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Cobalt oxalate coarseness prediction technique in hydrometallurgy synthesis course

A synthesis process and hydrometallurgy technology, applied in the direction of electrical program control, comprehensive factory control, instruments, etc., can solve problems such as difficulty in meeting production requirements, large lag in manual testing, and difficult quality control of testing values

Inactive Publication Date: 2009-07-15
BEIJING GENERAL RES INST OF MINING & METALLURGY
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

The current method used by the factory is to measure the particle size by applying the bulk density, and it is obtained through offline analysis. The operator adjusts the production process according to the data after hours of analysis. The disadvantages of this method are: Manual testing has a large lag; in addition, it is difficult to directly apply the test value to quality control; the operator's operation can only be carried out according to their own experience, so that the particle size distribution of the product is uneven, and it is difficult to meet the production requirements

Method used

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  • Cobalt oxalate coarseness prediction technique in hydrometallurgy synthesis course
  • Cobalt oxalate coarseness prediction technique in hydrometallurgy synthesis course
  • Cobalt oxalate coarseness prediction technique in hydrometallurgy synthesis course

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Experimental program
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Effect test

Embodiment 1

[0204] Implementation of particle size prediction method on cobalt oxalate (fines) production line.

[0205] The specific implementation process is as follows:

[0206] 1) Auxiliary variable selection: The selection of auxiliary variables is the first step in establishing the soft sensor model. This step determines the input information matrix of the soft sensor, thus directly determining the structure and output of the soft sensor model. It is crucial. The selection of auxiliary variables includes the selection of variable type, the selection of variable number and the selection of detection point location.

[0207] During the synthetic process of cobalt oxalate, we select temperature (T), stirring speed (Np), cobalt chloride concentration (CB) and ammonium oxalate flow rate (V B ) is an auxiliary variable.

[0208] 2) Data collection and processing: Data collection was carried out at the site of the synthetic cobalt oxalate process. The specific measuring instruments are...

Embodiment 2

[0223] Implementation of the particle size prediction method on a cobalt oxalate (coarse material) production line.

[0224] With above-mentioned embodiment 1, first consider the situation of each position of production line, collect the data on the coarse material production line and then go through the following main modeling steps: 1) auxiliary variable selection; 2) data acquisition and processing; 3) simplified mechanism model calculation; 4 ) mixed model predictive calculation.

[0225] The cobalt oxalate (coarse material) particle size prediction model established according to this modeling method has been used in a cobalt oxalate production unit in a factory. In order to illustrate the effectiveness of the model, the calculated data were compared with the laboratory analysis values. The laboratory has 2 batches of average particle size test values ​​per day, and a total of 25 valid data were collected (with parking during the period). The curve comparison between the ...

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Abstract

The present invention provides a cobalt oxalate granularity real-time prediction method in hydrometallurgy synthesis process. The method comprises the steps of data collecting, auxiliary variable selecting, standardized processing, hybrid model establishing, etc. The invention is characterized in that a parallel-connected structure hybrid model composed based on mechanism model and based on data driving model is established. A genetic algorithm is adopted for confirming the related model parameter in the mechanism model. A model based on data driving is used as an error compensating model of mechanism model. The invention also provides a software system which actualizes the cobalt oxalate granularity prediction. The software system comprises a main program, a database and a human-machine interaction interface. The system software uses a microcomputer of hydrometallurgy synthesis process control system as a hardware platform. When the cobalt oxalate granularity real-time prediction method is used for the cobalt oxalate synthesis process of a certain hydrometallurgy factory for predicting the granularity of cobalt oxalate, the prediction result is in the preset error range. The cobalt oxalate granularity real-time prediction method according to the invention has the advantages of simple model, strong interpretability, good extrapolation property and higher prediction precision.

Description

technical field [0001] The invention belongs to the technical field of hydrometallurgy. In particular, a method for predicting the particle size of cobalt oxalate in a hydrometallurgical synthesis process is provided, that is, a method for real-time prediction of the average particle size of cobalt oxalate is provided. Background technique [0002] Hydrometallurgy technology is a new technology that is gradually mature and urgently needs industrialization. Compared with traditional pyrometallurgy, hydrometallurgy technology has the advantages of high efficiency, cleanness, and suitable for recovery of low-grade complex metal mineral resources. Especially in view of the characteristics of my country's mineral resources, which are rich in lean ore, complex symbiosis, and high impurity content, the industrialization of hydrometallurgical processes is of great significance for improving the comprehensive utilization of mineral resources, reducing solid waste production, and redu...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B19/418G05B13/04C07C55/07
CPCY02P90/02
Inventor 周俊武徐宁张学峰李传伟王振文于旭光
Owner BEIJING GENERAL RES INST OF MINING & METALLURGY
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