The Soft Sensor. Sequencing Batch Activated Sludge Wastewater Chemical Oxygen Demand Study

With economic and social development and the increasing human needs, water pollution is becoming a more and more serious problem. Sequencing batch reactor (SBR) is a biological wastewater treatment method, which is widely used in the wastewater treatment plants worldwide. The features of SBR make it rely heavily on the modern automation technology. Chemical oxygen demand (COD) is a comprehensive indicator of the total organic matter amount in water and an important indicator of the natural water quality classification, and also one of the main factors causing the deterioration of waterbodies.It largely contributes to the smelly and black water. COD is an important control parameter of SBR process. Traditionally, COD is measured with chemical methods, which is accurate with the shortage of poor timeliness and high cost, and the testing process is usually carried on with the production of other pollutants. As the sewage treatment technology has matured, the focal points of sewage treatment research has changed to how to improve the level of automation in the sewage treatment process, improve the water quality and enhance the operation monitoring and so on. Therefore, the fast and accurate measuring of COD in SBR process is both theoretically and practically important.This paper set the research direction of real-time accurate COD parameter measurement by introducing soft measurement technology, and then introduces the soft measurement modeling approaches. With the modeling approaches, the paper gave a detailed analysis on the COD parameter-related water-quality parameter in the SBR process, found out the secondary variables in soft measurement modeling, and established the COD soft measurement modeling in SBR process based on the change rate of water quality parameter and the time window of water quality parameter. And then, the paper gave details of how to carry out the soft measurement function by BP neural network, and brought forward the optimization program of neural network on generalization problem and local minimum problem.Considering the contradictory data caused by limited types of auxiliary variables in COD soft measurement modeling and local minimum problem, the paper put forward a method combining a support vector machine and neural network for COD soft measurement in SBR process.This method can have a COD pre-estimation value by using the support vector machine, then under the COD changing rule, this method can estimate the COD value by using the BP neural network and Elman neural networks respectively. Then the estimated COD value can be got after data fusion. Experiments show that the soft measurement results of this method is better than ones of single neural network measurements.Finally, after proposing unilateral BP neural network modeling and a method jointing a support vector machine and neural networks for COD soft measurement modeling, the paper introduced the concept of integrated multiple neural network and AdaBoost, and put forward an AdaBoost multi-neural network COD soft measurement model.The experiment results show that the model haves better results on both prediction accuracy and stability...