Mid Sweden University

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Pulp Particle Classification Based on Optical Fiber Analysis and Machine Learning Techniques
Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering, Mathematics, and Science Education (2023-). (FSCN)ORCID iD: 0000-0002-1503-8293
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2024 (English)In: Fibers, ISSN 2079-6439, Vol. 12, no 1, article id 2Article in journal (Refereed) Published
Abstract [en]

In the pulp and paper industry, pulp testing is typically a labor-intensive process performed on hand-made laboratory sheets. Online quality control by automated image analysis and machine learning (ML) could provide a consistent, fast and cost-efficient alternative. In this study, four different supervised ML techniques—Lasso regression, support vector machine (SVM), feed-forward neural networks (FFNN), and recurrent neural networks (RNN)—were applied to fiber data obtained from fiber suspension micrographs analyzed by two separate image analysis software. With the built-in software of a commercial fiber analyzer optimized for speed, the maximum accuracy of 81% was achieved using the FFNN algorithm with Yeo–Johnson preprocessing. With an in-house algorithm adapted for ML by an extended set of particle attributes, a maximum accuracy of 96% was achieved with Lasso regression. A parameter capturing the average intensity of the particle in the micrograph, only available from the latter software, has a particularly strong predictive capability. The high accuracy and sensitivity of the ML results indicate that such a strategy could be very useful for quality control of fiber dispersions. 

Place, publisher, year, edition, pages
MDPI AG , 2024. Vol. 12, no 1, article id 2
Keywords [en]
image analysis, machine learning, online quality control, particle classification
National Category
Paper, Pulp and Fiber Technology
Identifiers
URN: urn:nbn:se:miun:diva-50455DOI: 10.3390/fib12010002ISI: 001149343800001Scopus ID: 2-s2.0-85183380771OAI: oai:DiVA.org:miun-50455DiVA, id: diva2:1835429
Available from: 2024-02-06 Created: 2024-02-06 Last updated: 2024-02-09Bibliographically approved

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Lindström, Stefan BLiubytska, KaterynaPersson, JohanBerg, Jan-ErikEngberg, Birgitta A.Nilsson, Fritjof

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Lindström, Stefan BLiubytska, KaterynaPersson, JohanBerg, Jan-ErikEngberg, Birgitta A.Nilsson, Fritjof
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Department of Engineering, Mathematics, and Science Education (2023-)
Paper, Pulp and Fiber Technology

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1314151617181916 of 20
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Citation style
  • apa
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Language
  • de-DE
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  • Other locale
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