Spartan UK is trialling AI technology to reduce emissions and energy consumption
The UK-based company Spartan UK has launched a pilot project with the industrial AI start-up Deep.Meta to reduce emissions and improve energy efficiency at its heavy plate mill in Newcastle upon Tyne.
The project utilises Deep.Optimiser-PhyX, an AI-powered digital twin that uses live data from the natural gas-fired preheating furnace to optimise heating cycles and production planning in real time. By making the process more efficient and stable, the system aims to reduce both CO₂ intensity and energy consumption. Spartan UK is a subsidiary of the Ukrainian steel group Mitinvest and operates a heavy plate rolling mill in Newcastle-upon-Tyne with a production capacity of up to 200,000 tonnes of heavy plate per year.
Deep.Meta’s modelling for Spartan UK’s operations shows that the technology could enable a reduction in CO₂ intensity of up to 10 per cent, subject to validation in live production. The pilot trial is focusing on smarter energy use, improving process stability and reducing fluctuations – all crucial factors in a market where energy and CO₂ costs account for a significant proportion of total production costs.
“Deep.Meta is a trusted partner, and we are trialling the Deep.Optimiser solution in response to rising energy and CO₂ costs,” said Michael Brierley, CEO of Spartan UK. “Improving production efficiency is of great importance, as energy costs make up a significant part of our cost structure.”
The collaboration marks another important step for the Metinvest Group towards decarbonisation and digital innovation across all its plants. By trialling advanced AI solutions at Spartan UK, the group aims to roll out successful approaches across its entire steel portfolio in the future.
The British start-up Deep.Meta states that, using its AI software Deep.Optimiser-PhyX, a UK-based start-up has developed an AI-powered Digital Twin – an intelligent digital replica of the steel production process that combines physics and machine learning to optimise furnace operation, thereby simulating years of production in just a few hours.
Source: Metinvest/Deep.Meta