Multi-gaussian kernel self-optimization relevance vector machine based wastewater quality soft-measurement method

A correlation vector machine and self-optimization technology, applied in the field of soft sensing, can solve problems such as inability to perform, inability to respond to sewage treatment site conditions in a timely manner, and long cycle times

Active Publication Date: 2015-09-16
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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  • Multi-gaussian kernel self-optimization relevance vector machine based wastewater quality soft-measurement method
  • Multi-gaussian kernel self-optimization relevance vector machine based wastewater quality soft-measurement method
  • Multi-gaussian kernel self-optimization relevance vector machine based wastewater quality soft-measurement method

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Embodiment Construction

[0080] The present invention will be further described below in conjunction with specific examples.

[0081] The sewage water quality soft-sensing method based on the multi-Gaussian kernel self-optimization correlation vector machine described in this embodiment determines the kernel parameters on each scale through the self-optimization method, uses the genetic optimization algorithm to optimize the initial parameters, and establishes the optimal model. Under the condition of ensuring the convergence and sparsity of the model, the output accuracy of BOD in sewage can be effectively improved. The details are as follows:

[0082] 1) Eliminate the abnormal points in the data of sewage input and output, and normalize them to the [0,1] interval due to the different dimensions of each input variable;

[0083] 2) Multi-Gaussian kernel function correlation vector machine soft sensor model module, its calculation formula:

[0084] Given a set of input and target values x n ∈R M ...

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Abstract

The invention discloses a multi-gaussian kernel self-optimization relevance vector machine based wastewater quality soft-measurement method. The method comprises the following steps: (1) removing abnormal data from wastewater input and output data, performing normalization processing due to different dimensions of input variables and normalizing to be within an interval from 0 to 1; (2) forming a multi-gaussian kernel function relevance vector machine soft-measurement model module; (3) performing multi-gaussian kernel function nuclear parameter self-optimization algorithm; (4) forming an initial parameter optimization module by the genetic optimization algorithm; and (5) realizing modeling of a multi-gaussian kernel function relevance vector machine soft-measurement model. According to the method, nuclear parameters of all dimensions can be determined by a self-optimization method, the initial parameters are optimized by the genetic optimization algorithm, an optimal model is built, the output precision of BOD (Biochemical Oxygen Demand) in wastewater is effectively improved under conditions that the convergence and the sparseness of the model are ensured.

Description

technical field [0001] The invention relates to the technical field of soft measurement, in particular to a method for soft measurement of sewage water quality based on a multi-Gaussian kernel self-optimizing correlation vector machine. Background technique [0002] With the growth of the global economy and the improvement of people's living standards, the discharge of urban domestic sewage and industrial sewage is increasing day by day, and the limited water resources are polluted to varying degrees. It is human beings to prevent water pollution and protect the water environment on which people live. It is imperative. An important aspect of protecting water resources is to improve the technical level and detection methods of sewage treatment. The sewage treatment process is quite complex, with the characteristics of time-varying parameters, multi-variable coupling, strong nonlinearity, and serious lag. It makes it difficult to measure the quality of effluent water online,...

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Application Information

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IPC IPC(8): G01N33/18
Inventor 许玉格刘莉
Owner SOUTH CHINA UNIV OF TECH
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