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| Construction and Comparison of Cardboard Quality Prediction Models Based on Machine Learning |
| Received:August 11, 2022 |
| DOI：10.11980/j.issn.0254-508X.2023.07.009 |
| Key Words:machine learning data mining paper industry quality prediction soft measurement |
| Fund Project:国家重点研发计划（2020YFE0201400）；人工智能与数字经济广东省实验室（广州）青年学者项目（PLZ2021KF0019）。 |
| Author Name | Affiliation | Postcode | | QIAN Jiwei | State Key Lab of Pulp and Paper Engineering, South China University of Technology, Guangzhou, Guangdong Province, 510640 | 510640 | | LI Jigeng | 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
Guangdong Artificial Intelligence and Digital Economy Lab （Guangzhou）, Guangzhou, Guangdong Province, 510335 | 510335 | | HONG Mengna | State Key Lab of Pulp and Paper Engineering, South China University of Technology, Guangzhou, Guangdong Province, 510640
China-Singapore International Joint Research Institute, Guangzhou, Guangdong Province, 510555 | 510555 | | HE Zhenglei* | State Key Lab of Pulp and Paper Engineering, South China University of Technology, Guangzhou, Guangdong Province, 510640 | 510640 |
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| Abstract:The production of cardboard in volves a series of complex processes and the lack of online monitoring methods for key qualities， which makes it difficult to control the quality of cardboard. This paper attempted to establish predictive models， also known as soft measurement models， which based on machine learning methods that could monitor cardboard quality on line to facilitate effective solutions to the above problems. This study used actual data from cardboard companies to train and compared the predictive performance of random forest （RF）， gradient boosted regression （GBR）， K-nearest neighbor regression （KNN）， and partial least squares regression （PLS） on a variety of quality indicators. The results showed that the different quality indicators themselves largely effected the upper limit of prediction accuracy， while the degree of approximation to the theoretical upper limit varied significantly among algorithms. Complex， nonlinear integrated models （RF， GBR） had better performance， compared to simple models （KNN，PLS）. |
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