Focusing on the critical pre-heater and kiln process stages of cement production, Carbon Re optimises fuel use to reduce carbon emissions.
Carbon Re’s AI-powered solution is specifically designed for the cement pyroprocess. By utilising the latest advances in machine learning, it identifies energy inefficiencies, cuts costs and reduces carbon emissions.
Optimise fuel consumption and reduce specific heat consumption to protect plants from rising fuel costs, a volatile alternative fuels market, and low margins.
Effectively control the quantity of C3S and free lime per tonne of clinker. Alert teams to potential blockages and minimise the impact of unplanned shutdowns.
Reduce fuel-derived emissions and increase thermal substitution to achieve sustainability goals and avoid carbon taxes.
Using historical data, Carbon Re models the bounds of optimal performance to tailor its predictions and recommendations to provide optimal operating conditions based on real-time information.
These predictions and recommendations are integrated into a plant’s advanced process control systems in closed loop, optimising the pyroprocess without intervention from the control room operator.
Carbon Re’s models are continually retrained to match actual plant performance, negating the challenge of model drift.
The image below demonstrates the savings a typical plant can make with Carbon Re. Click here to receive a customised calculation of your plant’s potential savings.
Carbon Re is built on cutting-edge systems architecture and machine learning frameworks tailored specifically for the cement industry.
Leveraging cloud-based storage databases, Carbon Re manages large and complex datasets. Our team conducts pioneering machine learning research, adhering to the highest levels of scientific rigour.
