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AI活用事例 2026年7月更新
鉱業・採掘業のAI活用事例｜鉱床探査・自律運搬トラック・予知保全・選鉱最適化を徹底解説【2026年最新】
公開日: 2026/07/02
この記事のポイント
鉱業・採掘業のAI活用を、鉱床探査・自律運搬トラック・設備予知保全・安全管理・選鉱最適化の5領域で整理。KoBold・Komatsu・住友金属鉱山など国内外の導入事例と主要サービス比較、料金の実態、導入判断基準まで公式情報ベースで解説します。
鉱業・採掘業のAI活用は、鉱

原文链接：https://ai-revolution.co.jp/media/ai-in-mining/

Load-Haul-Dump machines using deep reinforcement learning
Rodrigo Salas,
Francisco Leiva,
Expert Systems with Applications: An International Journal (EXWA), Volume 264, Issue C https://doi.org/10.1016/j.eswa.2024.125770
Abstract
This work presents a deep reinforcement learning-based approach to train controllers for the autonomous loading of ore piles with a Load-Haul-Dump (LHD) machine. … (More)
research-article
Detecting rocks in challenging mining environments using convolutional neural networks and ellipses as an alternative to bounding boxes
Patricio Loncomilla,
Expert Systems with Ap

原文链接：https://dl.acm.org/institution/60122505/publications?PublisherRaw=PERGA&pageSize=20

矿产精锡行业AI应用及布局策略深度研究.pptx
阅读：2次|页数：31页 |上传：2025-03-26 15:49
矿产精锡行业AI应用及布局策略深度研究 汇报人：XXX 2025-X-X 目 录 1. 矿产精锡行业概述 2. AI技术在矿产精锡行业的应用 3. AI应用案例分析 4. 矿产精锡行业AI应用挑战与机遇 5. 矿产精锡行业AI应用布局策略 6. 矿产精锡行业AI应用实施路径 7. 矿产精锡行业AI应用未来展望 01 矿产精锡行业概述 行业背景及发展历程 行业起源 精锡行业起源于我国古代，距今已有数千年的历

原文链接：https://www.docin.com/touch_new/preview_new.do?id=4847797498

A comprehensive exploration of nongeostationary satellite systems in the mining industry: emphasizing AI , ethical considerations, and communication strategies
Author
Listed:
Fredrick Kayusi
Petros Chavula
Registered:
Abstract
Non-geostationary satellite (NGSO) constellations—particularly LEO/MEO—are transforming mining by providing low-latency connectivity and taskable Earth observation to remote, infrastructure-poor sites. Objectives include mapping NGSO applications across exploration, planning, and operations; assessing AI’s role in tasking, routing, and analytics; and examining govern

原文链接：https://ideas.repec.org/a/dbk/ethaic/v4y2025ip408id408.html

research-article
AI-DeepFrothNet: : Continuous monitoring and tracking of froth flotation working condition by root cause analysis and optimized predictive control
Authors :
Khalid A. Abouda , Degang Xu, Wail M. Idress, Hager M. Elmaki , + 5
, Tehseen Mazhar, Muhammad Aoun, + 3, Yazeed Yasin Ghadi, Tariq Shahzad, Habib Hamam (Less)
Volume 123, Issue PD
https://doi.org/10.1016/j.compeleceng.2025.110251
Published : 01 April 2025 Publication History
Abstract
References
Abstract
Achieving optimal working conditions in froth flotation is critical for maximizing mineral recovery. Traditional manua

原文链接：https://dl.acm.org/doi/abs/10.1016/j.compeleceng.2025.110251

d sequentially in a random path, the environment generates the states for each grid node, and feeds the state to the actor and critic agents that respectively predict and evaluate the updated property of the grid node The data is stored in a replay memory, which is sampled at regular intervals to train the agents. The trained agents are then used for further rounds of self-learning. An application of the proposed algorithm at a copper mining operation with incoming drilling machine sensor data (collected spatially), and processing mill sensor data (collected over time), demonstrates its applie

原文链接：https://dl.acm.org/doi/abs/10.1016/j.cageo.2021.104962

锡矿开采中的机器人技术应用-全面剖析
布***
DOCX
48.07KB
约32页
文档ID:599024877
锡矿开采中的机器人技术应用 第一部分 锡矿开采现状分析 2第二部分 机器人技术概述 6第三部分 机器人在锡矿的应用领域 9第四部分 机器人在锡矿中的优势 12第五部分 机器人技术在锡矿的实施挑战 16第六部分 机器人技术在锡矿的应用案例 20第七部分 未来机器人技术发展趋势 23第八部分 经济效益与环保效益评估 27第一部分 锡矿开采现状分析关键词关键要点锡矿开采现状分析

原文链接：https://m.jinchutou.com/shtml/view-599024877.html

l:
luis.soto14@unmsm.edu.pe
Page:
2583-2594
|
DOI:
https://doi.org/10.18280/isi.301006
Received:
17 September 2025
|
Revised:
20 October 2025
|
Accepted:
26 October 2025
|
Available online:
31 October 2025
|
Citation
isi_30.10_06.pdf
OPEN ACCESS
Abstract:
The integration of artificial intelligence (AI), particularly machine learning (ML), into geometallurgy provides an important opportunity to optimize mineral processing and mine planning. This study synthesizes recent research on ML-based geometallurgical applications and examines advances and challenges within the Peruvian mining sector. A n

原文链接：https://iieta.org/journals/isi/paper/10.18280/isi.301006

云锡将创建“品牌卓著”的锡铟行业世界一流企业新田选矿车间全国行业内首创：机器人“不辞辛苦”参与高效选矿云锡华联锌铟开启“智慧之眼” 探索5G远程遥控赋能智慧矿山建设全国有色冶炼领域首家无煤无焦绿色工厂支撑云锡创建锡铟行业世界一流企业——技术创新提升云锡文山锌铟竞争力从“表绿”到“深绿”云锡华联锌铟让绿色成为矿山发展最亮眼的底色全国有色冶炼领域首家无煤无焦绿色工厂 云锡创建锡铟行业世界一流企业全国行业内

原文链接：https://finance.yunnan.cn/cms_udf/2023/gqggfzyxjt/index.shtml

Artificial Intelligence in Mineral Exploration
Last Updated : 14 Apr 2025
Introduction
This paper focuses on the best practices on how Artificial Intelligence (AI) is playing a very important role in the improvement of speed and accuracy in the identification of minerals. Conventional approach of exploration takes considerable amount of time, the cost and human intervention but the AI tools reduce the time taken by analysing the geological data. Specific machine learning algorithms are utilized to analyse satellite images together with geophysical surveys and previous mining data in order to d

原文链接：https://www.tpointtech.com/artificial-intelligence-in-mineral-exploration