We are delighted to announce a dynamic cooperation between FLSmidth Cement and Carbon Re, a UK based climate tech company that applies AI and machine learning to uncover energy efficiencies and reduce costs and carbon emissions in industrial processes.
This cooperation is about the integration of the onsite FLSmidth Cement’s ECS/ProcessExpert® advanced process control software known as PXP with Carbon Re’s AI-powered cloud platform providing cement producers with access to state-of-the-art process optimisation capabilities.
Our cooperation unlocks the full potential of ECS/ PXP Data books, serving as an open platform for AI models. By seamlessly integrating ECS/ PXP Data books with Carbon Re for predictive modelling, we’re helping the cement industry address its unique challenges and elevate optimisation to new heights.
The fusion of advanced process control techniques with AI-driven predictive modelling empowers cement manufacturers with unprecedented foresight into production dynamics. But our collaboration extends far beyond mere integration – it fosters a comprehensive ecosystem of interoperable tools.
Driven by a shared commitment to empowering our customers, these solutions in combination offer tailored strategies to address diverse challenges and uncover new opportunities for growth. Whether it’s optimising energy consumption, reducing emissions, or enhancing product quality, we’re here to support our customers every step of the way.
Together, let’s pave the way towards a sustainable tomorrow.
About FLSmidth Cement
FLSmidth Cement is a technology and service supplier with a passion to help our customers produce cement efficiently. After 140+ years of pioneering new innovations, we are uniquely positioned to be at the forefront of our industry’s green transition.
https://www.flsmidth-cement.com/
About Carbon Re
Carbon Re is an Industrial AI company on a mission to significantly reduce the large carbon footprint of energy-intensive industries. Starting with cement and steel, we are working to remove gigatonnes of CO2-equivalent emissions from their production processes using state-of-art machine learning, control, and fundamental AI research in these areas.