A vision-based method for detecting and controlling slurry flow in a spiral concentrator.

By constructing a slurry edge recognition model in a spiral concentrator, using image segmentation and Kalman filtering algorithms to identify the slurry edge position, and combining this with a geometric relationship model to calculate the flow rate, the problem of low flow control accuracy in existing technologies is solved, and automated and precise slurry flow rate adjustment is achieved.

CN122089645APending Publication Date: 2026-05-26JIANGXI UNIV OF SCI & TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
JIANGXI UNIV OF SCI & TECH
Filing Date
2025-12-30
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

In existing technologies, the determination and adjustment of slurry flow rate in spiral concentrators rely on the operator's experience, resulting in large errors, low control precision, and an inability to achieve stable and accurate flow control.

Method used

By acquiring images of the slurry at the inlet of the spiral concentrator, a slurry edge recognition model is constructed. The edge position of the slurry is identified using an image segmentation algorithm based on UNet and a target tracking algorithm based on Kalman filtering. Combined with a geometric relationship model, the slurry flow rate is calculated to achieve automated flow control.

Benefits of technology

It achieves precise automatic control of slurry flow, improves the accuracy of judgment and adjustment, reduces reliance on human experience, and improves the efficiency and accuracy of the entire process.

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Abstract

This invention relates to the field of gravity mineral processing technology, and provides a vision-based method for detecting and controlling the slurry flow rate of a spiral concentrator. The method includes: acquiring slurry images at the inlet of the spiral concentrator; extracting the edge geometric features of each slurry image to construct a slurry edge feature image sample set; constructing and training a slurry edge recognition model based on the sample set; identifying the slurry edge position using the recognition model; constructing a geometric relationship model between the slurry edge and the outer edge of the spiral concentrator using the identified slurry edge position; calculating the geometric distance from the slurry edge to the outer edge of the spiral concentrator using the geometric relationship model; determining the slurry flow rate using the geometric distance, and controlling the slurry flow rate. This invention enables visual determination of the slurry flow rate in a spiral concentrator, efficiently performing automatic control of the slurry flow rate without relying on human experience and subjective judgment, thus significantly improving the accuracy of the determination.
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