首页|Curtin University Researcher Discusses Findings in Machine Learning (The future of underground mine planning in the era of machine learning: Opportunities for e ngineering robustness and flexibility)
Curtin University Researcher Discusses Findings in Machine Learning (The future of underground mine planning in the era of machine learning: Opportunities for e ngineering robustness and flexibility)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Researchers detail new data in artificial intelli gence. According to news reporting from Kalgoorlie, Australia, by NewsRx journal ists, research stated, “Machine learning (ML) applications are increasing their footprint in underground mine planning, enabled by the gradual enrichment of res earch methods.” The news journalists obtained a quote from the research from Curtin University: “Indeed, improvements in prediction results have been accelerated in areas such as mining dilution, stope stability, ore grade, and equipment availability, amon g others. In addition, the increasing deployment of equipment with digital techn ologies and rapid information retrieval sensor networks is resulting in the prod uction of immense quantities of operational data. However, despite these favoura ble developments, optimisation studies on key input activities are still siloed, with minimal or no synergies towards the primary objective of optimising the pr oduction schedule. As such, the full potential of ML benefits is not realised. T o explore the potential benefits, this study outlines primary input areas in pro duction scheduling for reference and limits the scope to six key areas, covering dilution prediction, ore grade variability, geotechnical stability, ventilation , mineral commodity prices and data management.”
Curtin UniversityKalgoorlieAustraliaAustralia and New ZealandCyborgsEmerging TechnologiesEngineeringMachin e LearningMining and Minerals
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