6 ways AI can help Mining reduce carbon emissions

The 2015 Paris agreement on climate change has grown from an original 55 countries to 191 countries who have ratified the agreement1 which aims to limit global warming to below 2 degrees and preferably to 1.5 degrees Celsius. As world leaders commit to this agreement, industry too is stepping up to its responsibility to reduce emissions.

The mining industry is directly responsible for 4% to 7% of greenhouse-gas emissions globally, and indirectly to approximately 28% of emissions.² Reducing emissions is a complex task, one that is different for each mining operation. Mining companies can look to operational efficiencies, recycling, renewables, electrification of equipment, and the diversification of mining portfolios, such as the reduction of coal mining and the inclusion of commodities that can be used in the creation of low-carbon technologies.

With all of these strategies at play, understanding the absolute carbon footprint created by each mine site becomes a complex calculation. The ability to monitor carbon emissions at a local level would assist day to day operations in reducing emissions, as well as corporate strategic planning, reporting, carbon accounting and compliance.

The release of greenhouse-gas emissions varies based on the commodity being mined, the process used, surface proximity and quality. For example, the extraction of lower grade minerals typically consumes larger amounts of energy, and mine sites that are heavily reliant on renewable energy may have fluctuating CO2 emissions as the weather changes and they require off-the-grid or carbon energy sources.

HOW CAN AI HELP THE MINING INDUSTRY WITH THE REDUCTION OF CARBON EMISSIONS?

1. Artificial intelligence can easily handle the billions of data points collected by a mine site, and display critical information and insights on a high level carbon monitoring dashboard for management.
2. At a corporate level, artificial intelligence can help inform strategies for further diversification or technology adoption. AI can be applied to the carbon data across the entire enterprise and assist with scenario planning.
3. AI can predict the renewable power that will be required to sustain future operations – down to a few days in advance. This assists with planning for off-the-grid power utilisation and carbon resources, as well as energy storage and utilisation.
4. AI can provide accurate absolute carbon emission calculations along with metrics for reporting and compliance.
5. Produce insights with AI to help optimise mining operations by setting desired outcomes, such as reduction of power consumption, asset life extension or shutdown planning.
6. AI can provide insights in advance when process and equipment deviate from normal operation as well as predictions on time to failure and increased power consumption. These insights allow for the implementation of advanced asset management strategies, such as predictive maintenance for servicing and replacement of parts.

HOW CAN MINING OPERATORS USE NO-CODE AI?

• VROC streams real-time data from your existing IoT sensors, process information, lab data, CMMS, ERP and even manually gathered information.
• Using VROC’s enterprise grade machine learning technologies and advanced analytics algorithms, the VROC platform prepares and processes this data in its enterprise historian, DataHUB4.0.
• VROC’s AI platform, OPUS models the normal operation of the plant and equipment, and models what past failures and performance look like to detect the extremely small signs that indicate that a failure is imminent. Teams are alerted ahead of time so that they can plan interventions.
• Engineers and technicians can very easily build and deploy their own models in minutes/hours without requiring any programming or data science expertise. Models can be built based on KPIs, process optimisation or reliability outcomes.
• ​This information is then provided through our user customisable live dashboards. The information is calculated and presented in real-time, and provides a quantified confidence level and dominant factors to the prediction.

Through the application of artificial intelligence to existing data, mining operators are able to obtain valuable insights to help them reduce emissions as well as overhead operational costs. As investors demand greater transparency on climate change risk and ESG, it is increasingly important for mine operators to consistently collect, analyse and learn how their initiatives are working, and monitor and plan for reduced emissions.

Learn more about VROC’s solutions for the mining industry, or get in touch for a free demo of how our AI platform can monitor your carbon emissions.

References:

1. https://unfccc.int/process/the-paris-agreement/status-of-ratification
2. https://www.mckinsey.com/business-functions/sustainability/our-insights/climate-risk-and-decarbonization-what-every-mining-ceo-needs-to-know
3. https://sustainabilitycommunity.springernature.com/posts/59131-the-climate-footprint-of-mining
4. https://rmi.org/wp-content/uploads/2018/08/RMI_Decarbonization_Pathways_for_Mines_2018.pdf