1. Juha Kortelainen UPM R&D, Paper and Pulp Finland Avogadro Scale Engineering November 18-19, 2003 The Bartos Theater, MIT

2. Contents ●UPM overview ●Jämsänkoski Paper Mill ●Paper quality and data analysis

3. 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

4. From the Forest to the Customer

5. Jämsänkoski – Finland, year 2002 Products: - PM5&6: uncoated magazine570 000 t/a - PM4: coated magazine125 000 t/a - PM3: label paper110 000 t/a Founded: 1888 Capacity: 815.000 t/a Personnel: 940

6. Jämsänkoski SC PM6 ●325 000 t/a, 39 … 56 g/m ², 9.30 m width, 25 m/s speed

7. Automation Hierarchy, open systems

9. 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

10. Paper Web Break Camera Monitoring

11. 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

12. 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. :

13. 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!

14. 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

15. Modelling of paper quality ●Paper strength ●Optical properties ●PM control variables dominate ●some correlation from raw material disturbances

16. Neural Networks: Self Organised Maps (T. Kohonen)

17. Clustering of SOM by k-means

18. 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