Sewage treatment soft measuring method based on RVM

A correlation vector machine and sewage treatment technology, applied in the direction of electrical digital data processing, special data processing applications, instruments, etc., can solve the problems of not being able to respond to the situation of the sewage treatment site in time, the delay of the sewage control system, and the inability to perform.

Inactive Publication Date: 2014-05-14
SOUTH CHINA UNIV OF TECH
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Problems solved by technology

[0004] The current sewage treatment generally adopts the dilution method and sensors to measure the concentration of BOD and COD in sewage. However, due to the long period of analysis and determination of these two indicat

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  • Sewage treatment soft measuring method based on RVM
  • Sewage treatment soft measuring method based on RVM
  • Sewage treatment soft measuring method based on RVM

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Embodiment Construction

[0063] The present invention will be further described in detail below in conjunction with the embodiments and the accompanying drawings, but the embodiments of the present invention are not limited thereto.

[0064] Such as figure 1 , a soft-sensing method for sewage treatment based on a correlation vector machine, including the steps in the following sequence:

[0065] S1. Use the fuzzy monotonically increasing dependence algorithm to reduce the attributes of the collected sewage input data, and quantitatively analyze the input attributes that have a greater impact on chemical oxygen demand COD and biochemical oxygen demand BOD;

[0066] The fuzzy monotonically increasing dependence algorithm specifically includes the following steps:

[0067] A. Use a two-dimensional array to store the decision table D[n, m], where the mth column is the decision attribute, and the 1st to m-1th columns are conditional attributes;

[0068] B. Sort the decision attributes from small to large...

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Abstract

The invention discloses a sewage treatment soft measuring method based on an RVM. The method sequentially comprises the steps that a fuzzy monotone increasing dependence algorithm is used for conducting attribute reduction on collected sewage input data, and an input attribute which has a great influence on COD and BOD is quantificationally analyzed; a predication model is established through the RVM and the collected sewage input data, model parameters are optimized, and accordingly an optimal predication model is established; the input attribute which has the great influence on COD and BOD is determined through the collected sewage input data; sewage sample data to be predicted are predicted; the sewage input data after attribute reduction is used as the input of a soft measuring model of the well-trained RVM, namely the output of the model is the prediction result of effluent COD and BOD. By the adoption of the method, prediction accuracy is high, and needed time is short.

Description

technical field [0001] The invention relates to the field of sewage treatment, in particular to a soft-sensing method for sewage treatment based on a correlation vector machine. Background technique [0002] Wastewater treatment is an integral part of economic development and water conservation. With the rapid growth of the national economy, the amount of sewage discharge has also increased significantly. However, there are too few sewage treatment plants and the treatment cycle is too long, which is far from meeting the national requirements for environmental protection. At the same time, the country has increased investment in environmental protection, and sewage treatment technology has received more and more attention. The national development plan clearly states that it is necessary to develop and promote low-energy and effective sewage treatment technologies. [0003] In the sewage discharge standard, the parameters to measure whether the standard is met include: che...

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Application Information

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IPC IPC(8): G06F19/00
Inventor 许玉格曹涛罗飞宋亚龄张雍涛
Owner SOUTH CHINA UNIV OF TECH
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