The mining industry is increasing using AI to optimize processes, enhance decision-making, derive value from data, and improve safety. While there are many benefits to its implementation, it is still very new and there is a lot of work that needs to go into successful application. Planning, research, communication, and change management are all essential foundations for implementation.
In an industry that can be reluctant to change, industry collaboration is essential in order to communicate the advantages of AI, as well as to establish best practices for moving forward with AI technology. During this virtual forum, the industry will come together to collaborate on the future of AI from a wide range of perspectives.
Advanced Analytics in Mining Industry; Opportunities and Challenges
Ali Soofastaei, AI Global Projects Leader, Vale
A few years ago, it would have been unthinkable to regard complex algorithms and analytics (in general) as the fundamental pillar of digital transformation in mining companies. Today, however, there are multiple opportunities for high-impact applications in different sectors, including mining operations, thanks to the availability of a massive amount of data generated along the mining industry’s value chain, storage capacities, the new analysis possibilities, and data processing through more powerful and complex algorithms. Advanced analytics use has only been able to capture a small part of the forecasted potential, which means that there are still plenty of opportunities to deploy analytics in the mining industry.
“Analytics” applications are generally divided into three groups: descriptive, predictive, and prescriptive analytics. The optimization should also be included in the analytics applications to make a sustainable and applicable solution for the industries. These applications can take different use cases depending on each stage of the mining industry value chain. This presentation will explain some technical details of advanced analytics projects in the mining industry to clarify the opportunities and challenges in mining companies’ digital transformation programs.
Dr Ali Soofastaei is a global artificial intelligence (AI) projects leader, an international keynote speaker, and a professional author. He completed his Ph.D. and Postdoctoral Research Fellow at The University of Queensland, Australia, in the field of AI applications in mining engineering, where he led a revolution in the use of deep learning and AI methods to increase energy efficiency, reduce operation and maintenance costs, and reduce greenhouse gas emissions in surface mines. As a scientific supervisor, for many years, he has provided practical guidance to undergraduate and postgraduate students in mechanical and mining engineering and information technology. Dr Soofastaei has more than fifteen years of academic experience as an Assistant Professor and leader of global research activities. Results from his research and development projects have been published in international journals and keynote presentations; He has presented his practical achievements at conferences in the United States, Europe, Asia, and Australia. He has been involved in industrial research and development projects in several industries, including oil and gas (Royal Dutch Shell); steel (Danieli); and mining (BHP, Rio Tinto, Anglo American, and Vale). His extensive practical experience in the industry has equipped him to work with complex industrial problems in highly technical and multi-disciplinary teams. For more information please see www.soofastaei.net
AI-enabled predictive maintenance for mobile equipment
Kevin Urbanski, Co-Founder and CTO, Rithmik Solutions
Why is artificial intelligence such a good tool when it comes to predictive maintenance? Kevin Urbanski, the CTO at Rithmik Solutions, will go into the specifics of the techniques and why they fit so well, along with what implementing an AI-enabled predictive maintenance project looks like in practice, including when and how to bring in AI. He’ll also take a look at some of the valuable possibilities these powerful algorithms generate, some of which would never exist otherwise.
Kevin Urbanski has 12 years of experience in industrial measurement, analytics and automation. For Channel Systems Incorporated, he designed and implemented custom measurement, automation, machine vision and spectral imaging systems for applications ranging from assembly line anomaly detection to crime scene investigation. At Matrikon (acquired by Honeywell), he was Technical Lead for the hardware interface of the Mobile Equipment Monitor (MEM) solution, which was one of the earliest data collection and reporting systems for mobile mining equipment. In that role, he traveled to mines all over North and South America interfacing data collection hardware to the equipment and MEM architecture. Following his time at Matrikon/Honeywell, he was a Senior Technical Analyst for the real-time systems team at Teck Resources, supporting and helping the company leverage its Mobile Equipment Monitor implementation as well as other data-driven solutions within maintenance, reliability and operations. He founded Rithmik Solutions because, after completing studies in Machine Learning, he realized artificial intelligence was the key to unlocking the value in data he’d spent the bulk of his career gathering and analyzing.
What can AI do?
Javen Shi, Professor at the University of Adelaide, Chief Scientist of Smarter Regions Cooperative Research Centre bid
In this talk, Javen will show you what AI and machine learning can do with applications in diverse domains ranging from agriculture, mining, sport, manufacturing, automated trades, computer vision, water utility, health, education and others. One of his very recent achievements is that he formed the team DeepSightX recently won 2nd place in a global competition to predict mineral deposits in June 2019. One thousand people from 62 countries had entered. In merely 3 months, his team outperforms professional companies with over 40 years experience in mining. More recently (Sept. 2019), he and his team helped MIDDOL Pty Ltd win the Golden Prize (1st place) at SAIC Volkswagen’s Logistics Innovation Day in Shanghai. The prize was for the development of MIDDOL’s digital factory, a dashboard that can monitor the processes taking place on the factory floor powered by AI beating VW’s suppliers around the global. In Gawler Challenge 2020, his team DeepSightX developed models to predict Ternary domaining and pixel level distributions of minerals and geology. In AUS/NZ Bushfire Data Quest 2020, his team (in collaboration with USC and CSIRO) developed models to predict fire scars and spread.
Professor Javen Shi is the Chief Scientist of Smarter Regions Cooperative Research Centre (CRC) bid (~$160M over 10 years), Director in Advanced Releasing and Learning, Australian Institute for Machine Learning (AIML), and Founding Director of Probabilistic Graphical Model Group at the University of Adelaide. He is a leader in machine learning in both high-end AI research and also real world applications with high impacts. He has transferred his research to diverse industries including agriculture, mining, sport, manufacturing, bushfire, water utility, health and education. Recent awards include 1) 2nd place from a global mining competition OZ Minerals Explorer Challenge 2019 (over 1000 participants from 62 countries), 2) Golden prize (1st place) from Volkswagen in 2019 (digital factory powered by AI), 3) Finalist of SA Department of Energy and Mining’s Gawler Challenge in 2020 organised by (2000+ participants from 100+ countries) with his work being considered as “The most innovative modeling approach we’ve seen” by the judge panel, and 4) the top winning team (in collaboration with USC and CSIRO) in AUS/NZ Bushfire Data Quest using AI to predict fire scar and spread. He has initiated Smarter Regions CRC bid to empower regional Australia to gain the maximum benefit from the AI revolution. Together the partners of the CRC bid have committed to invest $90M cash and $156M in-kind. The bid seeks to secure $71.25M from the CRC Program and aims to transform existing industries and grow a technology sector in and for regional Australia.
To get involved, click “register”.
November 17 | 8am-11am (EST)| 2pm-5pm (CEST)| 9pm-12am (AWST)|