Operation control method of high magnetic grading process

Abstract

An operation control method of a high magnetic grading process belongs to the technical field of automatic control and comprises the following steps of: performing optimal control on a set value of a basic control loop of the high magnetic grading process according to an expected value of a high magnetic grading index and an actual value of the basic control loop to improve the concentrate grade and lowering the tailing grade. With the adoption of the method, a system is ensured to run on working points corresponding to the expected value of concentrate grade and the expected value of the tailing grade, the concentrate grade and the tailing grade which cannot be measured on line are predicated in real time by a neural network, the poor effect to the grading process, caused by each unknown interference, is inhibited effectively by using a dynamic compensation method, in this way, the concrete grade is improved, meanwhile, the tailing grade is lowered, and thus the metal recovering rate of a final grading product is increased. The method can be realized by a software system; with the adoption of the software system, the separation of a software functional module and an algorithm is realized, the de-coupling degree is low, the algorithm is easy to expand and maintain, and a technical support and a running platform are provided to the research and the system design of the operation control method of the high magnetic grading 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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