Predicting Wet-End Web Breakage in Paper Mills

Piero Bonissone, Kai Goebel, and Yu-To Chen

This paper presents a systematic process for building a predictive model to estimate time-to-breakage and provide a web break tendency indicator in the wet-end part of paper making machines. Through successive information refinement of information gleaned from sensor readings via data analysis, principal component analysis (PCA), adaptive network based fuzzy inference system (ANFIS), and trending analysis, a break tendency indicator was built. Output of this indicator is the break margin. The break margin is then interpreted using a stoplight metaphor. This interpretation provides a more gradual web break sensitivity indicator, since it uses more classes compared to a binary indicator. By generating an accurate web break tendency indicator with enough lead-time, we help in the overall control of the paper making cycle by minimizing down time and improving productivity.

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