Abstract

To produce paperboard is a complex process which requires sophisticated monitoring to achieve a paperboard of high quality. Holmen Iggesund is a company in the paperboard manufacturing industry, aiming to produce paperboard of world leading quality. Therefore, they continuously develop their knowledge the production process. In this study, conducted at Holmen Iggesund, the focus is the property of delamination, which is tested with a method called Scott bond. Seven different input signals, measured over a two-year period, were used as input to six different models and used to predict the output (Scott bond). The result showed that a Random Forest model provided the best prediction performance among the tested models. EXplainable Artificial Intelligence (XAI) was then used to better understand the predictions of the Random forest model. It provided an understanding of which input signals were most significant for the model predictions and the values that the input signals should have to predict a high or low value of the output signal. The results from the work give an increased understanding of the process behavior which may help to improve the monitoring of the process and how to counter interact when a process disturbance occurs. It also shows the potential of using complex machine learning models combined with XAI algorithms.