| 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 |
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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）。 |
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| 摘要点击次数: 2830 |
| 全文下载次数: 1945 |
| 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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