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Development of Online Monitoring Scheme for Prediction and Diagnosis of Sheet-Break in a Pulp and Paper Mill

Abstract Sheet-break is a long standing problem in pulp and paper industry. If advance warnings can be issued ahead of time, corrective actions may be taken to prevent some of the breaks. In this paper, we describe the development and implementation of an online monitoring scheme. A data based approach was taken in this analysis. PCA was used to model the process, and a combined index based on the Hotelling's T 2 and Q-statistics was developed as a sheet-break predictor. As the process is subject to external disturbances, grade changes, and frequent interruptions, preprocessing of the data played an important role in getting consistent results. We used several novel techniques for data selection, scaling and modelling. The models were validated using a large validation data set with known fault condition.