Predictive advisory solutions for chemistry management, control, and optimization, TAPPICon24

Process runnability and end-product quality in paper and board making are often connected to chemistry. Typically, monitoring of the chemistry status is based on few laboratory measurements and a limited number of online specific chemistry related measurements. Therefore, mill personnel do not have real-time transparency of the chemistry related phenomena which can cause production instability, such as deposition, higher chemical consumption, quality issues in the end-product and runnability problems. Machine learning techniques have been used to establish soft-sensor models and to detect abnormalities. Furthermore, these soft sensors prove to be most useful when combined with expert driven interpretation.

This study is aimed at utilizing a hybrid solution comprising chemistry/physics models and machine learning models for stabilizing chemistry related processes in paper and board production. The principal idea is to combine chemistry/physics models and machine learning models in a fashion close to white box modelling. A cornerstone in the approach is to formulate explanations of the findings from the models. I.e., explain in plain text what the findings mean and how operational changes can mitigate the identified risks.

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