Method for predicting dissolved oxygen concentration of sewage based on fuzzy clustering supporting vector regression algorithm

A technology of support vector regression and dissolved oxygen concentration, which is applied in the field of big data analysis, can solve problems such as high time complexity, poor prediction accuracy, and high computational complexity, and achieve improved prediction efficiency, short modeling time, and good comprehensive performance Effect

Inactive Publication Date: 2019-01-04
HEFEI UNIV
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Problems solved by technology

[0004] The purpose of the present invention is to solve the defects of high computational complexity, high time complexity, and poor prediction accuracy in the online prediction process of the traditional prediction method for the content of dissolved oxygen DO in sewage, and to provide a method based on fuzzy clustering support Prediction Method of Sewage Dissolved Oxygen Concentration Based on Vector Regression

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  • Method for predicting dissolved oxygen concentration of sewage based on fuzzy clustering supporting vector regression algorithm
  • Method for predicting dissolved oxygen concentration of sewage based on fuzzy clustering supporting vector regression algorithm
  • Method for predicting dissolved oxygen concentration of sewage based on fuzzy clustering supporting vector regression algorithm

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[0038] In order to make the technical means, creative features, goals and effects achieved by the present invention easy to understand, the present invention will be further described below in conjunction with specific illustrations.

[0039] Such as figure 1 As shown, the sewage dissolved oxygen concentration prediction method based on fuzzy clustering support vector regression of the present invention comprises the following steps:

[0040] The first step is the collection of historical water quality data. In this example, the research data of this example comes from the annual sewage index data of a sewage treatment plant in Hefei City, Anhui Province in 2017. The data is collected every minute, and a total of 46,000 sets of samples have been collected. data. Among them, the water quality parameters are PH, MLSS, ORP, influent NH4N, effluent COD, effluent TP, influent cumulative flow, and effluent cumulative flow.

[0041] After correlation analysis, these seven indicator...

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Abstract

The invention discloses a method for predicting the dissolved oxygen concentration of sewage based on fuzzy clustering supporting a vector regression algorithm, which is used for predicting the content of dissolved oxygen DO in the sewage. Aiming at the problem that the dissolved oxygen is difficult to measure in real time in a sewage treatment process, firstly, a whole sample is divided into a plurality of sub-samples through fuzzy clustering, then, a support vector regression model is established on each sub-sample, and then integration is carried out, and on-line prediction on the content of the dissolved oxygen DO in the sewage is carried out. The method has higher prediction precision, and is superior to other time series prediction methods in the aspect of comprehensive performance,and provides an effective solution for quickly and accurately predicting water quality.

Description

Technical field: [0001] The design of the invention relates to the technical field of big data analysis, specifically a method for predicting dissolved oxygen concentration in sewage based on fuzzy clustering support vector regression. Background technique: [0002] With the increasingly tight water resources and the increasingly serious pollution of the water environment, the problem of sewage treatment has attracted more and more attention. Dissolved oxygen (DO) is one of the important indicators for evaluating water quality. However, the existing DO prediction method has a small training sample size, does not consider the lag in the measurement of influent parameters, does not consider the time parameter, and the generalization ability of the model is not strong. For massive The data will be underfitting, resulting in poor prediction accuracy. Therefore, the method of simply passing the sensor is not suitable for real-time monitoring, so we use the method of support vect...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16C20/70G16C10/00G06K9/62
CPCG06F18/2321G06F18/2411
Inventor 王晓峰施星靓周建邹乐
Owner HEFEI UNIV
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