Juha KortelainenUPM R&D, Paper and Pulp

Finland

Avogadro Scale Engineering November 18-19, 2003The Bartos Theater, MIT

Contents

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

UPM Key Figures, 2002

● One of the world's largest paper producers

Turnover, EUR million 10,475

Personnel at 31 December 35,579

Paper production, t/a 10E6

● 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

From the Forest to the Customer

Jämsänkoski – Finland, year 2002Products: - PM5&6: uncoated magazine 570 000 t/a- PM4: coated magazine 125 000 t/a- PM3: label paper 110 000 t/a

Founded: 1888Capacity: 815.000 t/aPersonnel: 940

Jämsänkoski SC PM6● 325 000 t/a, 39 … 56 g/m², 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 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