ON DEMAND SESSION
|Wednesday, November 10, 2021|
It seems like every week, we hear about a new data breach during which some company has been hacked and their data stolen. Breaches like this result in severe costs to both reputation and the bottom line. It seems clear that companies should do everything in their power to prevent their data from falling into the wrong hands.
And yet, companies should be sharing their data more, not less. Justin will discuss the surprising reasons why.
The incredible benefits of Machine Learning and modern MLOps across a plethora of non-mining industries are already evident. However, astute mining industry leaders and investors want an accelerated pathway to tangibly harness these benefits to best utilise their data, in turn achieving optimal performance at their mining operations.
With new unprecedented precision in measurement and large-scale data collection possible today, Mining Leaders want to move beyond stale PoCs to data impact at scale.
In this energetic presentation, Coert will cover;
How to navigate the operational uncertainty that starts in-ground and permeates the mining value chain to the customer, which requires a vastly different approach to ML than most other industries where their inputs are stable and known.
In early 2019, Roy Hill started using machine learning to forecast our process plant yield / recovery. This solution started off as a simple MVP (minimum viable product) and has grown into a MLOps implementation - This session is about sharing this journey and the learnings from this journey.
Machine learning to classify oil samples: Implementation and change management lessons from WesTrac.
The impact of deep machine learning on the development of sensors and diagnostic systems in the mineral processing industries.
Exponential growth in big data and recent breakthroughs in deep learning continue to drive the widespread adoption of machine learning in industry. In this presentation, the impact of deep learning in the process industries will be reviewed, focusing on sensor data analytics and process monitoring.
This will include examples of the monitoring of bulk particulates on conveyor belts, the underflow of hydrocyclones, froth image analysis and signal processing in general, and a brief look at the emerging application in modelling and control.
- Data-driven decisions in geoscience may be achieved through a machine augmented and human-driven approach
- Machine learning can be used to produce efficient, consistent and repeatable outcomes, but its deployment in industry practice is challenging
- Deployable machine learning (or data science in general) needs to address transparency, their seamless integration into human interpretation workflow, and generating solutions that are acceptable by domain experts
- Machine learning is used not only used for structured data but also unstructured data towards building AI for geological knowledge discovery
Whether it’s automated haulage, decarbonisation, robotics, remote work or machine learning, we want to connect the best and most creative minds to the opportunities and challenges we face as an industry, and change the very nature of the way we work.
We believe that the ingenuity and energy that exists in the METS sector unlocks potential for companies like us, our peers and partners, and ultimately the nation. As an industry, we must continue to work on the new ideas and solutions to make what we do better every day.
Over the past two years, we've been working together with the METS sector and with the women and men on the front line of our operations to improve safety outcomes across our mobile maintenance teams - developing Dash Tools (dash.bhp.com).
In this session, we'll share our journey and lessons learnt from our work on elimination of live work with Dash Tools - putting sensors in harm's way, not people - and how they can be applied to Machine Learning challenges.
|Thursday, November 11, 2021|
As technology advances, data can provide opportunities to solve problems in various areas, including accelerated research, increased transparency, and the identification of novel solutions to problems. Unfortunately, the appropriate data are not always readily available. The Global Mining Guidelines Group (GMG) has produced a Guideline for Sharing Open Data Sets in Mining to assist in this area. The purpose of this guideline is to provide best practices for data sharing for those within the mining industry based on existing initiatives so they can benefit from open data.
The Data Fit Organisation - insights on the framework and examples of how it is changing the roles of workers across mining organisations
Realising value through data is hard and outcomes can be inconsistent. The technology is getting better but process and capability are still developing. A successful data workflow, one that is embedded in the business, invokes all roles to consistently realise value. It follows then, that all roles need to be data capable and demonstrate an understanding of the data workflow. Having an industry framework or shared way to build data capability in support of all roles across an organisation is therefore critical.
Machine learning and cloud computing hold the immense promise of adding value to mining operations. A new domain modelling solution delivers significant improvements in processing speed, ease of setup and use, alongside the ability to use all your data and in a secure manner. This paper will outline how access to cutting-edge machine learning has never been easier and how it delivers confidence in domaining and modelling decisions.
Using machine learning and data analytics to improve drilling technologies and real-time geological modelling