Predicting handsheet properties and enhancing refiner control using fiber analyzer data and latent variable modeling

This study focuses on the development of a compact model with improved interpretability compared to similar approaches, relating thermomechanical pulp (TMP) properties, quantified using a fiber analyzer, to Canadian standard freeness and handsheet properties. The data used in this study are obtained from TMP produced by a conical disc refiner. Utilizing the LASSO-regularized Latent Variable Regression (LASSO-LVR) model, we identified three key latent variables – representing shives content, fibrillation, and slender fines content – that accurately predict eight distinct handsheet properties. In a subsequent analysis, we investigated the linkage between refiner settings and Specific Refining Energy (SRE) to these key analyzer readings and, consequently, to handsheet properties. The inclusion of SRE as an internal state variable in the model significantly enhanced predictive accuracy, providing a foundation for more precise and energy-efficient control strategies in refining processes. • A compact model predicts TMP handsheet properties using LASSO-regularized regression. • Three key latent variables predict eight handsheet properties from pulp data. • CD refiner controls insufficient to predict TMP handsheet properties directly. • Specific Refining Energy improves control strategies and predictive accuracy.