#7846. Application of Machine Learning in Industrial Boilers: Fault Detection, Diagnosis, and Prognosis

October 2026publication date
Proposal available till 15-05-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Industrial and Manufacturing Engineering;
Biochemistry;
Process Chemistry and Technology;
Chemical Engineering (miscellaneous);
Bioengineering;
Filtration and Separation;
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Abstract:
Enhancement in boiler efficiency via controlled operation could lead to energy savings and reduction in pollutant emission. Activities such as scheduled maintenance could be improved by increasing boiler availability and reducing running costs. However, the time interval between recommended maintenance is varied depending on boilers. The application of fault detection, diagnosis and prognosis (FDDP) in industrial boilers plays an important role in optimizing operation, early-warning of faults, and identification of root causes. This review discusses the application of machine learning (ML)-based algorithms (knowledge-driven and data-driven) for FDDP, thus allowing the identification of fit-for-purpose techniques for specific applications leading to improved efficiency, operability, and safety.
Keywords:
Diagnosis system; Fault detection; Industrial boiler; Machine learning; Prognostics

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