Operation control method of high magnetic grading process

Abstract

The invention relates to an operation control method of a strong magnetic separation process, which belongs to the technical field of automatic control. According to the expected value of the strong magnetic separation grade index and the actual value of the basic control loop, the set value of the basic control loop of the strong magnetic separation process is optimally controlled. Improve the concentrate grade and reduce the tailings grade. The invention ensures that the system operates at the working point corresponding to the expected value of the concentrate grade and the expected value of the tailings grade, uses the neural network to perform real-time prediction of the concentrate grade and the tailings grade that cannot be measured online, and adopts a dynamic compensation method to effectively suppress various unknown disturbances The adverse effect on the sorting process, so as to effectively improve the grade of the concentrate while reducing the grade of the tailings, and improve the metal recovery rate of the final product of the beneficiation. This method can be realized through a software system, which realizes the separation of software function modules and algorithm files, has a low degree of decoupling, is easy to expand and maintain algorithms, and provides technical support and an operating platform for the research on the operation control method and system design of the strong magnetic separation process. .

Description

technical field [0001] The invention belongs to the technical field of automatic control, and in particular relates to an operation control method for a strong magnetic separation process. Background technique [0002] The magnetic separation process is an important link in the hematite beneficiation production process. It is the last process that affects the quality of the beneficiation product and directly determines the quality of the final product. Its main task is to separate the ore pulp after the grinding process into qualified grades. concentrates and tailings. [0003] Among them, the most important factors affecting the production index of the strong magnetic separation process are the flushing and rinsing water flow rate of the basic control circuit (rougher rinsing water flow rate, first-sweeping rinse water flow rate, second-sweeping rinse water flow rate), excitation current (rougher excitation current, sweeping excitation current), sweeping feeding concentrat...

AI Technical Summary

Problems solved by technology

Due to the complex dynamic characteristics such as strong nonlinearity, time-varying, and frequent changes in boundary conditions between the grade index and the basic control loop process variable, it is difficult to describe it with an accurate mathematical model, and the grade granularity index cannot be continuously detected online. Currently, the actual field can only be realized. The basic loop control of ore washing and rinsing water flow, excitation current and sweeping feed concentration, but it is difficult to use the optimal control method to give the set value of the basic control loop in real time according to the process index concentrate grade and tailings grade goals

In the prior art, most of the research on the control of the strong magnetic separation process assumes that the ideal basic control loop set value can be obtained, and focuses on improving the effect of feedback control. Neglecting the feedback control that deviates from the ideal set point cannot achieve a good operation of the system. In recent years, many researchers have begun to study the optimal control system of the set value of the strong magnetic separation process loop. However, due to the lack of real-time detection of indicators in these systems, it is difficult to timely and effectively suppress the adverse effects of various unknown disturbances on process indicators. As a result, the system is in open-loop control for a long time, and it is difficult or impossible to make the magnetic separation process run in the best state, so that the quality of the product cannot be guaranteed, resulting in waste of raw materials and a decrease in production efficiency

[0004] Traditional optimization control software is mostly a dedicated or closed system with poor algorithm scalability, and the algorithm model embedded in the system is deeply coupled with the development environment, so it cannot become a truly independent and mutually portable industry public resource

The entire system needs to be recompiled, linked and debugged for every minor modification, which is not conducive to system maintenance

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