面向2035年的金屬礦深部多場智能開采發展戰略

郭奇峰; 蔡美峰; 吳星輝; 席迅; 馬明輝; 張杰

doi:10.13374/j.issn2095-9389.2021.10.22.004

面向2035年的金屬礦深部多場智能開采發展戰略

doi: 10.13374/j.issn2095-9389.2021.10.22.004

郭奇峰^{1, 2},
蔡美峰^{1, 2, ,},
吳星輝¹,
席迅¹,
馬明輝^{1, 3},
張杰¹

1.
北京科技大學土木與資源工程學院，北京 100083
2.
金屬礦山高效開采與安全教育部重點實驗室，北京 100083
3.
山東黃金礦業（萊州）有限公司焦家金礦，煙臺 261400

基金項目: 國家自然科學基金資助項目（L1824402）

詳細信息

通訊作者:
E-mail: caimeifeng@ustb.edu.cn

中圖分類號: TD801
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出版歷程
- 收稿日期: 2021-10-22
- 網絡出版日期: 2022-03-01
- 刊出日期: 2022-04-02

Technological strategies for intelligent mining subject to multifield couplings in deep metal mines toward 2035

GUO Qi-feng^{1, 2},
CAI Mei-feng^{1, 2
, ,},
WU Xing-hui¹,
XI Xun¹,
MA Ming-hui^{1, 3},
ZHANG Jie¹

1.
School of Civil and Resources Engineering, University of Science and Technology Beijing, Beijing 100083, China
2.
Key Laboratory of High Efficient Mining and Safety of Metal Mines, Ministry of Education, Beijing 100083, China
3.
Jiaojia Gold Mine, Shandong Gold Mine (Laizhou) Co Ltd, Yantai 261400, China

More Information

Corresponding author: E-mail: caimeifeng@ustb.edu.cn

摘要

摘要: 深部開采是金屬礦產資源開發的必然趨勢，向地球深部進軍，著力推動采礦行業智能化改造升級，開展深部智能化開采技術研究具有重要的戰略意義。立足國家深地戰略背景，剖析金屬礦深部資源開發對采礦科學技術發展的需求，依托工程技術預見技術方法開展全球技術態勢分析，梳理出本領域關鍵熱點和前沿技術清單，后經專家研判，形成面向2035年的金屬礦深部多場智能開采基礎理論和深部開采環境智能感知、深部開采過程智能作業、深部開采系統智能管控三大類前沿技術。在此基礎上，提出了我國面向2035年的金屬礦深部多場智能開采發展戰略、重點任務、技術路線，包括發展目標與需求、基礎研究方向、關鍵技術裝備等。針對我國金屬礦深部開采技術變革和智能化升級的科技發展路徑，從政策、產業、技術、人才等方面提出了發展和保障建議。
- 金屬礦 /
- 深部開采 /
- 智能開采 /
- 多場耦合 /
- 發展戰略 /
- 技術路線
Abstract: Deep mining is an inevitable trend in the exploitation of metal resources owing to their increasing demand. The multifield coupling environment for deep mining, which includes a high in situ stress, high temperature, high hydraulic pressure, and strong disturbances from excavations, pose considerable challenges to mining safety and efficiency. Intelligent or smart mining is a key to revolutionizing the mining industry. Therefore, for promoting the intelligent transformation and upgrading of the mining industry, the study of intelligent mining technologies for deep mines has a considerable strategic significance. Based on the strategic background of mining deep resources, this study investigated future technological strategies for exploiting deep metal resources toward 2035. Global technological trends on deep intelligent mining subject to multifield couplings were analyzed using technological forecasting methods. Hot research topics and advanced technologies related to intelligent deep mining subject to multifield couplings were obtained. Based on experts’ opinions and analyses, key fundamental theories and techniques for intelligent deep mining toward 2035 were proposed. There are three promising mining methods: unconventional deep mining methods without blasting, continuous pastes backfill mining in deep mines, integration of mining, mineral beneficiation and backfill. Advanced technologies can be divided into three types: (1) smart perception of the deep mining environment, (2) intelligent working during deep mining, and (3) intelligent control of mining systems. Type 1 includes intelligent in situ stress measurements, the intelligent identification of rock mass structures, microseismic monitoring and early warning of disasters, intelligent underground space exploration, and intelligent perception of man–machine systems. Type 2 includes intelligent full-section well excavation equipment, intelligent support technology and equipment, intelligent continuous mining technology and equipment, unmanned intelligent mining equipment, and intelligent lifting technology and equipment. Type 3 includes the intelligent control of the filling system, intelligent control of the microclimate in tunnels, flexible data communication on working faces, intelligent scheduling for the entire life cycle of deep mining, intelligent scheduling of the entire mining process, integrated platform for mining management, and big data analysis for deep mining. Technological strategies, key tasks, and a technical roadmap for 2035 were proposed for intelligent deep mining subject to multifield couplings in China, including development targets and demands, fundamental research areas, and key technologies and equipment. Technological development procedures to transform deep mining technologies and improve mining intelligence were presented. Some suggestions were provided in terms of policies, industries, technologies, and talent for intelligent deep mining.
- metal mine /
- deep mining /
- intelligent mining /
- multifield couplings /
- development strategy /
- technical roadmap

HTML全文

圖 1 技術體系圖

Figure 1. Technological system

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圖 2 各國文獻收錄情況

Figure 2. Literature collection in various countries

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圖 3 各國專利申請情況

Figure 3. Patent applications in various countries

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圖 4 文獻詞云熱點

Figure 4. Hot word cloud in the literature

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圖 5 專利詞云熱點

Figure 5. Hot word cloud in patents

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圖 6 面向2035的技術路線圖

Figure 6. Technological roadmap for 2035

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參考文獻(53)

[1]	Cai M F, Tan W H, Ren F H. Strategic Research on Innovative Technology System for Deep Mining of Metal Mines. Beijing: Science Press, 2018 蔡美峰, 譚文輝, 任奮華. 金屬礦深部開采創新技術體系戰略研究. 北京: 科學出版社, 2018
[2]	Xie H P. Research review of the state key research development program of China: Deep rock mechanics and mining theory. J China Coal Soc, 2019, 44(5): 1283 謝和平. 深部巖體力學與開采理論研究進展. 煤炭學報, 2019, 44(5):1283
[3]	Cai M F, Xue D L, Ren F H. Current status and development strategy of metal mines. Chin J Eng, 2019, 41(4): 417 蔡美峰, 薛鼎龍, 任奮華. 金屬礦深部開采現狀與發展戰略. 工程科學學報, 2019, 41(4):417
[4]	Cai M F, Brown E T. Challenges in the mining and utilization of deep mineral resources. Engineering, 2017, 3(4): 432 doi: 10.1016/J.ENG.2017.04.027
[5]	Holliday C O. Good morning engineers: A wake up call. Engineering, 2016, 2(1): 8 doi: 10.1016/J.ENG.2016.01.002
[6]	Li X B, Wang S F, Wang S Y. Experimental investigation of the influence of confining stress on hard rock fragmentation using a conical pick. Rock Mech Rock Eng, 2018, 51(1): 255 doi: 10.1007/s00603-017-1309-9
[7]	Hallada M R, Walter R F, Seiffert S L. High-power laser rock cutting and drilling in mining operations: Initial feasibility tests. High-Power Laser Ablation III, 2000, 4065: 614
[8]	Kosyrev F K, Rodin A V. Laser destruction and treatment of rocks // 9th International Conference on Advanced Laser Technologies (ALT 01). Constanta, 2002: 4762: 166
[9]	Zhang F P, Peng J Y, Qiu Z G, et al. Rock-like brittle material fragmentation under coupled static stress and spherical charge explosion. Eng Geol, 2017, 220: 266 doi: 10.1016/j.enggeo.2017.02.016
[10]	Wu A X, Yang Y, Cheng H Y, et al. Status and prospects of paste technology in China. Chin J Eng, 2018, 40(5): 517 吳愛祥, 楊瑩, 程海勇, 等. 中國膏體技術發展現狀與趨勢. 工程科學學報, 2018, 40(5):517
[11]	Wu A X, Li H, Yang L H, et al. Cemented paste backfill paves the way for deep mining. Gold, 2020, 41(9): 51 吳愛祥, 李紅, 楊柳華, 等. 深地開采, 膏體先行. 黃金, 2020, 41(9):51
[12]	Wu A X, Wang Y, Zhang M Z, et al. New development and prospect of key technology in underground mining of metal mines. Metal Mine, 2021(1): 1 吳愛祥, 王勇, 張敏哲, 等. 金屬礦山地下開采關鍵技術新進展與展望. 金屬礦山, 2021(1):1
[13]	Yin S H, Hao S, Zhang H S, et al. Water balance model and cost optimization of waste rock-unclassified pastes slurry. Chin J Nonferrous Met, https://kns.cnki.net/kcms/detail/43.1238.tg.20210831.1433.016.html 尹升華, 郝碩, 張海勝, 等. 廢石全尾砂充填料漿的水平衡模型及成本尋優. 中國有色金屬學報, https://kns.cnki.net/kcms/detail/43.1238.tg.20210831.1433.016.html
[14]	Sun C Y, Song Z G. Development and application outline of integrated underground mining-processing system. Min Metall, 2017, 26(1): 1 doi: 10.3969/j.issn.1005-7854.2017.01.001 孫傳堯, 宋振國. 地下采選一體化系統的研究及應用概況. 礦冶, 2017, 26(1):1 doi: 10.3969/j.issn.1005-7854.2017.01.001
[15]	Wu A X, Wang H J, Yin S H, et al. Conception of in situ fluidization mining for deep metal mines. J Min Sci Technol, 2021, 6(3): 255 吳愛祥, 王洪江, 尹升華, 等. 深層金屬礦原位流態化開采構想. 礦業科學學報, 2021, 6(3):255
[16]	Andrault D, Monteux J, Le Bars M, et al. The deep Earth may not be cooling down. Earth Planet Sci Lett, 2016, 443: 195 doi: 10.1016/j.jpgl.2016.03.020
[17]	Wu X H, Cai M F, Ren F H, et al. Heat exchange cooling technology of high temperature roadway in deep mine. J Central South Univ Sci Technol, 2021, 52(3): 890 doi: 10.11817/j.issn.1672-7207.2021.03.021 吳星輝, 蔡美峰, 任奮華, 等. 深部礦井高溫巷道熱交換降溫技術探討. 中南大學學報(自然科學版), 2021, 52(3):890 doi: 10.11817/j.issn.1672-7207.2021.03.021
[18]	Kang F C, Tang C A. Overview of enhanced geothermal system (EGS) based on excavation in China. Earth Sci Front, 2020, 27(1): 185 亢方超, 唐春安. 基于開挖的增強型地熱系統概述. 地學前緣, 2020, 27(1):185
[19]	Xie H P, Li C B, Gao M Z, et al. Conceptualization and preliminary research on deep in situ rock mechanics. Chin J Rock Mech Eng, 2021, 40(2): 217 謝和平, 李存寶, 高明忠, 等. 深部原位巖石力學構想與初步探索. 巖石力學與工程學報, 2021, 40(2):217
[20]	Li Y, Fu S S, Qiao L, et al. Development of twin temperature compensation and high-level biaxial pressurization calibration techniques for CSIRO in-situ stress measurement in depth. Rock Mech Rock Eng, 2019, 52(4): 1115 doi: 10.1007/s00603-018-1618-7
[21]	Ge Y F, Xia D, Tang H M, et al. Intelligent identification and extraction of geometric properties of rock discontinuities based on terrestrial laser scanning. Chin J Rock Mech Eng, 2017, 36(12): 3050 葛云峰, 夏丁, 唐輝明, 等. 基于三維激光掃描技術的巖體結構面智能識別與信息提取. 巖石力學與工程學報, 2017, 36(12):3050
[22]	Ge Y F, Zhong P, Tang H M, et al. Intelligent measurement on geometric information of rock discontinuities based on borehole image. Rock Soil Mech, 2019, 40(11): 4467 葛云峰, 鐘鵬, 唐輝明, 等. 基于鉆孔圖像的巖體結構面幾何信息智能測量. 巖土力學, 2019, 40(11):4467
[23]	Yuan L. Research progress on risk identification, assessment, monitoring and early warning technologies of typical dynamic hazards in coal mines. J China Coal Soc, 2020, 45(5): 1557 袁亮. 煤礦典型動力災害風險判識及監控預警技術研究進展. 煤炭學報, 2020, 45(5):1557
[24]	Li X, Xu N W. Research developments and prospects on microseismic source location. Prog Geophy, 2020, 35(2): 598 doi: 10.6038/pg2020DD0105 李翔, 徐奴文. 微震震源定位研究現狀及展望. 地球物理學進展, 2020, 35(2):598 doi: 10.6038/pg2020DD0105
[25]	Chen A G, Gao Y. Developments of research on earthquake detection methods. Prog Geophys, 2019, 34(3): 853 doi: 10.6038/pg2019CC0098 陳安國, 高原. 微震識別方法研究進展. 地球物理學進展, 2019, 34(3):853 doi: 10.6038/pg2019CC0098
[26]	Li T, Cai M F, Sun L J, et al. Inversion of mining-induced stress field and its application based on focal mechanism solution. Chin J Rock Mech Eng, 2016, 35(9): 1747 李鐵, 蔡美峰, 孫麗娟, 等. 基于震源機制解的礦井采動應力場反演與應用. 巖石力學與工程學報, 2016, 35(9):1747
[27]	Wang G F, Zhao G R, Ren H W. Analysis on key technologies of intelligent coal mine and intelligent mining. J China Coal Soc, 2019, 44(1): 34 王國法, 趙國瑞, 任懷偉. 智慧煤礦與智能化開采關鍵核心技術分析. 煤炭學報, 2019, 44(1):34
[28]	Li J L, Yang C Y, Hu Y, et al. Application research of UAV-lidar in detection of underground goaf. Met Mine, 2020(12): 168 李杰林, 楊承業, 胡遠, 等. 無人機三維激光掃描技術在地下采空區探測中的應用研究. 金屬礦山, 2020(12):168
[29]	Yang B S, Liang F X, Huang R G. Progress, challenges and perspectives of 3D LiDAR point cloud processing. Acta Geod Cartogr Sin, 2017, 46(10): 1509 楊必勝, 梁福遜, 黃榮剛. 三維激光掃描點云數據處理研究進展、挑戰與趨勢測繪學報, 2017, 46(10): 1509
[30]	Zhang Y S, Zhan K, Ma C Y, et al. Technical architecture and construction ideas of intelligent mine. Nonferrous Met Min Sect, 2020, 72(3): 1 張元生, 戰凱, 馬朝陽, 等. 智能礦山技術架構與建設思路. 有色金屬(礦山部分), 2020, 72(3):1
[31]	Wang G F, Du Y B. Coal mine intelligent standard system framework and construction ideas. Coal Sci Technol, 2020, 48(1): 1 王國法, 杜毅博. 煤礦智能化標準體系框架與建設思路. 煤炭科學技術, 2020, 48(1):1
[32]	Liang F M. Intelligent Sensing Theory and Key Technologies of Multi-Parameter Fiber Bragg Grating in Coal Mining [Dissertation]. Beijing: China University of Mining & Technology Beijing, 2019 梁敏富. 煤礦開采多參量光纖光柵智能感知理論及關鍵技術[學位論文]. 北京: 中國礦業大學(北京), 2019
[33]	Xie H P. Research framework and anticipated results of deep rock mechanics and mining theory. Adv Eng Sci, 2017, 49(2): 1 謝和平. “深部巖體力學與開采理論”研究構想與預期成果展望. 工程科學與技術, 2017, 49(2):1
[34]	Long Z Y, Guo X X. Development and application of full tunnel boring machine. Mine Constr Technol, 2017, 38(5): 7 龍志陽, 郭孝先. 全斷面掘進機發展和應用. 建井技術, 2017, 38(5):7
[35]	Tan J, Liu Z Q, Song Z Y, et al. Status and development trend of mine shaft sinking technique in China. Met Mine, 2021(5): 13 譚杰, 劉志強, 宋朝陽, 等. 我國礦山豎井鑿井技術現狀與發展趨勢. 金屬礦山, 2021(5):13
[36]	Li J B. Current status, problems and prospects of research, designng machine in China, and manufacturing of bor. Tunn Constr, 2021, 41(6): 877 李建斌. 我國掘進機研制現狀、問題和展望. 隧道建設(中英文), 2021, 41(6):877
[37]	Li X B, Huang L Q, Zhou J, et al. Review and prospect of mining technology in hard rock mines. Chin J Nonferrous Met, 2019, 29(9): 1828 李夕兵, 黃麟淇, 周健, 等. 硬巖礦山開采技術回顧與展望. 中國有色金屬學報, 2019, 29(9):1828
[38]	Wang S F, Li X B, Gong F Q, et al. Breakage characteristics and mechanized mining experiment in deep hard rock. J Central South Univ Sci Technol, 2021, 52(8): 2772 王少鋒, 李夕兵, 宮鳳強, 等. 深部硬巖截割特性與機械化破巖試驗研究中南大學學報(自然科學版), 2021, 52(8): 2772
[39]	Li X B, Zhou J, Wang S F, et al. Review and practice of deep mining for solid mineral resources. Chin J Nonferrous Met, 2017, 27(6): 1236 李夕兵, 周健, 王少鋒, 等. 深部固體資源開采評述與探索. 中國有色金屬學報, 2017, 27(6):1236
[40]	Wang G F, Liu F, Meng X J, et al. Research and practice on intelligent coal mine construction (primary stage). Coal Sci Technol, 2019, 47(8): 1 王國法, 劉峰, 孟祥軍, 等. 煤礦智能化(初級階段)研究與實踐. 煤炭科學技術, 2019, 47(8):1
[41]	Yang J J, Zhang Q, Wu M, et al. Research progress of autonomous perception and control technology for intelligent heading. J China Coal Soc, 2020, 45(6): 2045 楊健健, 張強, 吳淼, 等. 巷道智能化掘進的自主感知及調控技術研究進展. 煤炭學報, 2020, 45(6):2045
[42]	Liu L, Fang Z Y, Zhang B, et al. Development history and basic categories of mine backfill technology. Met Mine, 2021(3): 1 劉浪, 方治余, 張波, 等. 礦山充填技術的演進歷程與基本類別. 金屬礦山, 2021(3):1
[43]	Qi C C, Yang X Y, Li G C, et al. Research status and perspectives of the application of artificial intelligence in mine backfilling. J China Coal Soc, 2021, 46(2): 688 齊沖沖, 楊星雨, 李桂臣, 等. 新一代人工智能在礦山充填中的應用綜述與展望. 煤炭學報, 2021, 46(2):688
[44]	Zhou F B, Wei L J, Xia T Q, et al. Principle, key technology and preliminary realization of mine intelligent ventilation. J China Coal Soc, 2020, 45(6): 2225 周福寶, 魏連江, 夏同強, 等. 礦井智能通風原理、關鍵技術及其初步實現. 煤炭學報, 2020, 45(6):2225
[45]	Zhang Q H, Yao Y H, Zhao J Y. Status of mine ventilation technology in China and prospects for intelligent development. Coal Sci Technol, 2020, 48(2): 97 張慶華, 姚亞虎, 趙吉玉. 我國礦井通風技術現狀及智能化發展展望. 煤炭科學技術, 2020, 48(2):97
[46]	Yuan L, Yu X, Ding E J, et al. Research on key technologies of human-machine-environment states perception in mine Internet of things. J Commun, 2020, 41(2): 1 doi: 10.11959/j.issn.1000-436x.2020036 袁亮, 俞嘯, 丁恩杰, 等. 礦山物聯網人-機-環狀態感知關鍵技術研究. 通信學報, 2020, 41(2):1 doi: 10.11959/j.issn.1000-436x.2020036
[47]	Li Y, Wu Z W. Research on time synchronization accuracy method in synchronous Ethernet environment. Comput Netw, 2016, 42(22): 72 doi: 10.3969/j.issn.1008-1739.2016.22.070 李曄, 伍宗文. 同步以太網環境下時間同步精度方法研究. 計算機與網絡, 2016, 42(22):72 doi: 10.3969/j.issn.1008-1739.2016.22.070
[48]	Ji H, Zhang D, Dai R, et al. High precision time synchronization system designed and implemented for underground mine distributed system. China Min Mag, 2019, 28(Suppl 2): 219 冀虎, 張達, 戴銳, 等. 一種適用于地下礦山分布式系統的高精度時間同步系統設計及實現. 中國礦業, 2019, 28(增刊2): 219
[49]	Wang G F, Wang H, Ren H W, et al. 2025 scenarios and development path of intelligent coal mine. J China Coal Soc, 2018, 43(2): 295 王國法, 王虹, 任懷偉, 等. 智慧煤礦2025情景目標和發展路徑. 煤炭學報, 2018, 43(2):295
[50]	Wang L G, Chen X. Advancing technologies for digital mine. Chin J Nonferrous Met, 2016, 26(8): 1693 王李管, 陳鑫. 數字礦山技術進展. 中國有色金屬學報, 2016, 26(8):1693
[51]	Bi L, Wang J M. Construction target, task and method of digital mine. Met Mine, 2019(6): 148 畢林, 王晉淼. 數字礦山建設目標、任務與方法. 金屬礦山, 2019(6):148
[52]	Ding E J, Hu Q S. Design ideas of the top layer of the mine Internet of things. Chin J Internet Things, 2018, 2(1): 69 doi: 10.11959/j.issn.2096-3750.2018.00043 丁恩杰, 胡青松. 礦山物聯網頂層設計思路. 物聯網學報, 2018, 2(1):69 doi: 10.11959/j.issn.2096-3750.2018.00043
[53]	The project team of “Technological strategy research of China Engineering Science and technology towards 2035”. The Development Strategy of China's Engineering Science and Technology for 2035. Beijing: Science Press, 2019 “中國工程科技2035發展戰略研究”項目組. 中國工程科技2035發展戰略−綜合報告. 北京: 科學出版社, 2019