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投稿时间：2022-11-27 修订日期：2022-12-22
投稿时间：2022-11-27 修订日期：2022-12-22
中文摘要: 在半自磨磨矿作业中,半自磨机的给矿通常是粒级分布较宽的固体物料,利用筒体内的物料自身做磨矿介质,在筒体内进行连续强烈地冲击磨剥以达到粉磨的目的。所以给矿粒度特性以及矿石可磨性对半自磨过程的影响远大于常规碎磨流程,矿石粒级(块度)分布是一个表征碎矿状态的关键检测工艺参数。通过对给矿皮带上矿石图像进行矿石的粒度特性分析,从给矿源头上实现矿石块度实时检测。本文基于多尺度的矿石图像分析系统,实时采集半自磨给矿矿石图像,通过图像处理技术,实现矿石块度有效分割,以及粒级统计,为半自磨给矿矿石块度控制提供数据支撑。
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.
keywords: semi-autogenous mill ore lumpiness image analysis
文章编号：20221127001 中图分类号： 文献标志码：
基金项目:智能(可穿戴)选冶过程数据感知元器件与产品；(国家重点研发计划(2021YFC2902702))第一作者姓名,朱颖舟(1987- ),男,自动化工程师,主要从事选矿自动化及优化控制工作。E-mail：382134449@qq.com通信作者陆博, E-mail：lu_bo@bgrimm.com 基于机器视觉的矿石块度智能识别及应用
| 作者 | 单位 | |
| 朱颖舟 | 安徽铜冠（庐江）矿业有限公司 | 382134449@qq.com |
| 汪晓春 | 安徽铜冠（庐江）矿业有限公司 | lu_bo@bgrimm.com |
| 陆博* | 矿冶集团 | lu_bo@bgrimm.com |
| 刘道喜 | 矿冶集团 | lu_bo@bgrimm.com |
引用文本：
朱颖舟,汪晓春,陆博,刘道喜.基于机器视觉的矿石块度智能识别及应用[J].矿冶,2023,32(4):.
朱颖舟,汪晓春,陆博,刘道喜.基于机器视觉的矿石块度智能识别及应用[J].矿冶,2023,32(4):.
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