The power generation sector is under increasing pressure to optimize processes and reduce maintenance-related costs through digitalization. However, many players do not yet feel ready for a comprehensive introduction of AI solutions. This is partly because many of their technical assets are diverse and have grown over time. The fact that many IT and data science departments are already overloaded is another frequently cited problem. And last but not least, many daily investigations require knowledge that only experienced engineers can provide and which cannot be fully automated in the medium term.

This webinar uses practical examples to show how leading players in hydropower and thermal power generation are solving these challenges. The use cases presented include the detection of problems with individual turbines at Norwegian hydropower producer Hafslund Eco and the optimization of start-up processes at German player EnBW. The presentation also covers the use of advanced analytics to detect leaks in boilers and fouling of heat exchangers. The use cases will be presented by Mr. Harald Piringer and Mr. Thomas Mühlbacher, who have been supporting companies in power generation for many years with the self-service solution Visplore. The participants of the webinar will thus learn

• which solutions leading companies use to solve the above-mentioned issues in maintenance and operations
• what options are available to give engineers access to advanced analytics even without programming and data science
• how this can reduce the workload for analyses by 90% and save many hours of precious expert time

The participants thus gain important learnings for their own digitalization journey. In particular, they will learn how AI and self-service analytics complement each other to get the most out of the digitalization of their own plants based on the experience of their engineers. Register now to not miss this thought-provoking input from high-level speakers!

Learning Objectives:

• Why AI can only be a part of the digitalization roadmap and how players can start lean to complement AI

• Which solutions leading companies in hydropower and thermal power generation use to optimize their maintenance and operations

• What options are available to support operators and maintenance engineers in their daily investigations by advanced analytics even without need for data science

• How this can reduce the workload for analyses by 90% and save many hours of precious expert time