Heidelberg Materials improves performance by deploying Gigaton’s Self-Learning Control system
reduction in fuel cost index by leveraging a 2.2% reduction in specific heat consumption
reduction in C3S variability
reduction in fuel-derived carbon emissions
weeks to go live with plant
This collaboration aimed to enhance the Mokra plant with Gigaton’s AI to reduce fuel costs, lower emissions, and improve clinker quality.
The plant maintains a stable production process with a thermal substitution rate of up to 86%, primarily using refuse-derived fuels. Yet, the plant team sought to extend the capabilities of its EO to deliver improved automation and optimisation for the kiln.
Gigaton was chosen because it is the market-leading AI platform for pyroprocess optimisation. Gigaton’s AI and machine learning models give operators dynamic targets for better decision-making and efficiency.
To learn more, download the full results of our deployment below.
We had been searching for a system to enhance our Expert Optimizer with AI for a long time and Gigaton’s AI platform looked promising. ABB’s confidence in the company, along with Gigaton’s understanding of the challenges of cement production and their expertise in AI, were key to us working together.
Download the full results
Through Gigaton’s cloud-based AI platform, Heidelberg Materials is unlocking the full potential of its process, laboratory, and chemical data, enabling real-time, dynamic optimisation for maximum efficiency and sustainability.