Hesitant fuzzy c-means clustering method for copper removal process based on state transition algorithm

Abstract

The hesitant fuzzy c-means clustering method for the copper removal process based on the state transition algorithm comprises the steps that parameters of the state transition algorithm are set basedon data of a copper removal cascade reaction kettle, and distance measurement weights are initialized; obtaining a clustering center and a membership matrix of the data of the copper removal cascade reaction kettle through fuzzy c-means clustering simulation based on the distance measurement weight; and establishing a target model according to the clustering center and the membership matrix, and outputting an optimization result after simulation. According to the method, clustering analysis is carried out on the data of the zinc hydrometallurgy copper removal technological process, the data with security threats can be well judged, meanwhile, the optimal weight result can be obtained quickly and accurately, and the method has important significance in parameter identification and optimization control of the whole copper removal technological process.

Description

Technical field [0001] The present invention relates to the technical field of copper removal process data mining, and in particular to a hesitant fuzzy c-means clustering method for copper removal based on a state transition algorithm. Background technique [0002] In the industrial process of non-ferrous metallurgy hydrometallurgical zinc smelting process, purification and removal of copper is one of the very important processes. Copper ions are the impurity ions with the highest content in zinc sulfate solution and harmful to the subsequent electrolysis process, so they need to be removed first in the purification section . The copper removal process removes copper ions by adding zinc powder to two consecutive reactors, but a certain amount of copper ions needs to be properly retained as the activator for the subsequent cobalt removal section, so it is necessary to ensure the copper ion concentration at the outlet of the final reactor Keep it stable and within the qualified r...

AI Technical Summary

Problems solved by technology

[0017] The traditional fuzzy c-means uses a single similarity measure, that is, the Euclidean distance as the similarity measure. Using the Euclidean distance as the similarity measure of the fuzzy c-means algorithm is unstable, and it is likely that the final clustering result and cluster center

For the same industrial data set, the clustering results obtained by using other similarity measures, such as Minkowski distance, Chebyshev distance, Manhattan distance and Gaussian kernel function, are different, and it is difficult to judge these similarity measures. which is better

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