Discrimination Method of Zinc Flotation Working Conditions Based on Improved Adaptive Multi-Population Genetic Algorithm

Abstract

The invention discloses a zinc flotation working condition discrimination method based on an improved self-adaptive multi-population genetic algorithm, which is applied in the froth flotation process, and the foam image feature data set obtained in real time is used as the initial training population, and experienced workers are added On this basis, taking into account the subjectivity of manual selection and the degree of influence by the environment, add subjective factor coefficients, that is, the subjective judgment can be offset, and re-evaluate to determine whether the current selection is within the allowable range of error. In this way, the human subjective influence is controlled to a certain range. During the whole process, the current good working conditions are fed back in real time, and the population adjustment is carried out with the working conditions returned in real time, so as to realize the real-time refresh of the optimal solution and reduce the influence of changes in the grade values ​​of different raw ore piles, so as to achieve the final adjustment of the grade value and Effective control of recovery rate.

Description

technical field [0001] The invention relates to a zinc flotation working condition discrimination method based on an adaptive multi-population genetic algorithm for selecting the corresponding optimal solution under the optimal working condition in the froth flotation process. Background technique [0002] In the foam flotation process, the genetic algorithm can effectively solve the optimal value problem or the optimal interval problem, and then effectively discriminate the working conditions. In the initial traditional genetic algorithm, the initial population is randomly generated, and the search rate is reduced. It is easy to fall into a local optimal state, and secondly, only the difference in individual fitness is considered, but the difference in the evolutionary state of the population is not considered. [0003] Apply the multi-population genetic algorithm to the foam flotation process, set the immigration operator, successively migrate from the previous population ...

AI Technical Summary

Problems solved by technology

Among the existing algorithms, some ignore the subjective initiative of human beings and the importance of artificial experience, and some make full use of human experience, but ignore the influence of human subjectivity and the large fluctuations in human judgment. Inevitably wrong decisions, without finding effective ways to solve the impact of human subjectivity, or without taking any measures to evaluate and compensate

[0004] Secondly, in the existing algorithm, the initial population has not changed, and the optimal value can only be obtained for objects with stable raw ore grade values. For objects with fluctuating raw ore grade values, the calculated optimal value is far from the real The offset error of the optimal value is large

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