Development of Online Detection Technologies for Ore Grade

Huaiyuan Wang , Zhengyu Liu , Fuming Qu , Liancheng Wang , Xingtong Yue , Xingfan Zhang , Anlin Shao

Strategic Study of CAE ›› 2024, Vol. 26 ›› Issue (3) : 152 -163.

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Strategic Study of CAE ›› 2024, Vol. 26 ›› Issue (3) :152 -163. DOI: 10.15302/J-SSCAE-2024.03.013
Development of Online Detection Technologies for Ore Grade
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Abstract

The ore grade is a core indicator for measuring the economic value of minerals, and its online detection capability is related to the economic benefits, environmental impact, and production intelligence level of a mining enterprise. This study discusses the application value and classification of online detection technologies for ore grade and summarizes the research and application progress of these technologies in terms of the following technical directions: radioactive, optical, electromagnetic, and machine-vision detection. Challenges faced by the development of related technologies are identified at the technical research and practical application levels. Challenges at the technical research level include (1) accuracy bottlenecks and interference factors, (2) difficulties in signal analysis and optimization, and (3) model construction and data dependency. Challenges at the practical application level include (1) radiation safety and cost-effectiveness, (2) technological breakthroughs adapted to diverse ore characteristics, and (3) stable operation and real-time feedback in harsh environments. The study further elaborates on the future development directions of online detection technologies for ore grade. Future efforts should focus on breakthroughs in exploring the forefront of multimodal fusion and intelligent perception technologies, iterating and upgrading intelligent perception and data processing algorithms, developing miniaturized/remote/intelligent equipment, and constructing and optimizing real-time dynamic monitoring network systems. Moreover, emerging technologies, such as deep learning for promoting the fusion analysis of micro and macro features, quantum computing and bioinspired algorithms, as well as intelligent sensor networks and the Internet of Things technology, are summarized. Furthermore, active actions are recommended in the following aspects: (1) technological innovation and equipment upgrading, (2) standards formulation and standardization construction, (3) deepening of the industry–education–research–application cooperation mechanism, (4) talent cultivation and team building, and (5) international cooperation and resource sharing, thereby promoting the intelligent and efficient development and utilization of mineral resources.

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Keywords

ore grade / online detection / radiological testing / optical testing / electromagnetic testing / machine vision inspection

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Huaiyuan Wang, Zhengyu Liu, Fuming Qu, Liancheng Wang, Xingtong Yue, Xingfan Zhang, Anlin Shao. Development of Online Detection Technologies for Ore Grade. Strategic Study of CAE, 2024, 26(3): 152-163 DOI:10.15302/J-SSCAE-2024.03.013

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Funding

Funding project: Chinese Academy of Engineering project "Research on China's Mineral Resources Security Strategy"(2022-XBZD-27)

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