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

• Published in: Fibers 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
• Language: English
• 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
• DOI: 10.3390/fib12010002
• ISSN: 2079-6439

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.