Multiple linear regression modelling of pulp and handsheet properties based on fiber morphology measurements and process data

Abstract

A multiple regression model was evaluated to predict pulp and handsheet properties including z-directional tensile strength (z-strength) and Scott bond values. One hypothesis that was central for the model evaluation was that the crill content, as measured with ultraviolet and infrared lights, would improve the statistical models. A chemi-thermomechanical pulp (CTMP) mill designed with two parallel primary refining lines and a reject refiner was the basis for this study, and all process data and pulp samples were gathered from the specific process. Pulp was extracted from the process for an extended period from a position after the latency chest (primary refined pulp) and from the pulp-stream exiting the mill to the board machine (accept pulp). The crill content was positively correlated to the z-strength of the accept pulp, explaining 55% of the variance with a linear regression model with the crill content as the sole predictor. The estimation model of the z-strength of accept pulp was based on a combination of the crill content, freeness, fibril perimeter for longer fibers, and mean kink angle, and resulted in an R2 of 0.79. When applying cross-validation to determine the predictive model performance, the highest R2 obtained was 0.67. This latter model included the crill content, fibril perimeter, and mean kink angle as predictors.