Pulp Particle Classification Based on Optical Fiber Analysis and Machine Learning Techniques

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 su...

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Bibliographic Details
Published inFibers Vol. 12; no. 1; p. 2
Main Authors Lindström, Stefan B., Amjad, Rabab, Gåhlin, Elin, Andersson, Linn, Kaarto, Marcus, Liubytska, Kateryna, Persson, Johan, Berg, Jan-Erik, Engberg, Birgitta A., Nilsson, Fritjof
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.01.2024
Subjects
Algorithms
Artificial intelligence
Automatic control
Automation
Cellulose fibers
Classification
Deep learning
Dispersions
Energy consumption
Fabric analysis
Fiber optics
Image analysis
Image quality
Light
Machine learning
Microscopy
Neural networks
online quality control
Optical fibers
Optimization techniques
particle classification
Photomicrographs
Pulp & paper industry
Quality control
Recurrent neural networks
Software
Support vector machines
Sweden
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Summary: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.
ISSN:2079-6439
2079-6439
DOI:10.3390/fib12010002