Using hybrid modelling to mitigate the effects of variable fuel quality in an energy boiler
07.juni, 08-08.45 CEST – Teams event
Variability in the quality of the fuel needed for their operations is a big challenge for today’s thermal-energy producers.
Poor quality fuel can significantly contribute to harmful phenomena like fouling and corrosion in the critical structures of an energy boiler, including on its heat-exchange (HX) surfaces. This is why Sumitomo SHI FW, University of Oulu, and SINTEF AS have been working on a pilot case with COGNITWIN – a new process-industry framework that integrates AI, smart sensors and machine-learning. Through the pilot we are targeting to improve the monitoring of fouling, and how to better support the control of fouling through the use of a digital twin.
Names of presenters: Mika Liukkonen (Sumitomo SHI FW), Enso Ikonen (University of Oulu), Anders Hansen (SINTEF AS)