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Intelligent ore pulp concentration detection method based on combination of identification model and deep learning

An identification model and deep learning technology, applied in biological neural network models, measuring devices, scientific instruments, etc., can solve the problem that the accuracy of the differential pressure concentration meter is difficult to meet the requirements of mineral processing production, cannot compensate the error of the pulp concentration, and does not consider the dynamics of the flowing pulp Process and other issues to achieve accurate measurement, good adaptability and accuracy, and improve production efficiency

Active Publication Date: 2021-11-05
NORTHEASTERN UNIV
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Problems solved by technology

At present, most of the differential pressure concentration meters on the market do not consider the dynamic process of the flowing pulp, and cannot compensate the pulp concentration error, which makes it difficult for the accuracy of the differential pressure concentration meter to meet the production requirements of mineral processing

Method used

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  • Intelligent ore pulp concentration detection method based on combination of identification model and deep learning
  • Intelligent ore pulp concentration detection method based on combination of identification model and deep learning
  • Intelligent ore pulp concentration detection method based on combination of identification model and deep learning

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Embodiment 1

[0062] Such as figure 1 and figure 2 As shown, this embodiment provides an intelligent detection method for pulp concentration based on a combination of identification model and deep learning, the execution subject of which is a control device / computer, and the intelligent detection method for pulp concentration may include:

[0063] Step 101, obtaining real-time process data of the slurry flowing in the pipeline;

[0064] Step 102. Obtain the pulp density information according to the real-time data and the pre-established pulp density detection model;

[0065] Step 103, according to the pulp density-concentration conversion relationship and the pulp density information, obtain the detection result of the pulp concentration;

[0066] For example, the pulp density-concentration conversion relationship can be:

[0067] δ is the true density of the ore, is the slurry concentration.

[0068] In this embodiment, the pulp density detection model includes: the density value ...

Embodiment 2

[0093] In order to better understand the technical solution of the first embodiment above, the following combination Figure 1 to Figure 7 The intelligent detection method of the pulp concentration is described in detail.

[0094] The pulp concentration intelligent detection method of the present embodiment may comprise the following steps:

[0095] 201: Based on the law of conservation of momentum of the fluid, determine the momentum equation of the pulp flowing in the vertical pipeline to establish a dynamic model of the pulp density, and determine the input variables and output variables of the dynamic model of the pulp density based on the established dynamic model of the pulp density.

[0096] The dynamic model of the pulp density in this embodiment consists of an identifiable model and an unknown nonlinear dynamic system. combine figure 1 The schematic structure of the micro-element control body and pipeline is shown. According to the law of conservation of momentum, ...

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Abstract

The invention relates to an intelligent ore pulp concentration detection method based on combination of an identification model and deep learning. The method comprises the steps of 101, acquiring real-time process data of flowing of ore pulp in a pipeline; 102, acquiring ore pulp density information according to the real-time data and a pre-established ore pulp density detection model; and 103, obtaining a detection result of the ore pulp concentration according to an ore pulp density-concentration conversion relation and the ore pulp density information, wherein the ore pulp density detection model comprises a density value, which is estimated by adopting a least square method, of an identifiable model and a deep learning model which corresponds to an unknown nonlinear dynamic system and is modeled by adopting LSTM; and the identifiable model and the unknown nonlinear dynamic system are established based on basic information of ore pulp and a momentum conservation rule. By means of the method, the ore pulp concentration can be accurately measured, and then real-time control over the ore pulp concentration is achieved.

Description

technical field [0001] The invention belongs to the technical field of industrial artificial intelligence, and in particular relates to an intelligent detection method for pulp concentration based on a combination of an identification model and deep learning. Background technique [0002] In the industrial production process, the pulp concentration is an important parameter in the beneficiation process. have important influences on energy and so on. Therefore, in industrial processes such as metallurgy and mineral processing, it is often necessary to detect the pulp concentration in real time, so as to facilitate the adjustment of production operations or realize automatic control. For a long time, reliable real-time detection of pulp concentration has been a very difficult problem. At present, the main detection methods include concentration pot detection, drying method detection, radioactive source concentration meter, ultrasonic concentration meter and differential pres...

Claims

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Application Information

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IPC IPC(8): G01N9/36G06N3/04
CPCG01N9/36G06N3/044
Inventor 柴天佑牟晓迪韩先尧王兰豪
Owner NORTHEASTERN UNIV
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