Juha Kortelainen UPM R

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Juha KortelainenUPM RD, Paper and
PulpFinlandAvogadro Scale Engineering
November 18-19, 2003The Bartos Theater, MIT
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Contents

• UPM overview
• Jämsänkoski Paper Mill
• Paper quality and data analysis

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UPM Key Figures, 2002

• One of the world's largest paper producers

• Yearly production corresponds to 170,000 km2 area
  covered by paper! (land area of Massachutes is
  20,000 km2)
• Mills mainly in Europe, North America and China

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From the Forest to the Customer
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Jämsänkoski Finland, year 2002
Products - PM56 uncoated magazine 570 000
t/a - PM4 coated magazine 125 000 t/a - PM3
label paper 110 000 t/a
Founded 1888 Capacity 815.000
t/a Personnel 940
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Jämsänkoski SC PM6

• 325 000 t/a, 39 56 g/m², 9.30 m width, 25 m/s
  speed

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Automation Hierarchy, open systems
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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

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Paper Web Break Camera Monitoring
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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

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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.

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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!

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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

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Modelling of paper quality

• Paper strength
• Optical properties
• PM control variables dominate
• some correlation from raw material disturbances

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Neural Networks Self Organised Maps (T. Kohonen)
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Clustering of SOM by k-means
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Summary for data-amounts / hour

• DCS data
• 5 Hz rate, 10,000 channels ? 2E8 samples / hour
• multichannel vibration, NIR spectra
• 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