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Preventor - digitalised process state description

Reference number
Coordinator ACOSENSE AB
Funding from Vinnova SEK 1 816 625
Project duration June 2016 - September 2018
Status Completed
Venture Strategic innovation programme for process industrial IT and automation – PiiA

Important results from the project

The project was intended to provide the process industry (with focus on pulp and paper) with an analysis instrument to optimize their processes, by either 1) alarm when undesired process situations occur (e.g. flakes in return fiber) or 2) to detect different, correct process stages (when changing paper quality) in order to know when the “right” quality was produced. The goal of the project was to provide two working instruments to Skoghall that can detect flakes in the return pulp in order to control a disperger and to reduce spillage when switching from one paper quality to the next.

Expected long term effects

Acosense has developed an instrument with algorithms that detect and alarm situations of undesired content in the pipe and can detect and indicate different paper qualities. Especially the detection of paper quality reached a stage that it can be used in process optimisation. Expected effects are that StoraEnso Skoghall can reduce their waste, Fraunhofer Chalmers Center (FCC) can further develop its knowledge within machine learning and that Acosense has a method and an instrument that can repeatably describe different process stages.

Approach and implementation

In order to fulfill the project goals, Acosense did an upgrade on its current product Acospector and installed two instruments at StoraEnso Skoghall. During a one year period the instrument collected process data which FCC used to identify the optimum algorithm for describing the process. This by using data from specific test runs and continuous production information from StoraEnso Skoghall. The derived algorithms were verified with more dedicated test runs, comparing the project algorithm results with the process data of the mill.

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

Last updated 8 January 2019

Reference number 2016-02399

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