Research on phosphorite photoelectric sorting process based on fuzzy algorithm

In this paper, we synthesize the differences in grayscale and texture information of phosphorite X-ray images and extract a total of six feature quantities under their grayscale and texture features, respectively. After feature fusion, a fuzzy support vector machine classifier with a normal plane-type affiliation function is used to optimize the binary classification method of traditional support vector machines, which improves the accuracy of classification. To improve the sorting model’s division of the optimal classification hyperplane, the centroid position of the two types of samples is considered comprehensively. In order to give full play to the classification performance of the sorting model, the particle swarm algorithm is further used to optimize the core parameters of the fuzzy support vector machine, the penalty factor, and the kernel parameters to complete the construction of the classifier model. In the face of the mixed colloidal phosphorite that can not be exploited, based on the PSO-NP-FSVM photoelectric sorting operation, the concentrate yield rate, operation recovery rate, and optical sorting waste rate are 56.52%, 20.87%, and 71.17%, respectively. After photoelectric sorting, the grade of concentrate P₂ O₅ can be increased to 20.98%, which can meet the requirements for the grade of the processing plant. Therefore, the photoelectric sorting process combined with a fuzzy algorithm is capable of meeting the actual production requirements.