Chemicals
enterpriseTarget: Large-scale chemical plant
If you are a chemical plant dealing with volatile energy costs — this project developed a decision intelligence platform that can reduce energy costs by 10–15% and CO2 emissions by 15–20%.
FLARE · Project
AI-Driven Decision Platform for Energy-Efficient and Resilient Industrial Production
manufacturingTestedTRL 5
Imagine a factory that acts like a smart home, automatically switching power sources when electricity is cheap or green. It uses a digital twin—a virtual mirror of the plant—to predict problems and change production lines on the fly. This helps heavy industry keep running smoothly even when energy prices spike or power grids flicker.
By the numbers
10–15%
energy cost reduction
15–20%
CO2 emissions reduction
10–15%
waste reduction
10–15 minutes
improved flexible response time
The business problem
What needed solving
Energy-intensive industries struggle with volatile energy prices and unstable renewable power supplies, which threaten production stability and increase carbon footprints.
The solution
What was built
A decision intelligence platform combining digital twins, AI, and predictive analytics to orchestrate energy use and reconfigure production lines in real-time.
Audience
Who needs this
Chemical plant operatorsSteel mill managersCement factory ownersIndustrial energy procurement officers
Business applications
Who can put this to work
Steel
enterpriseTarget: Steel mill
If you are a steel mill dealing with unstable renewable energy supply — this project developed a system that improves flexible response times to 10–15 minutes to keep production steady.
Cement
enterpriseTarget: Cement manufacturer
If you are a cement manufacturer dealing with high waste and emissions — this project developed an energy-aware orchestration tool that cuts waste by 10–15%.
Frequently asked
Quick answers
How much will this system cost to implement?
Based on available project data, specific pricing or implementation costs are not provided.
Is this technology ready for industrial scale?
Yes, the project uses three high-impact European example sites as living laboratories to demonstrate the path from prototype to market adoption.
Who owns the IP and how is licensing handled?
Based on available project data, IP and licensing details are not specified in the project description.
How does this integrate with existing factory hardware?
The platform uses digital twins and AI to allow process equipment to switch between energy sources and reconfigure production lines in real-time.
What is the timeline for deployment?
The project runs from June 1, 2026, to May 31, 2030, indicating a multi-year development and testing cycle.
Consortium
Who built it
The consortium is highly balanced for commercialization, featuring 16 partners with a 50% industry ratio (8 industrial partners). The presence of 5 SMEs suggests a focus on agile implementation and scalable software solutions, while the mix of 4 universities and 4 research centers ensures the AI and digital twin foundations are scientifically robust.
How to reach the team
Contact Munster Technological University in Ireland
Next steps
Talk to the team behind this work.
Contact us to connect with the FLARE consortium for pilot site opportunities.
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