Concentrate grade prediction method in zinc flotation process

Abstract

The invention provides a concentrate grade prediction method in a zinc flotation process. The concentrate grade prediction method comprises the following steps: firstly, collecting zinc flotation process image feature data and corresponding concentrate grade data through a flotation process image collecting and processing system, and preprocessing the collected data; forming a sample space according to the preprocessed zinc flotation image feature data and concentrate grade data, then dividing five independent sub-sample spaces according to the concentrate grade, and respectively performing time difference; performing correlation analysis on the features by adopting KPCA, and extracting features with high contribution rate as key foam features; training an LSSVM based on the key feature sample, and establishing a relationship between the image features and the concentrate grade; and solving the prediction model by using an improved flora algorithm, and optimizing two parameters of a penalty factor xi and a kernel width sigma of the LSSVM. The concentrate grade prediction method can be directly implemented by programming on a computer, is low in cost, high in precision and good in timeliness, and has important significance for guiding field production.

Description

technical field [0001] The invention belongs to the technical field of froth flotation, and in particular relates to a method for predicting the concentrate grade in the zinc flotation process. [0002] technical background [0003] Foam flotation is one of the most important beneficiation methods in current lead-zinc smelting. The flotation method is a method of sorting minerals by using the different physical and chemical properties of the surface of mineral particles to lead to different hydrophilicity. practical value. However, because the flotation process is long, the internal mechanism is not clear, there are many influencing factors, many variables are involved and the nonlinearity is serious, many process indicators cannot be detected online. Predicting the obtained concentrate grade and using it to complete the on-site operation is highly subjective and relies too much on experience and knowledge. It is difficult to achieve accurate and stable prediction of the con...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the difficulty of on-line detection of the concentrate grade in the flotation process and the insufficiency of the prior art in zinc flotation concentrate grad...

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