基于机器学习的箱纸板质量预测模型构建及比较

作者：Qian Jiwei; Li Jigeng; Man Yi; Hong Mengna; He Zhenglei^*

来源：China Pulp and Paper, 2023, 42(7): 72-129.

DOI：10.11980/j.issn.0254-508X.2023.07.009

• data mining
• machine learning
• paper industry
• quality prediction
• soft measurement

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 measure? ment 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 accura? cy，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）. ? 2023 China Technical Association of Paper Industry.

• 单位
  华南理工大学; 制浆造纸工程国家重点实验室