Underflow concentration prediction method based on thickener mechanism model

Abstract

The invention provides an underflow concentration prediction method based on a thickener mechanism model. The method comprises the steps of collecting thickener field data; converting fluid pressure into flow rate; using the parameter-identified layered thickener mechanism model with parameters to perform underflow concentration prediction. The thickener field data includes top volume flow, feeding flow, fluid pressure and underflow volume density. After the fluid pressure is converted into the flow rate, an abnormal value is processed by using a 3sigma principle. The thickener mechanism modelis established by the steps of collecting historical data of the thickener field data; establishing a thickener mechanism model; converting the fluid pressure into the flow rate and conducting data preprocessing; constructing the layered thickener mechanism model with parameters. The method reduces prediction errors brought by a pure mechanism model and improves the prediction accuracy of the mechanism model.

Description

Technical field [0001] The invention belongs to the field of metallurgical technology, and specifically relates to an underflow concentration prediction method based on a thickener mechanism model. Background technique [0002] With the large-scale, centralized and continuous production of the hydrometallurgical industry, there is an urgent need for efficient and stable automated production lines. The overall level of automation in my country's hydrometallurgical production process is relatively low, and its automation technology greatly restricts the development of my country's hydrometallurgical industry. At present, it is difficult to detect the underflow concentration of a concentrate thickener in Shandong. The operator relies on production experience to draw the ore, resulting in sharp fluctuations in the downstream filter press process, and the moisture content of the filter cake product is difficult to meet the standard. The tailings thickener is controlled by the operato...

AI Technical Summary

Problems solved by technology

At present, most of the following problems exist in the application process of thickeners in my country's mineral processing plants: many key variables in the production process have not yet been detected online; the production process of thickeners is still in a state of manual operation, and most production personnel rely on their own experience and feelings to judge Production conditions, so as to operate; the work load of the thickener, the concentration of the underflow water, the turbidity of the overflow water and other key links cannot be effectively controlled, resulting in relatively large fluctuations in its concentration and flow rate, which will affect the production indicators of the subsequent beneficiation process At the same time, it is likely to increase the dosage of chemicals in the subsequent flotation process, increase the cost of ore dressing, and seriously restrict the improvement of the production efficiency of the thickener

However, it faces a problem that is difficult to apply directly. Real-time prediction results cannot be obtained in the industrial field. At the same time, because the mechanism model has multiple uncertain parameters, how to identify the parameters of the mechanism model according to the actual process in the field is another problem.

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