A data-driven dynamic operation control method for rare earth extraction process

Abstract

The invention belongs to the technical field of rare earth cascade extraction automatic control technology, and particularly relates to a data-driven dynamic operation control method for the rare earth extraction process. By using the Least Square Support Vector Machine (LSSVM) to establish a soft sensor model of rare earth element component content, a clustering-based LSSVM sparsity improvement method and a dynamic update method of the LSSVM model were proposed. Based on the improved LSSVM proposes a dynamic operation optimization method for the rare earth cascade extraction production process to determine the optimal adjustment amount of each control variable when a disturbance occurs, thereby achieving dynamic and precise control of the content of rare earth element components, thereby improving the quality of rare earth products. . The invention can stabilize and improve the quality of rare earth products.

Description

Technical field [0001] The invention belongs to the technical field of rare earth cascade extraction automatic control technology, and particularly relates to a data-driven dynamic operation control method of a rare earth extraction process. Background technique [0002] Rare earths contain 17 elements including chromatic elements, scandium and yttrium. These elements are called strategic elements of the new century and have been widely used in machinery, metallurgy, petrochemical, aerospace and other industrial fields. At present, the hydrometallurgy based on organic solvent extraction is widely used in my country to extract rare earth elements. The process and control flow chart of rare earth cascade extraction process is as follows figure 1 Shown. The aqueous rare earth feed liquid F001 containing separated components A and B (A is easy to extract and B is difficult to extract) is added to the extraction tank from the nth stage (feeding stage), and the extractant F002 is from...

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Problems solved by technology

[0004] In order to overcome the defects of the existing rare earth cascade extraction production process component content prediction and product quality control methods, the present invention provides a data-driven rare earth cascade extraction process dynamic operation control method, by using the least squares support vector machine (Least Square Support Vector Machine, LSSVM) establishes a soft sensor model for the content of rare earth elements, proposes a method for improving the sparsity of LSSVM based on clustering and a dynamic update method for the LSSVM model, and proposes a cascade extraction method for rare earth elements based on the improved LSSVM The dynamic operation optimization method of the production process is to determine the optimal adjustment amount of each control variable when disturbance occurs, so as to realize the dynamic and precise control of the content of rare earth element components, and then improve the quality of rare earth products

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