CatenaX
Catalysts are central to industrial processes, but during operation they are subject to complex ageing processes that impair their activity and stability. To maintain catalyst yield, they are therefore replaced at regular intervals. Service life is generally estimated based on past experience and includes safety margins to ensure consistently high conversion rates. Since every catalyst change entails high direct and indirect costs, the aim is to minimise downtime without risking losses in yield.
With its CatenaX software, LOGE aims to develop a comprehensive understanding of ageing in industrial catalysts and to reassess it. The solution combines experimental characterisation, thermodynamic-kinetic modelling, and machine learning. Sensor data from the plant is integrated directly into the software, enabling on-board monitoring of catalyst functionality, activity, selectivity, and ageing. Machine learning methods identify ageing mechanisms such as sintering or poisoning based on characteristic sensor signatures, allowing precise predictions of remaining service life and the optimal time for catalyst replacement.
CatenaX thus lays the foundation for proactive catalyst management and predictive maintenance: premature replacements and downtime are minimised, regeneration strategies are optimised, and resource efficiency, plant availability, and sustainability are enhanced – in line with Industry 4.0 principles.
Antragsnummer: 85070128 / 80287055
Projektlaufzeit: 16.02-2026 und 30.06.2028
