Deep.Meta is helping solve this by applying AI and machine learning to steel producers’ existing data to drive energy efficiency. Doing that we can cut a significant portion of global CO2 emissions.
We impact the bottom line
That’s because 40% of Steel Production costs are from Energy.
Much of this is currently fossil fuel-based which means driving reduction in Energy and directly cuts CO2 emissions.
energy saved every month
Energy to heat 250 homes a month
reduction in time to head slabs
CO2 prevented every month
Emissions by 470 cars a month
Deep.Meta’s software optimises production by harnessing data from existing sensors at steel plants to enable real-time insights to warn operators of potential defects across the melt shop, caster and rolling mill.
Our Algorithms are designed to assist producers in minimising the energy to melt or reheat products, helping them achieve significant bottom line savings combined with reduced carbon emissions.
Efficiency of production can be improved significantly through our specialised scheduling algorithms. We help you increase your output capacity by developing optimised, dynamic order schedules.
Our Vision for the Future of Metals Circularity
Optimising production by reducing energy consumption and material loss is just the first way we are achieving ‘AI-enabled circularity’. In the future, we want to expand our offering to make it simpler to track steel through the supply chain.
Supply Chain Complexity
The Supply Chain is complex and it can be difficult to know the condition of steel at the end of its life, like when an office building is decommissioned. Deep.Meta will identify whether a steel beam used in a office block can have a second lease on life as a park swing without having to remelt it, which helps to reduce overall energy consumption.
Since steel is the world’s most widely used and recycled metal, tracking it becomes a way of remotely monitoring structures, products, and its lifetimes — a concept we call ‘iron-dating’.