Prediction of paperboard properties based on on-line process data and infrared imaging

Abstract

In papermaking, online measurement of product properties is crucial for process control. However, current practices are limited to QCS scanner measurements, offering only partial coverage. The introduction of infrared imaging provides full coverage temperature measurements but leaves other properties unmeasured. This study aims to predict these properties using available data from QCS scanners and infrared imaging and construct a full coverage prediction. This work focuses on the prediction of two properties: moisture and grammage. The analysis revealed distinct relationships between and within properties. For example, the strong correlation between temperature and moisture makes a full coverage prediction of moisture especially promising. Meanwhile, the weak non-linear relationship between temperature and grammage makes the prediction of grammage challenging, but still with feasibility. These findings led to the proposal of two prediction models: a linear regression model with a moving window for moisture prediction and a CNN-BiLSTM model for grammage prediction. Both models share the same essential idea of a scanner-infrared data replacement trick. However, the inability to synchronize scanner and infrared data prevents the evaluation of full-scale predictions. Despite this limitation, the proposed approaches show promise, particularly for moisture prediction. Further investigation is warranted, especially for grammage prediction, once the synchronization challenge is resolved