Thickener underflow concentration prediction method based on integrated learning

An integrated learning and concentration prediction technology, which is applied in the mining field, can solve problems such as inability to analyze and predict, inability to find underflow concentration in time, and unsatisfactory underflow concentration prediction effect, so as to improve generalization ability, fast training speed and high prediction accuracy Effect

CN109784561AInactive Publication Date: 2019-05-21UNIV OF SCI & TECH BEIJING
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Publication Date
2019-05-21
Estimated Expiration
Not applicable · inactive patent

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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.
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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...

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Embodiment Construction

[0041] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will describe in detail with reference to the drawings and specific embodiments.

[0042] The invention provides a thickener underflow concentration prediction method based on integrated learning.

[0043] Such as figure 1 As shown, the method includes the following steps:

[0044] S1: Data Acquisition: Obtain actual production history data, which is recorded by the mine's automation system and stored in the enterprise database;

[0045] S2: Data preprocessing: Preprocess the data obtained in S1, remove irrelevant attributes, and then perform feature selection to obtain the preprocessed data set;

[0046] S3: Construct training set and test set: use the preprocessed data set in S2 to construct training set and test set;

[0047] S4: Establish a prediction model: use the integrated learning method, use the training set and test set c...