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| Intelligent identification and application of ore size distribution based on machine vision |
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Received:November 27, 2022
Revised:December 22, 2022
Accepted:December 30, 2022
Published Online:August 22, 2023
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| DOI： |
| KeyWord:semi-autogenous mill; ore lumpiness; image analysis |
| Author | Institution |
| 陆 |
Anhui Tongguan Lujiang Mining Co,Ltd,Hefei |
| Wang Xiaochun |
Anhui Tongguan Lujiang Mining Co,Ltd,Hefei |
| 陆 |
BGRIMM Technology Group |
| Liu Daoxi |
BGRIMM Technology Group |
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| Abstract: |
| In the semi-autogenous grinding operation, the ore feed of the semi-autogenous grinding machine is usually solid materials with a wide particle size distribution. The material in the cylinder is used as the grinding medium, and the continuous and strong impact grinding and peeling is carried out in the cylinder. In order to achieve the purpose of grinding. Therefore, the influence of ore particle size characteristics and ore grindability on the semi-autogenous grinding process is much greater than that of the conventional crushing process. By analyzing the particle size characteristics of the ore on the ore feeding belt, the real-time detection of the ore lumpiness is realized from the source of the ore feeding. Based on the multi-scale ore image analysis system, this paper collects the images of semi-autogenous grinding feed ore in real-time, and realizes the effective segmentation of ore lumps and particle size statistics through image processing technology, which provides data support for the control of semi-autogenous grinding feed ore lumps. |
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