Study Of Modeling And Simulation Based On Neural Network In Sludge Sewage Disposal

In order to protect the limited freshwater resource of our country, we should develop the work of sewage water treatment. Activated sludge wastewater process is one of advanced sewage water treatment processes and it suits the conditions of our country. Sewage disposal is a complex process, which is often affected by many factors such as the quality and volume of water supply, equipments and processes. Water quality changes have the non-linear feature and nondeterministic features. The traditional water-quality prediction, ASM Model, has not produced good results in application because it involved many reaction process and parameters, which cannot be determined by sophisticated methods for measuring, Artificial Neural Network has self-adaptive, self-learning and fault-tolerant capacity as well as large-scale concurrent operation, which is particularly applicable for inaccurate and fuzzy information processing with many factors and conditions involved. So it has especially essential realistic value to study the modeling and simulation technology of wastewater process.The paper analyzed the parameters which affect the process of treatment during organic degradation, nitrification and anti-nitrification and phosphorus removal, also had further analysis to the current modeling methods, and pointed out the development direction of modeling and simulation technology of the process. The paper studied the modeling methods in activated sludge sewage disposal by BP and RBF neural networks, and testified these wastewater treatment models by neural network are effective; the paper adopted MATLAB 6.5 as computational platform and BP neural network configuration which consists of input level, implied level and output level. Six data of water supply quality were determined after process analysis, which are water volume, PH, temperature, COD, sulfide, MLSS. MLSS refers to input nerve cell whereas COD refers to output nerve cell. Based on monitoring data of sewage disposal station, BP network is analyzed in terms of the factors affecting learning efficiency and prediction...