Abstract

A study was conducted to examine the most likely parameters responsible for poor dregs settling at a kraft mill over a 2.5-year period, using multivariate data analysis (MVDA) and machine learning (ML) techniques. The dregs settling behavior seems to be seasonally influenced, implying that wood quality variation can be a factor. The results from the MVDA/ML analysis show that poor dregs settling is correlated to incomplete combustion and/or low load conditions in the recovery boiler, low sulfidity in the causticizing plant, and high flow in the green liquor–weak wash cycle. Compositions of dregs and black liquor were also examined to identify correlations with impaired dregs settling. The results show that poor dregs settling strongly correlates with high silicon (Si) content in dregs and moderately correlates with high iron (Fe) and high aluminum (Al) contents, and with low bulk density in dregs. For mills that experience dregs settling or green liquor filtering issues, regular compositional analyses of dregs, green liquor, weak wash, and black liquor are recommended in order to monitor the dynamics of silicon and other constituents in the recovery cycle.

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