When will it break? A hybrid soft computing model to predict time-to-break margins in paper machines

Piero P. Bonissone; Kai Goebel

doi:10.1117/12.455868

6 December 2002 When will it break? A hybrid soft computing model to predict time-to-break margins in paper machines

Piero P. Bonissone, Kai Goebel

Proceedings Volume 4787, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation V; (2002) https://doi.org/10.1117/12.455868
Event: International Symposium on Optical Science and Technology, 2002, Seattle, WA, United States

Abstract

Hybrid soft computing models, based by neural, fuzzy and evolutionary computation technologies, have been applied to a large number of classification, prediction, and control problems. This paper focuses on one of such applications and 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 neural 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.

Citation Download Citation

Piero P. Bonissone and Kai Goebel "When will it break? A hybrid soft computing model to predict time-to-break margins in paper machines", Proc. SPIE 4787, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation V, (6 December 2002); https://doi.org/10.1117/12.455868

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