| 江伦,满奕,李继庚,洪蒙纳,孟子薇,朱小林.基于梯度增强决策树算法的纸张质量软测量模型[J].中国造纸,2020,39(5):37-42 |
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| 基于梯度增强决策树算法的纸张质量软测量模型 |
| Gradient Boosting Decision Tree Algorithm Based Soft Measurement Model for Paper Quality |
| 收稿日期：2020-01-13 |
| DOI：10.11980/j.issn.0254-508X.2020.05.006 |
| 关键词: 数据模型 纸张质量 软测量 梯度增强决策树（GBDT）算法 |
| Key Words:data model paper quality soft measurement gradient boosting decision tree algorithm |
| 基金项目: |
| 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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| 摘要:本研究提出了一种基于梯度增强决策树（GBDT）算法的纸张质量软测量模型，该方法可在线软测量纸张的关键物理指标如抗张强度、柔软度和松厚度。结果表明，采用GBDT进行纸张质量软测量时，抗张强度、柔软度和松厚度的平均相对误差分别为7.21%、7.38%和3.5%；采集新数据验证后，纸张抗张强度、柔软度和松厚度的平均相对误差分别为6.87%、6.88%和3.12%，表明模型对新验证数据的预测结果精度高。 |
| Abstract:In this study, a soft-sensing model of paper quality based on gradient boosting decision tree (GBDT) was proposed. This method could soft-measure the key physical indicators of paper such as tensile strength, softness and bulk online. The results showed that the average relative errors of tensile strength, softness and bulk when using GBDT for soft measurement of paper quality were 7.21%,7.38%, and 3.5%, respectively. Comparing the new data collected for verification, the average relative errors of tensile strength, softness, and bulk were 6.87%, 6.88%, and 3.12%, respectively, indicating that the model had high accuracy in predicting the new verification data, which could provide a reference for stabilizing product quality, optimizing the production process and reducing production costs. |
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