Stefan B. Lindström, Rabab Amjad, Elin Gåhlin, Linn Andersson, Marcus Kaarto, Kateryna Liubytska, Johan Persson, Jan-Erik Berg, B. Engberg, Fritjof Nilsson
{"title":"基于光纤分析和机器学习技术的纸浆颗粒分类","authors":"Stefan B. Lindström, Rabab Amjad, Elin Gåhlin, Linn Andersson, Marcus Kaarto, Kateryna Liubytska, Johan Persson, Jan-Erik Berg, B. Engberg, Fritjof Nilsson","doi":"10.3390/fib12010002","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":12122,"journal":{"name":"Fibers","volume":"16 4","pages":""},"PeriodicalIF":4.0000,"publicationDate":"2023-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Pulp Particle Classification Based on Optical Fiber Analysis and Machine Learning Techniques\",\"authors\":\"Stefan B. Lindström, Rabab Amjad, Elin Gåhlin, Linn Andersson, Marcus Kaarto, Kateryna Liubytska, Johan Persson, Jan-Erik Berg, B. Engberg, Fritjof Nilsson\",\"doi\":\"10.3390/fib12010002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":12122,\"journal\":{\"name\":\"Fibers\",\"volume\":\"16 4\",\"pages\":\"\"},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2023-12-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Fibers\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3390/fib12010002\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fibers","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/fib12010002","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
摘要
在纸浆和造纸行业,纸浆测试通常是在手工制作的实验室纸张上进行的劳动密集型过程。通过自动图像分析和机器学习(ML)进行在线质量控制,可以提供一种稳定、快速且经济高效的替代方法。在这项研究中,四种不同的监督式 ML 技术--拉索回归、支持向量机 (SVM)、前馈神经网络 (FFNN) 和递归神经网络 (RNN)--被应用于通过两个独立的图像分析软件分析纤维悬浮显微照片获得的纤维数据。通过对商用纤维分析仪的内置软件进行速度优化,使用 FFNN 算法和 Yeo-Johnson 预处理,达到了 81% 的最高准确率。利用内部算法,通过一组扩展的颗粒属性对 ML 进行调整,利用 Lasso 回归法实现了 96% 的最高准确率。只有后者的软件中才有捕捉显微照片中颗粒平均强度的参数,该参数具有特别强的预测能力。ML 结果的高精确度和灵敏度表明,这种策略对纤维分散体的质量控制非常有用。
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 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.
FibersEngineering-Civil and Structural Engineering
CiteScore
7.00
自引率
7.70%
发文量
92
审稿时长
11 weeks
期刊介绍:
Fibers (ISSN 2079-6439) is a peer-reviewed scientific journal that publishes original articles, critical reviews, research notes and short communications on the materials science and all other empirical and theoretical studies of fibers, providing a forum for integrating fiber research across many disciplines. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. Electronic files or software regarding the full details of the calculation and experimental procedure, if unable to be published in a normal way, can be deposited as supplementary material. The following topics are relevant and within the scope of this journal: -textile fibers -natural fibers and biological microfibrils -metallic fibers -optic fibers -carbon fibers -silicon carbide fibers -fiberglass -mineral fibers -cellulose fibers -polymer fibers -microfibers, nanofibers and nanotubes -new processing methods for fibers -chemistry of fiber materials -physical properties of fibers -exposure to and toxicology of fibers -biokinetics of fibers -the diversity of fiber origins