clustering was used to identify model's architecture and optimize fuzzy rule, meanwhile principal component analysis (PCA) was applied to reduce the input variable dimensionality. Input variables were reduced from six to four for COD and SS models, by considering PCA results and linear correlation matrices among input and output variables. For this reason, the lag problem of measuring system is solved satisfactorily.A fuzzy neural network predictive control scheme for studying the coagulation process of wastewater treatment was given based on the characteristic of wastewater treatment and fuzzy neural network's analysis. An adaptive fuzzy neural network was employed to model the nonlinear relationships between the removal rate of pollutants and the chemical dosages, in order to adapt the system to a variety of operating conditions and acquire a more ?exible learning ability. The system includes a fuzzy neural network emulator of the reaction process, a fuzzy neural network controller, and an optimization procedure based on a performance function that is used to identify desired control inputs. The gradient descent algorithm method was used to realize the optimization procedure. After fuzzy C-means clustering to identify models'architecture and a hybrid learning rule to identify parameters, the simulation indicated that the predictive model had good ability both in learning and generalization. Also, Fuzzy neural network algorithm with MCGS development package using VB program was developed,and then it was embedded into MCGS according to MCGS interface function criterion to achieve intelligent control system for wastewater treatment,so the control model could change the dosage according to different situationAccording to analyze the inherent mechanism of wastewater treatment process, mathematics model was established, which reflects the relations among dissolved oxygen, microorganism and substrate. And an integrated neural-fuzzy process controller was developed for studying the aeration of wastewater treatment. In order to improve the fuzzy neural network performance, the self-learning ability embedded in the fuzzy neural network model was emphasized for improving the rule extraction performance. The fuzzy neural network proved to be very effective in modeling the aeration performs better than artificial neural networks (ANN). For comparing between operation with and without the fuzzy neural controller, and comparing with operation with PID controller, a aeration unit in a Wastewater Treatment Process was picked up to support the derivation of a solid fuzzy control rule base. It is shown that, using the fuzzy neural controller, in terms of the cost effectiveness, it enables us to save almost 33% of the operation cost during the time period when the controller can be applied. Thus, the fuzzy neural network proved to be a robust and effective control tool, easy to integrate in a global monitoring system for cost managing.Analyzing and identifying the best reflux ratio and the denitrification efficiency of the influent loading under the different influent loading in A/O system, the inner loop control strategy was obtained; and on basis of the change law of nutrients in the system, fuzzy neural network models for forecasting dynamic change of nutrient concentration were designed, which can track the dynamic changes of the nutrients in the A/O system; taking nitrate nitrogen in the end of the anaerobic pond as modeling parameter, the fuzzy neural network control model to control nitrate nitrogen in the reactor is designed.The lab scale tests for the anaerobic/anoxic/oxic (AAO) process were carried out to investigate the removal and fate of di-n-butyl phthalate (DnBP) and optimum systematic operation parameters. Transfer and transformation of organic substances in the AAO system were especially concerned. The removal characteristics of phthalate esters were studied in anaerobic/anoxic/oxic (AAO) processes. A removal (biological degradation and sorption) model was formulated, and kinetic parameters were evaluated with batch experiments under anaerobic, anoxic, oxic conditions. Furthermore, the intelligent methods (neural networks and genetic algorithm) were used to model the relationships between phthalate esters and the water quality characteristic parameters, so as to realize to forecast the effluent quality of anaerobic/anoxic/oxic reactors of the AAO process.The research 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.
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