Juha Kortelainen UPM R D Paper and Pulp Finland Avogadro Scale Engineering November 18 19 2003 The Bartos Theater MIT Contents UPM overview J ms nkoski Paper Mill Paper quality and data analysis UPM Key Figures 2002 One of the world s largest paper producers Yearly production corresponds to 170 000 km 2 area covered by paper land area of Massachutes is 20 000 km 2 Mills mainly in Europe North America and China From the Forest to the Customer J ms nkoski Finland year 2002 Founded Capacity Personnel 1888 815 000 t a 940 Products PM5 6 uncoated magazine PM4 coated magazine PM3 label paper 570 000 t a 125 000 t a 110 000 t a J ms nkoski SC PM6 325 000 t a 39 56 g m 9 30 m width 25 m s speed Automation Hierarchy open systems Paper Formation micrometer range variations fibre level paper surface structure small defects optical and printing properties several meters range CD and MD profiles paper web brakes up to 100 km range Paper Web Break Camera Monitoring Image analysis Microscopic image analysis for fiber dimensions fiber length 2 mm width 40 um cell wall 2 um automatic fibre analysers with 1 5 um pixel resolution paper structure with SEM using 0 2 um pixel resolution Real time image analysis for web defects and brakes on line camera scanner defects down to 0 5 mm size Real time microscopic scale 20 um pixel resolution 10 meter web width 25 m s speed 12500 images second with 1 MPix image size On line control Distributed Controls thousands positions Supervisory Controls Paper quality data with web scanner e g cross direction profile control basis weight moisture caliper colour Time series data Multivariate AutoRegressive analysis Time dependent cross correlation disturbance sources Numerically efficient method needed FFT e g 1000 channels 10 s sample period 8 6E6 samples day Problems not efficient enough for long process delays assumes stationary process state during analysis period assumes linearity needs data prehandling about 80 of manual work Data Clustering Automatic clustering often ends up to distinct time periods which are more stationary product grades process states Principal Components k means Neural networks Self Organised Maps by T Kohonen visualization Problems poor numerical efficiency does not practically help in data prehandling Modelling of paper quality Paper strength Optical properties PM control variables dominate some correlation from raw material disturbances Neural Networks Self Organised Maps T Kohonen Clustering of SOM by k means Summary for data amounts hour DCS data 5 Hz rate 10 000 channels multichannel vibration NIR spectra 2E8 samples hour Paper web scanner six channels 1000 Hz 2E7 samples hour typically 5 scanners for one production line Camera systems many fast speed camera applications in use off line image analysis applications real time needs in future 20 um resolution 5E13 pixels hour
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