This paper presents a methodology, implemented for a real industrial-scale circulating fluidized bed boiler, to mitigate the risk of heating surfaces exposed to an intensive particle erosion process. For this purpose, a machine learning algorithm was developed to support the boiler reliability management process. Having a tool that can help mitigate the risk of uncontrolled power unit failure without expensive and technically complex modernization is desired. A virtual procedure can be seen as a milestone towards the application of digital models to the diagnostic procedure of large power units, providing answers for many scenarios that cannot be normally studied during boiler operation. The predictive model developed in this work allows us to provide the requested feedback to the unit control systems regarding possible changes in boiler operating conditions and reduce the erosion effect. The functionality of the discussed methodology is investigated via application of the developed multiphase computational model.
Authors
- Jarosław Grochowalski,
- Agata Widuch,
- Sławomir Sładek,
- Bartłomiej Melka,
- dr inż. Marcin Nowak link open in new tab ,
- Adam Klimanek,
- Marek Andrzejczyk,
- Marcin Klajny,
- Lucyna Czarnowska,
- Bartłomiej Hernik,
- Zhou Minmin,
- Sebastian Pawlak,
- Wojciech Adamczyk
Additional information
- DOI
- Digital Object Identifier link open in new tab 10.1016/j.powtec.2023.118651
- Category
- Publikacja w czasopiśmie
- Type
- artykuły w czasopismach
- Language
- angielski
- Publication year
- 2023
