<abstract xmlns="http://www.w3.org/1999/xhtml">

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.
</abstract>

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.

Research on phosphorite photoelectric sorting process based on fuzzy algorithm

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"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...

Research on phosphorite photoelectric sorting process based on fuzzy algorithm

Pubblicato online: 15 mag 2024

Pagine: -

Ricevuto: 06 feb 2024

Accettato: 18 apr 2024

DOI: https://doi.org/10.2478/amns-2024-1085

Parole chiave
X-ray, Image processing, Fuzzy support vector machine, Particle swarm algorithm, Photoelectric sorting

© 2024 Kunyi Luo et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on phosphorite photoelectric sorting process based on fuzzy algorithm

Pubblicato online: 15 mag 2024

Pagine: -

Ricevuto: 06 feb 2024

Accettato: 18 apr 2024

DOI: https://doi.org/10.2478/amns-2024-1085

Parole chiaveX-ray, Image processing, Fuzzy support vector machine, Particle swarm algorithm, Photoelectric sorting

© 2024 Kunyi Luo et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
X-ray, Image processing, Fuzzy support vector machine, Particle swarm algorithm, Photoelectric sorting