REFERENCES
- ABB. (2023). 5 potential levels of automation for the autonomous mine of the future. https://new.abb.com/mining/mineoptimize/systems-solutions/mining-automation/5-levels-of-automation-for-the-autonomous-mine-of-the-future
- Ali, D., & Frimpong, S. (2020). Artificial intelligence, machine learning and process automation: Existing knowledge frontier and way forward for mining sector. Artificial Intelligence Review, 53(8), 6025–6042. https://doi.org/10.1007/s10462-020-09841-6
- Avalos, S., Kracht, W., & Ortiz, J. M. (2020). Machine learning and deep learning methods in mining operations: A data-driven SAG mill energy consumption prediction application. Mining, Metallurgy & Exploration, 37(4), 1197–1212. https://doi.org/10.1007/s42461-020-00238-1
- Baek, J., & Choi, Y. (2019). Deep neural network for ore production and crusher utilization prediction of truck haulage system in underground mine. Applied Sciences, 9(19), 4180. https://doi.org/10.3390/app9194180
- Baek, J., & Choi, Y. (2020). Deep neural network for predicting ore production by truck-haulage systems in open-pit mines. Applied Sciences, 10(5), 1657. https://doi.org/10.3390/app10051657
- Bardinas, J. P., Aldrich, C., & Napier, L. F. (2018). Predicting the operating states of grinding circuits by use of recurrence texture analysis of time series data. Processes, 6(2), 17. https://doi.org/10.3390/pr6020017
- Barnewold, L., & Lottermoser, B. G. (2020). Identification of digital technologies and digitalisation trends in the mining industry. International Journal of Mining Science and Technology, 30(6), 747–757. https://doi.org/10.1016/j.ijmst.2020.07.003
- BHP. (2019). Automation data is making work safer, smarter and faster. https://www.bhp.com/news/articles/2019/07/automation-data-is-making-work-safer-smarter-and-faster
- Both, C., & Dimitrakopoulos, R. (2021). Applied machine learning for geometallurgical throughput prediction—A case study using production data at the Tropicana Gold Mining Complex. Minerals, 11(11), 1257. https://doi.org/10.3390/min11111257
- Boyes, H., Hallaq, B., Cunningham, J., & Watson, T. (2018). The industrial internet of things (IIoT): An analysis framework. Computers in Industry, 101, 1–12. https://doi.org/10.1016/j.compind.2018.04.015
- Burgess-Limerick, R. (2011). Injuries associated with underground coal mining equipment. The Ergonomics Open Journal, 4(Suppl. 2–M1), 62–73. https://doi.org/10.2174/1875934301104010062
- Cai, W., Dou, L., Zhang, M., Cao, W., Shi, J. Q., & Feng, L. (2018). A fuzzy comprehensive evaluation methodology for rock burst forecasting using microseismic monitoring. Tunnelling and Underground Space Technology, 80, 232–245. https://doi.org/10.1016/j.tust.2018.06.029
- Canadian Mining Journal. (2019a). Epiroc launches 6th Sense for smarter mining. https://www.canadianminingjournal.com/digital-edition/august-2019/
- Canadian Mining Journal. (2019b). Goldcorp and IBM launch EMB exploration with Watson. Canadian Mining Journal, 140(1), 10.
- Canadian Mining Journal. (2021). Champion Iron chooses Cat for AI drill technology at Bloom Lake. Canadian Mining Journal, 142(7), 14.
- Cao, B., Xie, Y., Gui, W., Wei, L., & Yang, C. (2013). Integrated prediction model of bauxite concentrate grade based on distributed machine vision. Minerals Engineering, 53, 31–38. https://doi.org/10.1016/j.mineng.2013.07.003
- Caterpillar Global Mining. (2008). Improving productivity through technology integration. Viewpoint: Perspectives on Modern Mining. http://viewpointmining.com/article/improving-productivity-through-technology-integration
- Ceda. (2015). Australia’s future workforce?. https://www.ceda.com.au/ResearchAndPolicies/Research/Workforce-Skills/Australia-s-future-workforce
- Cisco. (2014). Mining firm quadruples production, with internet of everything by Cisco: IoT ONE digital transformation advisors. IoT ONE. https://www.iotone.com/case-study/mining-firm-quadruples-production-with-internet-of-everything/c39
- Cisco. (2015). Goldcorp’s Éléonore: Internet of Things enables the mine of tomorrow today. https://www.cisco.com/c/dam/en_us/solutions/industries/materials-mining/downloads/c36-goldcorp-cs.pdf
- Clausen, E., Sörensen, A., Uth, F., Mitra, R., Lehnen, F., & Schwarze, B. (2020a). Assessment of the effects of global digitalization trends on sustainability in mining. Part I: Digitalization processes in the mining industry in the context of sustainability. https://www.bgr.bund.de/EN/Themen/Min_rohstoffe/Downloads/digitalization_mining_dustainability_part_I_en.pdf?__blob=publicationFile&v=4
- Clausen, E., Sörensen, A., Uth, F., Mitra, R., Lehnen, F., & Schwarze, B. (2020b). Assessment of the effects of global digitalization trends on sustainability in mining. Part II: Evaluation of digitalization trends and their effects on sustainability in the global mining sector. https://ecominingconcepts.cl/wp-content/uploads/2021/03/digitalization_trends_mining_sustainability_part_II_en.pdf
- de Carvalho, J. P., & Dimitrakopoulos, R. (2021). Integrating production planning with truck-dispatching decisions through reinforcement learning while managing uncertainty. Minerals, 11(6), 587. https://doi.org/10.3390/min11060587
- Delevingne, L., Glazener, W., Grégoir, L., & Henderson, K. (2021). Climate risk and decarbonization: What every mining CEO needs to know. McKinsey & Company. https://www.mckinsey.com/business-functions/sustainability/our-insights/climate-risk-and-decarbonization-what-every-mining-ceo-needs-to-know
- De, A., & Mukhopadhyay, A. K. (1989). Selection, maintenance, and relations of various parameters for off-highway hauling tires. Off-highway haulage in surface mines. Routledge.
- Dragt, B. J., Camisani-Calzolari, F. R., & Craig, I. K. (2005). An overview of the automation of load-haul-dump vehicles in an underground mining environment. IFAC Proceedings Volumes, 38(1), 37–48. https://doi.org/10.3182/20050703-6-CZ-1902.01389
- Dubois, M. (2019, May 1). Artificial intelligence using real-time data. GMG-CIM Montreal Forum: Artificial Intelligence in Mining.
- Dubois, M., & Campeau, L.-P. (2019). Artificial intelligence using real-time data. In Mining goes digital (1st ed.).
- Dumakor Dupey, N. K., Arya, S., & Jha, A. (2021). Advances in blast-induced impact prediction—A review of machine learning applications. Minerals, 11(6), 601. https://doi.org/10.3390/min11060601
- Dyson, N. (2020, May 26). Syama’s automation surge. Mining Magazine. https://www.miningmagazine.com/technology-innovation/news/1387604/syama%E2%80%99s-automation-surge
- Epiroc. (2019). Epiroc introduces the next generation of the rig control system for pit viper blasthole drilling rigs. https://www.epiroc.com/en-ca/newsroom/2019/epiroc-introduces-next-generation-of-the-rig-control-system-rcs5-for-pit-viper-drills
- Ernst & Young. (2019). Future of work: The economic implications of technology and digital mining, p. 59.
- Faries, J. (2018). Fragmentation analysis on the fly. CIM Magazine, 13(3), 16.
- Feroz, A. K., Zo, H., & Chiravuri, A. (2021). Digital transformation and environmental sustainability: A review and research agenda. Sustainability, 13(3), 1530. https://doi.org/10.3390/su13031530
- Fu, Y., & Aldrich, C. (2018). Froth image analysis by use of transfer learning and convolutional neural networks. Minerals Engineering, 115, 68–78. https://doi.org/10.1016/j.mineng.2017.10.005
- Fu, Y., & Aldrich, C. (2019). Flotation froth image recognition with convolutional neural networks. Minerals Engineering, 132, 183–190. https://doi.org/10.1016/j.mineng.2018.12.011
- Gallestey, E., Westerlund, P., Lima, E., Rietschel, F., Andai, R., & Colbert, C. (2015). Next Level mining. Securing the future through integrated operations & information technologies. ABB White Paper, 11–12.
- Gillis, A. S. (2022). What is the internet of things (IOT)? https://www.techtarget.com/iotagenda/definition/Internet-of-Things-IoT
- Gleeson, D. (2019). Newtrax helps haulage operations at Glencore’s Matagami Zinc-copper mine. International Mining. https://im-mining.com/2019/02/18/newtrax-helps-haulage-operations-glencores-matagami-zinc-copper-mine/
- GlobalData. (2020). Australia dominates global autonomous haul truck use with numbers set to triple. Mining Technology. https://www.mining-technology.com/comment/australia-autonomous-haul-trucks-use/
- GlobalData. (2022). Australia continues to dominate the use of autonomous haul trucks. Mining Technology. https://www.mining-technology.com/comment/australia-autonomous-haul-trucks/
- Global Mining Guidelines Group. (2019a). Foundations of AI – a framework for AI in mining. White Paper. http://gmggroup.org/wp-content/uploads/2019/10/GMG_Foundations-of-AI-A-Framework-for-AI-in-Mining-2019-10-07_v01_r01.pdf
- Global Mining Guidelines Group. (2019b). Guideline for the implementation of autonomous systems in mining. https://gmggroup.org/wp-content/uploads/2019/06/20181008_Implementation_of_Autonomous_Systems-GMG-AM-v01-r01.pdf
- Global Mining Guidelines Group. (n.d.). Virtual forum series: Autonomous drills. https://gmggroup.org/event-directory/virtual-forum-series-autonomous-drills-page/
- Goodbody, A. (2021). Continuous Flow. CIM Magazine, March/April, 56–58.
- Gourley, E. (2019). Hecla’s Casa Berardi gold mine has implemented automated underground truck haulage on a dedicated drift, the latest and largest step in the operation’s automation journey and one expected to save several million dollars. Canadian Mining Journal, 140(4), 32–33.
- Government of Canada. (n.d.). Natural Resources Canada. Comprehensive energy use database – Disaggregated industries tables. https://oee.nrcan.gc.ca/corporate/statistics/neud/dpa/menus/trends/comprehensive_tables/list.cfm
- Greiner, L. (2022). Thinking ahead. CIM Magazine, 16(8), 44–46.
- Gruske, C. (2022). End-to-end integration. CIM Magazine, 17(2), 54–58.
- Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645–1660. https://doi.org/10.1016/j.future.2013.01.010
- Guo, X., Liu, X., Zhou, H., Stanislawski, R., Królczyk, G., & Li, Z. (2022). Belt tear detection for coal mining conveyors. Micromachines, 13(3), 449. https://doi.org/10.3390/mi13030449
- Gustafson, A. (2011). Automation of load haul dump machines (Research Report). Luleå University of Technology. https://www.diva-portal.org/smash/record.jsf?pid=diva2:995534
- Hardcastle, S. G., & Kocsis, C. K. (2001). Ventilation design for automated underground metal mines. Proceedings of the International Mine Ventilation Congress.
- Heyduk, A. (2018). Machine vision monitoring and particle size feed analysis. Mining–Informatics, Automation and Electrical Engineering, 56(1), 7. https://doi.org/10.7494/miag.2018.1.533.7
- Holcombe, S., & Kemp, D. (2018). Indigenous employment futures in an automated mining industry: An issues paper and a case for research. Centre for Social Responsibility in Mining, Sustainable Minerals Institute, The University of Queensland.
- Horberry, T., Burgess-Limerick, R., & Steiner, L. J. (2016). Human factors for the design, operation, and maintenance of mining equipment. CRC Press.
- Horowitz, C. A. (2016). Paris agreement. International Legal Materials, 55(4), 740–755. https://doi.org/10.1017/S0020782900004253
- Huang, L., Li, J., Hao, H., & Li, X. (2018). Micro-seismic event detection and location in underground mines by using Convolutional Neural Networks (CNN) and deep learning. Tunnelling and Underground Space Technology, 81, 265–276. https://doi.org/10.1016/j.tust.2018.07.006
- Huo, D., Sari, Y. A., Kealey, R., & Zhang, Q. (2023). Reinforcement learning-based fleet dispatching for greenhouse gas emission reduction in open-pit mining operations. Resources Conservation and Recycling, 188, 106664. https://doi.org/10.1016/j.resconrec.2022.106664
- Ingram, T. (2017). Fortescue to increase autonomous trucks, test conveyor in FY18 innovation plan. Australian Financial Review. https://www.afr.com/companies/mining/fortescue-to-increase-autonomous-trucks-test-conveyor-in-fy18-innovation-plan-20170627-gwzcx8
- Inmarsat. (2020). The rise of IOT in mining. https://www.inmarsat.com/content/dam/inmarsat/corporate/documents/enterprise/insights/Inmarsat_WP_The_Rise_of_IoT_in_Mining.pdf.coredownload.inline.pdf
- International Council on Mining & Metals. (2021). Safety performance: Benchmarking progress of ICMM company members in 2020. https://www.icmm.com/en-gb/research/health-safety/benchmarking-2020-safety-data
- International Mining. (2019). Why the pilbara leads the way in haul truck automation. https://im-mining.com/2019/08/06/pilbara-leads-way-haul-truck-automation/
- IT Brief Australia. (2022). Digital decarbonisation of the mining industry - now or never. https://itbrief.com.au/story/digital-decarbonisation-of-the-mining-industry-now-or-never
- Jakobs, A. (2021). How long can mining operations still afford not to automate? An assessment. Mining Report, 157(4), 342–349.
- Jang, H., & Topal, E. (2020). Transformation of the Australian mining industry and future prospects. Mining Technology, 129(3), 120–134. https://doi.org/10.1080/25726668.2020.1786298
- Janusz, A., Grzegorowski, M., Michalak, M., Wróbel, Ł., Sikora, M., & Ślęzak, D. (2017). Predicting seismic events in coal mines based on underground sensor measurements. Engineering Applications of Artificial Intelligence, 64, 83–94. https://doi.org/10.1016/j.engappai.2017.06.002
- Jo, B. W., & Khan, R. M. A. (2017). An event reporting and early-warning safety system based on the internet of things for underground coal mines: A case study. Applied Sciences, 7(9), 925. https://doi.org/10.3390/app7090925
- Jung, D., & Choi, Y. (2021). Systematic review of machine learning applications in mining: Exploration, exploitation, and reclamation. Minerals, 11(2), 148. https://doi.org/10.3390/min11020148
- Kaniewski, T., Śliwiński, P., Hebda-Sobkowicz, J., & Zimroz, R. (2019). Comprehensive, experimental verification of the effects of the lock-up function implementation in LHD haul trucks in the deep underground mine. In Mining goes digital (pp. 506–514). CRC Press.
- Karacan, C. Ö. (2007). Development and application of reservoir models and artificial neural networks for optimizing ventilation air requirements in development mining of coal seams. International Journal of Coal Geology, 72(3–4), 221–239. https://doi.org/10.1016/j.coal.2007.02.003
- Katta, A. K., Davis, M., & Kumar, A. (2020). Development of disaggregated energy use and greenhouse gas emission footprints in Canada’s iron, gold, and potash mining sectors. Resources Conservation and Recycling, 152, 104–485. https://doi.org/10.1016/j.resconrec.2019.104485
- Keating, C. (2017). Equipment monitoring through machine learning. CIM Magazine, 12(5), 34–35.
- KPMG. (2021). Decarbonizing the mining industry: Achieving 2030 and 2050 goals. https://home.kpmg/ca/en/home/insights/2021/11/decarbonizing-the-mining-industry.html
- Lange, T. B. (1992). Application of machine vision in mining and metallurgical processes. IFAC Proceedings Volumes, 25(19), 229–233. https://doi.org/10.1016/S1474-6670(17)49926-5
- Lee, S., & Choi, Y. (2016). Reviews of unmanned aerial vehicle (drone) technology trends and its applications in the mining industry. Geosystem Engineering, 19(4), 197–204. https://doi.org/10.1080/12269328.2016.1162115
- Lee, J., & Prowse, K. (2014). Mining & Metals + Internet of Things: Industry opportunities and innovation. MaRS Market Insights. https://www.marsdd.com/wp-content/uploads/2014/11/Mining-Metals-and-IoT.pdf
- Legge, H., Müller-Falcke, C., Nauclér, T., & Östgren, E. (2021). Creating the zero-carbon mine. McKinsey & Company. https://www.mckinsey.com/industries/metals-and-mining/our-insights/creating-the-zero-carbon-mine
- Leonida, C. (2021). Advancing art of autonomous drilling. https://www.e-mj.com/features/advancing-art-of-autonomous-drilling/
- Levesque, M. (2015). An improved energy management methodology for the mining industry [ Doctoral dissertation]. Laurentian University of Sudbury.
- Levesque, M., Millar, D., & Paraszczak, J. (2014). Energy and mining—The home truths. Journal of Cleaner Production, 84(1), 233–255. https://doi.org/10.1016/j.jclepro.2013.12.088
- Lin, B., Wei, X., & Junjie, Z. (2019). Automatic recognition and classification of multi-channel microseismic waveform based on DCNN and SVM. Computers & Geosciences, 123, 111–120. https://doi.org/10.1016/j.cageo.2018.10.008
- Liu, D. (2021). Open-pit mine production scheduling and crusher location-relocation plan under semi-mobile IPCC systems [ Master’s thesis]. University of Alberta.
- Liu, Y., Zhang, Z., Liu, X., Wang, L., & Xia, X. (2021). Ore image classification based on small deep learning model: Evaluation and optimization of model depth, model structure and data size. Minerals Engineering, 172, 107–120. https://doi.org/10.1016/j.mineng.2021.107020
- Lopez-Pacheco, A. (2018a). Goldilocks autonomy. https://magazine.cim.org/en/technology/goldilocks-autonomy-en/
- Lopez-Pacheco, A. (2018b). Revving up: A look at machine learning projects across the mining industry. CIM Magazine, 13(1), 38–39.
- Luedtke, M. (2022). ABB: Trends in mining for 2022. Mining Magazine. https://www.miningmagazine.com/sustainability/opinion/1426845/abb-trends-in-mining-for-2022
- Lung, R. G. (2020). Automation and digitalisation potentials of underground mining methods; a comparative analysis and identification of key performance indicators [ Master’s thesis]. Technical University of Bergakademie Freiberg.
- MacGillivray, C., Olvet, T., & Wallis, N. (2014). Canadian realities of the Internet of Things: Defining and creating new opportunity. IDC.
- Maestro Digital Mine. (2023a). Plexus PowerNet™. https://www.maestrodigitalmine.com/products/plexus-powernet#click-here-to-view-downloads
- Maestro Digital Mine. (2023b). Vigilante AQS™. https://www.maestrodigitalmine.com/products/vigilante-aqs-air-quality-station#downloads
- McCoy, J. T., & Auret, L. (2019). Machine learning applications in minerals processing: A review. Minerals Engineering, 132, 95–109. https://doi.org/10.1016/j.mineng.2018.12.004
- McLaren, E. (2019). Ventilation on demand at Nickel Rim South. https://cornettscorner.com/wp-content/uploads/2019/10/Ventilation-on-Demand-at-Nickel-Rim-South-.pdf
- Metso Outotec. (2016). Delivering a step change in operating costs. https://www.mogroup.com/insights/blog/mining-and-metals/delivering-a-step-change-in-operating-costs/
- Meyer, M. A. (2008). Implementing a tracking and ventilation control system at Barrick Goldstrike’s underground division. In 12th US/North American Mine Ventilation Symposium (pp. 13–18).
- Mielli, F. (2013). The internet of things (IOT) and. … . Mining operations? Schneider Electric Blog. https://blog.se.com/smart-grid/2013/11/13/internet-things-iot-mining-operations/
- Minerva, R., Biru, A., & Rotondi, D. (2015). Towards a definition of the Internet of Things (IoT). IEEE Internet Initiative, 1(1), 1–86.
- Mining & Construction Online. (2013, August 26). Autonomy in the Andes – Chile’s copper giant Codelco puts Scooptram ST14 to the test. https://miningandconstruction.com/mining/autonomy-in-the-andes-chiles-copper-giant-codelco-puts-scooptram-st14-to-the-test-2533/
- Mining Magazine. (2019). Byrnecut uses automated drill tech at jundee. https://www.miningmagazine.com/development/special-report/1264288/byrnecut-automated-drill-tech-jundee
- Molaei, F., Rahimi, E., Siavoshi, H., Afrouz, S. G., & Tenorio, V. (2020). A comprehensive review on internet of things (IoT) and its implications in the mining industry. American Journal of Engineering and Applied Sciences, 13(3), 499–515. https://doi.org/10.3844/ajeassp.2020.499.515
- Molly. (2022). Sizing up Syama: The world’s first fully automated mine. Mining Technology. https://www.mining-technology.com/analysis/sizing-syama-worlds-first-fully-automated-mine/
- Moreau, K., Bose, R., Shang, H., & Scott, J. A. (2019). Automation technology to increase productivity and reduce energy consumption in deep underground mining operations. CIM Journal, 10(3), 115–124. https://doi.org/10.15834/cimj.2019.11
- Motion Metrics. (2022). Motion metrics. https://www.motionmetrics.com/
- Murray, J. (2020). The mining industry is undergoing an IOT revolution, study finds. NS Energy. https://www.nsenergybusiness.com/news/mining-industry-iot/
- Nad, A., Jooshaki, M., Tuominen, E., Michaux, S., Kirpala, A., & Newcomb, J. (2022). Digitalization solutions in the mineral processing industry: The case of GTK Mintec, Finland. Minerals, 12(2), 210. https://doi.org/10.3390/min12020210
- Narendran, T. V., & Weinelt, B. (2017). Digital transformation initiative mining and metals industry. World Economic Forum. https://report.weforum.org/digital-transformation/wp-content/blogs.dir/94/mp/files/pages/files/white-paper-dti-2017-mm.pdf
- Natural Resources Canada. (2016). Green mining initiative CanmetMINING research plan 2016–2020. https://publications.gc.ca/collections/collection_2017/rncan-nrcan/M154-107-2016-eng.pdf
- Newtrax. (2023). Hecla Casa Berardi mine. https://newtrax.com/case-study/hecla-casa-berardi
- Norgate, T., & Haque, N. (2010). Energy and greenhouse gas impacts of mining and mineral processing operations. Journal of Cleaner Production, 18(3), 266–274. https://doi.org/10.1016/j.jclepro.2009.09.020
- Norgate, T., & Haque, N. (2013). The greenhouse gas impact of IPCC and ore-sorting technologies. Minerals Engineering, 42, 13–21. https://doi.org/10.1016/j.mineng.2012.11.012
- Olivier, J., & Aldrich, C. (2021). Use of decision trees for the development of decision support systems for the control of grinding circuits. Minerals, 11(6), 595. https://doi.org/10.3390/min11060595
- Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000). A model for types and levels of human interaction with automation. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 30(3), 286–297. https://doi.org/10.1109/3468.844354
- Paraszczak, J. (2014). Maximization of productivity of autonomous trackless loading and haulage equipment in underground metal mines—A challenging task. Mining Engineering, 66, 24–41.
- Patil, S. D., Mitra, A., Tuggali Katarikonda, K., & Wansink, J. D. (2021). Predictive asset availability optimization for underground trucks and loaders in the mining industry. Opsearch, 58(3), 751–772. https://doi.org/10.1007/s12597-020-00502-4
- Payne, T., & Mitra, R. A. (2008). A review of heat issues in underground metalliferous mines. Proceedings of the 12th U.S./North American Mine Ventilation Symposium, 197–201.
- Plavšić, J., & Mišković, I. (2023). Industrial applications of digital twin technology in the mining sector: An overview. CIM Journal, 14(2), 1–10. https://doi.org/10.1080/19236026.2022.2145431
- PwC. (2021a). 24th CEO survey – Canadian insights. https://www.pwc.com/ca/en/ceo-survey/24th-ceo-survey.html
- PwC. (2021b). Canadian mine 2021. https://www.pwc.com/ca/en/industries/mining/canadian-mine-2021.html
- Qi, C., Chen, Q., & Kim, S. S. (2020). Integrated and intelligent design framework for cemented paste backfill: A combination of robust machine learning modelling and multi-objective optimization. Minerals Engineering, 155, 106–422. https://doi.org/10.1016/j.mineng.2020.106422
- Quash, K. (2019). Artificial intelligence for mine control. CIM Magazine, 14(2), 14.
- Rio Tinto. (2018). Autonomous-drilling-fleet-almost-doubled. https://www.riotinto.com/en/news/releases/Autonomous-drilling-fleet-almost-doubled.
- Rio Tinto. (2023). Automation. https://www.riotinto.com/about/innovation/automation
- Rolfe, K. (2018). Ibm’s Watson improves data processing times at Red Lake. https://magazine.cim.org/en/news/2018/ibm-watson-improves-data-processing-at-red-lake/
- Sbarba, H. D., Bartsch, E., & Lilley, J. (2012). SMARTEXEC mine ventilation on demand system at the Xstrata nickel rim south mine, Sudbury, Ontario; implementation and results to date. https://slideplayer.com/slide/3984708/
- Scales, M. (2019). Dundee tests new frontiers for drones. Canadian Mining Journal, 140(4), 24–27.
- Schunnesson, H., Gustafson, A., & Kumar, U. (2009). Performance of automated LHD machines: A review. International Symposium on Mine Planning and Equipment Selection, 773–782. https://www.diva-portal.org/smash/record.jsf?pid=diva2:1011995
- Shahmoradi, J., Talebi, E., Roghanchi, P., & Hassanalian, M. (2020). A comprehensive review of applications of drone technology in the mining industry. Drones, 4(3), 34. https://doi.org/10.3390/drones4030034
- Sheridan, T. B. (2002). Humans and automation: System design and research issues. In Wiley series in system engineering and management HFES issues in human factors and ergonomics series (Vol. 280). Cambridge University Press.
- Shirmard, H., Farahbakhsh, E., Müller, R. D., & Chandra, R. (2022). A review of machine learning in processing remote sensing data for mineral exploration. Remote Sensing of Environment, 268, 112750. https://doi.org/10.1016/j.rse.2021.112750
- Soofastaei, A. (2017). Improve haul truck availability by braking system failure prediction using advanced data analytics. https://www.researchgate.net/profile/Ali-Soofastaei/publication/316514923_Improve_Haul_Truck_Availability_by_Braking_System_Failure_Prediction_Using_Advanced_Data_Analytics/links/5901d6b4a6fdcc8ed5111401/Improve-Haul-Truck-Availability-by-Braking-System-Failure-Prediction-Using-Advanced-Data-Analytics.pdf
- Soofastaei, A. (2018). The application of artificial intelligence to reduce greenhouse gas emissions in the mining industry. In M. Pacheco (Ed.), Green technologies to improve the environment on Earth (pp. 25–42). IntechOpen.
- Soofastaei, A. (2019). Energy-efficiency improvement in mine-railway operation using AI. Journal of Energy and Power Engineering, 13(9), 333–348. https://doi.org/10.17265/1934-8975/2019.09.002
- Soofastaei, A., Aminossadati, S. M., Arefi, M. M., & Kizil, M. S. (2016). Development of a multi-layer perceptron artificial neural network model to determine haul trucks energy consumption. International Journal of Mining Science and Technology, 26(2), 285–293. https://doi.org/10.1016/j.ijmst.2015.12.015
- Soofastaei, A., Aminossadati, S. M., Kizil, M. S., & Knights, P. (2016). Reducing fuel consumption of haul trucks in surface mines using artificial intelligence models. In: N. Aziz & R.Kininmonth (Eds.), Proceedings of the 16th coal operators’ conference (pp. 477–489).
- Sun, E., Nieto, A., Li, Z., & Kecojevic, V. (2010). An integrated information technology assisted driving system to improve mine trucks-related safety. Safety Science, 48(10), 1490–1497. https://doi.org/10.1016/j.ssci.2010.07.012
- Sun, E., Zhang, X., & Li, Z. (2012). The internet of things (IoT) and cloud computing (CC) based tailings dam monitoring and pre-alarm system in mines. Safety Science, 50(4), 811–815. https://doi.org/10.1016/j.ssci.2011.08.028
- Teck. (2022). Teck’s technology transformation programs enhance performance, safety and sustainability; Expected to generate $1.1 billion in annualized benefits. https://www.teck.com/media/22-29-TR.pdf
- Tessier, J., Duchesne, C., & Bartolacci, G. (2007). A machine vision approach to on-line estimation of run-of-mine ore composition on conveyor belts. Minerals Engineering, 20(12), 1129–1144. https://doi.org/10.1016/j.mineng.2007.04.009
- Turner, J. (2017). Deep impact: Atlas Copco takes automated drilling to the next level. Mining Technology. https://www.mining-technology.com/analysis/deep-impact-atlas-copco-takes-automated-drilling-next-level/
- United Nations Framework Convention on Climate Change. (2018) . Yearbook of global climate action 2018. Mining and Metals.
- Valenta, R. K., Kemp, D., Owen, J. R., Corder, G. D., & Lèbre, É. (2019). Re-thinking complex orebodies: Consequences for the future world supply of copper. Journal of Cleaner Production, 220, 816–826. https://doi.org/10.1016/j.jclepro.2019.02.146
- Viewpoint Mining Magazine. (2008). Automation keeping underground workers safe at LKAB Malmberget Mine. http://viewpointmining.com/article/automation-keeping-underground-workers-safe-at-lkab-malmberget-mine
- Viewpoint Mining Magazine. (n.d.). Autonomous drilling delivers benefits across the mining operation. http://viewpointmining.com/article/autonomous-drilling-delivers-benefits
- Williams, K. (2018). Teck installs first production-scale machine learning system. CIM Magazine, 13(4), 24–25.
- Woof, M. (2005). Technology for underground loading and hauling systems offers exciting prospects. Engineering and Mining Journal, 206(3), 32.
- Zhang, J., Tang, Z., Xie, Y., Ai, M., & Gui, W. (2020). Convolutional memory network-based flotation performance monitoring. Minerals Engineering, 151, 106–332. https://doi.org/10.1016/j.mineng.2020.106332