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Wood prediction using Artificial Intelligence in Modelling (WAIMO)

Reference number
Coordinator Holmen Aktiebolag
Funding from Vinnova SEK 571 700
Project duration September 2019 - July 2020
Status Completed
Venture Strategic innovation programme for process industrial IT and automation – PiiA
Call Digitization of industrial value chains

Purpose and goal

The purpose and aim of the project was to develop a measurement method for the diameter of the input wood and from that, then create a predictive model of how the incoming wood would affect the fiber wall thickness in the process. Within the project we have succeeded in developing a measuring method. and studied the variation of incoming wood. The project was unsuccessful in achieving a predictive model, but created the conditions for this to be possible in the future.

Expected results and effects

The work that has been conducted has made it possible to measure the dimensions of the incoming logs and given a greater understanding of the connection between wood and fiber wall thickness, as well as provided quantitative measures of how the fiber wall thickness varies over time. Both in terms of short and long term. This work has given us good conditions to work on the goal in order to be able to predict the fiber wall thickness and with the help of creating better products and more stable processes.

Planned approach and implementation

Installation of measuring systems took place at Iggesund´s bruk, which is part of the Holmen Group. The installation itself was done by Andritz together with local technicians. Laboratory materials were extracted from several of Holmen´s industries. Analyzes have been performed by RISE Bioekonomi & RISE SICS together with support from Holmen AB and Pulpeye. The combined results show that the concept of predicting fiber wall thickness should be possible and of value for the mills, but more work is needed to get there.

The project description has been provided by the project members themselves and the text has not been looked at by our editors.

Last updated 4 September 2020

Reference number 2019-02515

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