How Machine Learning can accelerate low carbon concrete adoption
Thursday, Jun 2, 2022
Cement is responsible for 8% of worldwide CO2 emissions. Fortunately, the footprint can be reduced by 60% if burnt limestone, the main ingredient, is replaced partly by limestone powder. However, such low-carbon recipes react much more sensitive to changes in the chemical and mineralogical composition, limiting reliable production to laboratory conditions.
Alcemy is changing this. Robert presents a Machine Learning control and data analytics case study to optimize production processes of multiple plants such that low-carbon cement and concrete can be manufactured in real plants and at scale.