Monitoring kraft recovery boiler fouling using principal component analysis

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.