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| Gradient Boosting Decision Tree Algorithm Based Soft Measurement Model for Paper Quality |
| Received:January 13, 2020 |
| DOI：10.11980/j.issn.0254-508X.2020.05.006 |
| Key Words:data model paper quality soft measurement gradient boosting decision tree algorithm |
| Author Name | Affiliation | Postcode | | JIANG Lun | State Key Lab of Pulp and Paper Engineering, South China University of Technology, Guangzhou, Guangdong Province, 510640 | 510640 | | MAN Yi | State Key Lab of Pulp and Paper Engineering, South China University of Technology, Guangzhou, Guangdong Province, 510640
Shenzhen Xinyichang Technology Co.,Ltd.,Shenzhen,Guangdong Province,518000)
（*E-mail：corresponding author:manyi@scut.edu.cn） | 518000 | | LI Jigeng | State Key Lab of Pulp and Paper Engineering, South China University of Technology, Guangzhou, Guangdong Province, 510640 | 510640 | | HONG Mengna | State Key Lab of Pulp and Paper Engineering, South China University of Technology, Guangzhou, Guangdong Province, 510640 | 510640 | | MENG Ziwei | State Key Lab of Pulp and Paper Engineering, South China University of Technology, Guangzhou, Guangdong Province, 510640 | 510640 | | ZHU Xiaolin | State Key Lab of Pulp and Paper Engineering, South China University of Technology, Guangzhou, Guangdong Province, 510640 | 510640 |
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| 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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