Neural network intelligent control method for ammonia oxidation process of sbr method

Abstract

The invention relates to a neural network intelligent control method for the ammonia oxidation process of the SBR method, which belongs to the field of waste water treatment methods. In the SBR system, the real-time control strategy is used to control the aeration time, and the long-term stable SBR data is used as the basic data to establish a 3-layer BP neural network predictive control model, and then the ammonia nitrogen concentration is predicted in advance according to the online detection pH data; mainly based on data collection , data processing and model building; under the condition of constant dissolved oxygen (DO), use the BP neural network model to train, correct and test the data, and then use the neural network predictive control model in the SBR system after meeting the accuracy requirements. Predict and control the ammonia oxidation process.

Description

technical field [0001] The invention relates to a sewage treatment technology, especially capable of realizing the predictive control of the ammonia oxidation process of the SBR method, which is suitable for the denitrification treatment of urban domestic sewage, is beneficial to economically and effectively controlling nitrogen pollution in water bodies, improves the efficiency of denitrification of sewage, and saves The invention relates to nitrogen removal costs, and belongs to the field of wastewater treatment methods. Background technique [0002] In recent years, with the rapid development of the economy, the total amount of sewage discharge has been increasing, especially the discharge of nutrients such as nitrogen and phosphorus in sewage has continued to increase, which has led to the intensification of eutrophication in water bodies. People are aware that controlling nitrogen and phosphorus in water bodies is the key to limiting the growth of algae and curbing the ...

AI Technical Summary

Problems solved by technology

[0004] The sewage treatment process is a complex biochemical reaction process, accompanied by physical and chemical reactions, biochemical reactions, and the transformation and transfer of matter and energy, making its process control very difficult

The SBR intelligent control system mainly includes fuzzy control, expert system and neural network, etc. The fuzzy control system has poor robustness and is easily disturbed by parameter signal fluctuations. The information required by the expert system comes from experience and the database that is constantly updated by the mathematical model simulation module. The results of the expert system are not detailed and accurate enough, and the neural network composed of neurons has a strong nonlinear mapping ability and learning function, which can predictively control the nonlinear system well.

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