Data-driven Method for Pulp Properties Estimation in Stock Preparation

Abstract

Pulp properties are key factors to assessing the performance of a refining process, evaluating operational conditions, and optimizing the process of stock preparation. This paper presents a data-driven approach to estimate the beating degree and wet weight of pulp after refining using case-based reasoning (CBR). Historical data generated in a refining process at a paper mill was used to evaluate the proposed model. The root mean square error (RMSE) and coefficient of variance of the root mean square error (CV-RMSE) of the beating degree estimation results in CBR were 1.30 and 4.32%, respectively, and the RMSE and CV-RMSE of the wet weight were 0.50 and 19.09%, respectively. The results of beating degree prediction were satisfactory, and the results of wet weight were also acceptable. To test the performance of CBR model, support vector machine algorithm (SVM) were employed to verify the effectiveness and accuracy. The RMSE and CV-RMSE of the beating degree estimation results in SVM were 1.20 and 4.02%, respectively, and the RMSE and CV-RMSE of the wet weight were 0.44 and 16.73%, respectively. As a result, the proposed model was as accurate as the SVM method.

Keywords

• Stock preparation
• Pulp quality
• Data-driven model
• Case-based reasoning