Figure 1 is intended to illustrate a simple fuzzy Petri net representation of a wet press incipient break detected to the degree of 0.9 and the possible cause relationships. In close collaboration with GP mill personnel, we will review available sensor suites, data acquisition practices, etc. as they relate to the specific project objectives. Design of experiment methods, such as Taguchi methods, will guide the selection of a series of experiments aimed at collecting the most relevant data. Data mining techniques will be applied to extract useful information from raw data. Figure 2 is a schematic representation of the procedural steps to be pursued in this study.
Data mining is viewed as an exploratory data analysis on large data sets.
The objective here will be to exploit association rules and a sequencing algorithm that will find relationships among process variables and events across time. The output of the data mining module is a set of principal components that are least correlated and most significant in establishing cause-effect relationships. Whereas data mining and principal components are primarily retrospective analysis tools aimed at providing an understanding of the structure and process inter-relationships responsible for classes of web breaks, the proposed modeling effort emphasizes the dynamic interactions between process variables that are the root causes of such breaks. This research team has been developing hybrid neuro-fuzzy and first principle models for complex dynamic systems. Dynamic neuro-fuzzy models will be developed and validated for the wet end and dryer sections of the paper machine focusing on the dependence of web breaks on measurable process variables.