High resolution operational data from three recovery boilers was analyzed using the Principal Component Analysis (PCA) feature of a multivariate statistical analysis program to identify major operating variables that caused fouling and plugging in three kraft recovery boilers. The results show that not only can PCA be used to visualize the variability related to long-term fouling trends in the boilers, it also can be used to visually distinguish changes in the boiler fouling condition caused by operational variability over a short period of time. This represents a major step forward in identifying operating variables that may be adjusted to minimize fouling, and in developing an on-line fouling monitoring technology based on PCA.
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