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Metals & Mining

Machine-learning software that identifies opportunities to cut CO2 emissions

Key facts

4-8 wks

lead time for live implementation

10-20%

target reduction window in annual CO₂ tonnage

Next Steps
Book a call
Email sales
Downloads

Top business benefits

Reduced CO₂ emissions

Reduced emissions costs (carbon credits)

Reduced direct power costs

Improved operational stability

Optimised power usage

Reduced operational costs

Improved reputation

In more detail

Full description

Companies must safely and significantly reduce their CO₂ emissions to assist governments globally in achieving their net-zero targets. This directional change has to be balanced with the requirement to satisfy ever-growing global demands and production safety.

This innovative machine-learning software platform uses operational data, plant configurations, installed capacity, current production performance and operating model data to dynamically present contextualised insights. It aids decision-making and drives daily operational actions to improve energy management and reduce flaring, costs and emissions.

The challenge

Metals companies have a major role to play in achieving net-zero targets for CO₂. To do this, current ways of operating need to be overhauled in a manner that sustains production rates, protects people and the environment, and supports the net-zero agenda. This is a tall order, and investment in technology and innovation alone will not achieve this, but it will go a long way to support this ambition.

The starting point in reducing the CO2 footprint of a production asset is to understand how much CO2 is being produced daily via operational demands.

Without reliable data sets or the resources required to provide analytical insights of ongoing plant configuration changes, start-up optimisation and process optimisation, net-zero operations from production assets and efficient cost saving energy management will be difficult to achieve.

The approach

This software platform uncovers valuable information hidden deep within existing equipment and production data. It enables companies to access new insights and seek out new smart emissions management solutions. In turn, they can reduce operational costs without any CAPEX investment.

The platform harnesses the power of physics-guided machine learning and optimisation technologies to continuously calculate the lowest achievable emissions at any time – based on current plant configuration and production targets. The software highlights excess emissions problems and their causes via the intuitive user interface in near real-time. It also identifies necessary configuration changes that will resolve emissions issues.

Once implemented, customised and fully live – which takes from four to eight weeks – this cloud-based platform is proven to help operators significantly reduce annual CO₂ emissions through proactive daily operational changes. The contextualised insights dynamically adapt and highlight opportunities to remove excess CO₂ emissions and energy leakage, while also optimising the production process.

This also delivers annual financial savings through a reduction in emissions and fuel costs. It improves asset energy management and drives cultural change and awareness. These insights better inform decisions and day-to-day operational actions that operators can take, as well as highlighting longer term material improvement opportunities.

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