Image classification of the furnace bed height in recovery boilers, TAPPI Journal July 2026

 


Application: This work introduces a simple method for classifying char bed height in a recovery boiler during operation and deriving a corresponding bed height parameter. This may help identify relationships between bed height and recovery cycle operation, providing deeper insight into the mechanisms driving bed dynamics, and ultimately contributing to more stable combustion.

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Author: Torsten Meyer and Nikolai DeMartini
Image classification of the furnace bed height in recovery b
ABSTRACT: The goal of this study is to develop an approach for classifying char bed height in a recovery boiler during operation. The resulting classified images form a dataset that can be used by the mill to support operational improvements. The study integrates data from the digester, evaporation plant, recovery boiler, and recausticizing plant, along with the classified image data. A convolutional neural network (CNN) was trained on bed camera images that were manually annotated to classify furnace bed height. The model was then used to generate a binary bedheight parameter over a 16-month period, and an extreme gradient boosting (XGBoost) model was applied to identify relationships with operating parameters across the recovery cycle. The outcomes of this research may offer deeper insights into the processes underlying bed dynamics. Ultimately, the developed system may serve as a tool for optimizing bed conditions and supporting stable combustion.
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