Thickener underflow concentration prediction method based on integrated learning

Abstract

The invention provides a thickener underflow concentration prediction method based on integrated learning, and belongs to the technical field of mining. The method comprises the following steps: obtaining actual production historical record data, storing the actual production historical record data in an enterprise database, then preprocessing the obtained data set, and constructing a training setand a test set by using the preprocessed data; and an integrated learning method is adopted, the constructed training set and test set are utilized to establish a model, accurate prediction of the underflow concentration of the deep cone thickener is realized, and finally, a prediction result is displayed through a visual platform. According to the method, most factors influencing the underflow concentration can be comprehensively considered, so that the bottleneck problem of insufficient one-sided consideration when an existing underflow concentration prediction model considers the influencefactors is solved. And an integrated learning model is used, so that the problems that a single machine learning model is limited in learning capability and large-scale data cannot be processed are solved, and more effective and accurate reference is provided for control of the thickener.

Description

technical field [0001] The invention relates to the field of mining technology, in particular to a method for predicting the underflow concentration of a thickener based on integrated learning. Background technique [0002] In mine production, goaf collapse and tailings dam failure are the two main causes of mine disasters. In order to ensure mining safety, the paste filling mining method is widely used by countries all over the world because of its fast solidification speed and high filling strength, which can effectively control the above-mentioned disasters, and is economical and environmentally friendly. Paste filling is to thicken the whole tailings with high content of fine particles to make a paste slurry without segregation, precipitation and dehydration, and then pump it downhole for filling. Tailings thickening is the primary link in the paste filling process. The deep cone thickener is suitable for processing fine particle materials. It has the advantages of simp...

AI Technical Summary

Problems solved by technology

However, in the case of a large amount of data, only the case where the statistical ratio accounts for the vast majority can only be considered. The prediction of underflow concentration requires millions of pieces of data to be processed, and most of the data set is the process of preparing paste during normal operation of the equipment. At the same time, it also includes several downtime and restart data. Obviously, this traditional single machine learning model is not only unable to analyze and predict the entire thickening process, but also has an unsatisfactory prediction effect on the underflow concentration in abnormal situations.

In the actual production process, the abnormality of the underflow concentration cannot be found in time, and it is easy to ignore the safety hazards in the production

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