Effluent index online soft measurement prediction method in urban sewage treatment process

Abstract

The invention discloses an effluent index online soft measurement prediction method in the urban sewage treatment process. According to the method, firstly an instant learning model structure with correlation as the principle and the recursive partial least-square method as a local model is adopted, and meanwhile, description on uncertainty of the model is performed on the basis of the statistical learning theory. The model can be used for fully reflecting the characteristics such as uncertainty and large lag of the sewage treatment process, the explanatory performance of a prediction result is higher, controllers can adjust process variables such as the aeration rate and the backflow amount of an aeration tank in time, material balance and bacterial population balance are kept, and efficient removal of organic matter is achieved fully.

Description

technical field [0001] The invention relates to the technical field of urban sewage treatment, in particular to an online soft sensor prediction method for urban sewage treatment process effluent indicators with model uncertainty description based on instant learning algorithm and statistical learning theory. Background technique [0002] Sewage treatment is a process of purifying sewage by adopting specific processes to separate pollutants in sewage or convert them into harmless substances. However, the production conditions of the sewage treatment process are harsh, the random interference is serious, and it has the characteristics of multiple inputs, multiple outputs, uncertainty, strong nonlinearity, large lag, and large time variation, which makes the process complicated and difficult to use the mechanism model to accurately description of. In addition, due to the complexity of the sewage treatment system, there are some important effluent indicators (such as BOD 5 , ...

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Problems solved by technology

However, the production conditions of the sewage treatment process are harsh, the random interference is serious, and it has the characteristics of multiple inputs, multiple outputs, uncertainty, strong nonlinearity, large lag, and large time variation, which makes the process complicated and difficult to use the mechanism model to accurately description of

In addition, due to the complexity of the sewage treatment system, there are some important effluent indicators (such as BOD 5 , COD, SVI, etc.) cannot be measured online in real time, BOD 5 It is an important effluent indicator in the process of sewage treatment. It is used to detect the organic pollutants in the water body. It directly affects the production and operation of the sewage plant.

Although the development of new hardware testing instruments can directly solve the problem of BOD 5 However, the reaction of the sewage treatment process is complex, resulting in limited detection accuracy of hardware instruments and high maintenance costs of online instruments

The biochemical reaction process of sewage treatment is easily affected by the concentration of sewage, weather, temperature, and time changes. The standard model of sewage biochemical treatment process formulated by the International Water Association (IWA) is formulated in a specific environment and has great limitations

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