Sludge volume index (SVI) soft measuring method based on self-organized T-S fuzzy nerve network

A technology of fuzzy neural network and soft measurement, applied in neural learning methods, biological neural network models, instruments, etc., can solve problems such as measurement difficulties

Active Publication Date: 2014-02-26
BEIJING UNIV OF TECH
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Problems solved by technology

[0005] The present invention aims at the problem of difficulty in online measurement of SVI, analyzes the formation reasons of sludge bulking, summarizes the easily measurable water quality parameters closely related to SVI, and determines the input quantity of the model by using the principal component analysis

Method used

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  • Sludge volume index (SVI) soft measuring method based on self-organized T-S fuzzy nerve network
  • Sludge volume index (SVI) soft measuring method based on self-organized T-S fuzzy nerve network
  • Sludge volume index (SVI) soft measuring method based on self-organized T-S fuzzy nerve network

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Embodiment Construction

[0087] The experimental data comes from the actual daily report of a small sewage treatment plant in Beijing. figure 1 The neural network prediction model of SVI is given, and its inputs are the mixed liquid suspended solids concentration MLSS, acidity and alkalinity pH, aeration tank water temperature T, aeration tank ammonia NH 4 , the model output is the sludge volume index SVI. Among them, MLSS refers to the weight of dry sludge contained in the mixed liquid of the biochemical tank per unit volume; pH reflects the acidity and alkalinity of the influent water quality; T is the current sewage temperature in the aeration tank; NH 4 Represents the ammonia content of the aeration tank influent, and SVI represents the corresponding volume of 1 gram of dry sludge after the mixed solution of the aeration tank has settled for 30 minutes. In addition to pH and T, the other units are mg / L. The output unit is ml / g. A total of 150 sets of data, of which 90 sets of data are used to t...

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Abstract

The invention discloses a sludge volume index (SVI) soft measuring method based on a self-organized T-S fuzzy nerve network and belongs to both the field of control and the field of sewage treatment. The accurate prediction of an SVI is the guarantee for normal operation of a sewage treatment process. The method comprises: first of all, taking the output quantity of a rule layer, i.e., the space activation intensity of the rule layer as a basis for determining whether a fuzzy rule is increased; secondly, on the basis of generating a new fuzzy rule, taking the output quantity of a membership function layer as a basis for determining whether a fuzzy set is increased; and finally obtaining a self-organized T-S fuzzy recursion nerve network by using a gradient decrease algorithm to adjust the weight value parameter of a model and the center value and width of a Gauss function, and establishing an SVI on-line soft measuring model based on an SOTSFEN such that real-time detection of the SVI is realized, and an effective method is provided for preventing sludge expansion.

Description

technical field [0001] The invention utilizes the self-organized T-S fuzzy recursive network to establish the soft measurement model of the sludge volume index SVI, and realizes the real-time prediction of the sludge settlement index SVI. The accurate prediction of the sludge settlement index SVI is the guarantee for the normal operation of the sewage treatment process, and the invention belongs to both the control field and the sewage treatment field. Background technique [0002] Sewage treatment is an important measure for the comprehensive utilization of water resources by the Chinese government, and it is also an important part of my country's sustainable development strategy. At present, urban sewage treatment plants have basically been established in cities and counties across the country, and the sewage treatment capacity is equivalent to that of developed countries such as the United States. However, the operation status of sewage treatment is not optimistic, and t...

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Application Information

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IPC IPC(8): G06N3/02G06N3/08G01N15/04
Inventor 乔俊飞许少鹏韩红桂
Owner BEIJING UNIV OF TECH
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