PREDICTING THE PROPERTIES OF CORRUGATED BASE PAPERS USING MULTIPLE LINEAR REGRESSION AND ARTIFICIAL NEURAL NETWORKS

The difficulty in predicting the properties and behaviour of paper products produced using heterogeneous raw materials with high percentages of recovered fibres poses restrictions on their efficient and effective use as corrugated packaging materials. This work presents predictive models for the mechanical properties of corrugated base papers (liner and fluting-medium) from fibre and physical property data using multiple linear regression and artificial neural networks. The most significant results were obtained for the prediction of the tensile strength of liners in the cross direction from the origin (wood type, pulp method) of the fibres using linear regression, and the prediction of the compressive strength of fluting-medium in the longitudinal (machine) direction, according to the short-span test, using a neural network with one hidden layer with 6 neurons, with coefficients of determination at 95.14% and 99.28%, respectively.