Support vector machine method for measuring overflow granularity distribution of hydrocyclone for solid-liquid separation

Abstract

Disclosed is a support vector machine for measuring solid-liquid separation size-grade distribution of hydrocyclone, which is characterized in that: selecting feeding flow, feeding volume concentration, feeding pipe pressure, overflow concentration which relating to hydrocyclone and taking overflow grain size data obtained from offline analysis as auxiliary variable; matching with functional relations of auxiliary variables and program variables with computer to measure program variables.

Description

technical field [0001] The invention relates to measurement technology in automation technology. Specifically, it is a soft measurement method for an important index of a hydrocyclone, which is widely used in solid-liquid separation: the overflow particle size index. Background technique [0002] A hydrocyclone is a device for separating heterogeneous liquid mixtures, which uses centrifugal force to separate two or more phases based on the density difference between the two or more phases. Hydrocyclones are widely used in chemical, petroleum, ore dressing and other fields. According to the different substances to be separated, hydrocyclones can be divided into two types: solid-liquid separation and liquid-liquid separation. This article only discusses hydrocyclones used for solid-liquid separation. The following "cyclones" or "hydrocyclones" all refer to hydrocyclones used for solid-liquid separation. [0003] As a widely used solid-liquid separation equipment, the overfl...

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Problems solved by technology

The method proposed in patent A overcomes the shortcomings of the above two commonly used methods to a certain extent, but it also has certain shortcomings: 1. The connection between the ball mill and the classifier, the introduction of a large number of auxiliary variables, the introduction of small or repeated variables On the contrary, it will cause errors in the model; 2. Using the learning method based on empirical risk minimization requires a high number of samples for learning and updating, and it is difficult for companies that lack rapid overflow granularity index detection methods to meet the sample number requirements

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