基于梯度增强决策树算法的纸张质量软测量模型

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。

Gradient Boosting Decision Tree Algorithm Based Soft Measurement Model for Paper Quality

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.