Open Collections

Paper machine data analysis and optimization using wavelets

University of British Columbia

This thesis describes paper machine data analysis methods using wavelet and wavelet packets and their applications, aiming at improving paper machine efficiency. First, the validity and accuracy of the wavelet transform are confirmed by applying discrete wavelet transform to paper samples using both the paper machine on-line scanner data and off-line analyzer data. Results show that the wavelet transform can represent paper machine process data economically without loss of detail, and that it can also provide excellent visualization to the operator. Process monitoring and control performance assessment are then studied. By separating controllable and uncontrollable variations in the cross machine direction profile, the achieved performance and the best possible performance of the system are evaluated. A CD performance index can be calculated on-line, providing the operator with a quick assessment of the control system performance. Both wavelet and wavelet packets are used and the results are compared. Finally, the processed paper machine profiles obtained through wavelet and wavelet packet analysis are used for trim-loss optimization taking into account paper quality. Three different optimization schemes are compared and the potential savings through trim-loss optimization before and after improving control are analyzed.

Thesis/Dissertation

application/pdf

2009-06-12

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http://hdl.handle.net/2429/9065

Electrical and Computer Engineering

University of British Columbia

UBCV

DSpace