江伦,满奕,李继庚,洪蒙纳,孟子薇,朱小林.基于支持向量机算法的造纸过程磨后纤维形态软测量模型[J].中国造纸学报,2020,35(2):52-58 本文二维码信息
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基于支持向量机算法的造纸过程磨后纤维形态软测量模型
Support Vector Machine Algorithm Based Soft Measurement Model for Post-refiningFiber Morphology of Papermaking Process
投稿时间:2019-12-11  
DOI:10.11981/j.issn.1000-6842.2020.02.52
中文关键词:  磨浆  纤维形态  软测量  SVM算法
Key Words:pulping process  fiber morphology  soft sensing technology  SVM algorithm
基金项目:制浆造纸工程国家重点实验室开放基金(201830)。
作者单位邮编
江伦 华南理工大学制浆造纸工程国家重点实验室广东广州510640 510640
满奕 华南理工大学制浆造纸工程国家重点实验室广东广州510640
深圳新益昌科技股份有限公司, 广东深圳518000 
518000
李继庚 华南理工大学制浆造纸工程国家重点实验室广东广州510640 510640
洪蒙纳 华南理工大学制浆造纸工程国家重点实验室广东广州510640 510640
孟子薇 华南理工大学制浆造纸工程国家重点实验室广东广州510640 510640
朱小林 华南理工大学制浆造纸工程国家重点实验室广东广州510640 510640
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中文摘要:
      本研究提出了一种基于支持向量机(SVM)算法的造纸过程磨后浆料纤维形态软测量模型,该模型利用原始浆板纤维形态参数和磨浆参数作为输入,用于在线软测量生产过程中的磨后浆料纤维形态。结果表明,采用SVM算法进行建模时,7种磨后浆料纤维形态软测量模型的平均相对误差在2.87%~5.61%之间,均优于采用偏最小二乘回归(PLS)算法的建模效果(平均相对误差3.09%~6.60%),模型精度良好,满足生产中纤维形态实时检测对误差的要求。
Abstract:
      In this study, a soft measurement model of post-refining fiber morphology in a papermaking process based on support vector machine algorithm (SVM) was proposed. The model used the parameters of original pulp sheet and refining as input for online soft measurement of post-refining fiber morphology. The results showed that when SVM was used for modeling, the average relative error of the seven kinds of soft measurement models of post-refining fiber morphology was between 2.87% and 5.61%, which was better than the modeling based on PLS algorithm (the average relative error was between 3.09% and 6.60%) , and the model precision was good, which met the error requirements of real-time fiber morphology measurement in production.
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