Monitoring kraft recovery boiler fouling using principal component analysis
Source abstract
Abstract
Researchers analyzed high resolution operational data from three recovery boilers using the principal component analysis (PCA) feature of a multivariate statistical analysis program to identify major operating variables that contributed to fouling and plugging. The results show that PCA can be used to visualize the variability relative to long-term fouling trends in the boilers and to graphically distinguish changes in the boiler fouling condition caused by operational variability over a short period. This represents a major step forward in identifying operating variables that might be adjusted to minimize fouling, and in developing an online fouling monitoring technology based on PCA.
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Research context
OpenAlex topics: Fault Detection and Control Systems · Mineral Processing and Grinding · Spectroscopy and Chemometric Analyses
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (9)
Show source-provided references
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