Abstract The objective of this investigation is to analyze and model tensile index. Two approaches are used, one based on training and validation data, while the other novel approach tests models using all possible combinations of data points. This approach is focused on small data sets which have here been obtained from nineteen pulp samples at different refining conditions in a full-scale TMP production line with a CD-76 refiner as a primary stage. From each pulp sample twenty handsheet strips for tensile index measurements were performed. Initially, specific energy and the external variables (dilution water feed rates and plate gaps) are used as predictors in a modeling approach based on an adjusted R 2 {R^{2}} approach. Thereafter, the resulting models are compared with a combination of specific energy and internal variables (primarily consistencies) obtained from temperature measurements inside the refining zones using a soft sensor concept. It is found that specific energy and internal variables as predictors outperform the external variables when estimating tensile index.

中文翻译：

---

使用不确定数据集对拉伸指数进行建模

摘要 本研究的目的是对拉伸指数进行分析和建模。使用了两种方法，一种基于训练和验证数据，而另一种新颖的方法使用所有可能的数据点组合来测试模型。这种方法侧重于小数据集，这些数据集是从以 CD-76 磨浆机为初级阶段的全规模 TMP 生产线中不同磨浆条件下的 19 个纸浆样品中获得的。从每个纸浆样品中进行 20 条手抄纸条进行拉伸指数测量。最初，在基于调整后的 R 2 {R^{2}} 方法的建模方法中，比能和外部变量（稀释水进料速率和板间隙）用作预测变量。此后，由此产生的模型与使用软传感器概念从精炼区内的温度测量中获得的特定能量和内部变量（主要是一致性）的组合进行比较。结果发现，在估计拉伸指数时，作为预测变量的特定能量和内部变量优于外部变量。