SENSITIVITY ANALYSIS BY ARTIFICIAL NEURAL NETWORK (ANN) OF VARIABLES THAT INFLUENCE THE DIAGONAL TWIST IN A PAPERBOARD INDUSTRIAL MACHINE

Abstract

The dimensional stability of the paper may change due to middle exchange moisture, releasing the latent stress acquired into the manufacturing process. One result of this tension release is the diagonal curl. This study aims to conduct a sensitivity analysis of the different input’s variables of an industrial paper machine, along with some laboratory measurements, in order to identify the importance in production of paperboard quality control and relate to the property of the paper called twist. A survey was made of the production history, relating to 2012, to observe the products with the highest quality losses. From this, they were correlated with the critical points of measurement profile in the machine cross direction and consequently with the paper. It was found some changes once the variables correlated with twist, referring to the three analyzes of the profile (tender side, middle and drive side). It was revealed, from the sensitivity analysis, that the most important and sensitive variables, respectively for the tender side, middle and drive side, were total flow from the top layer, vapor pressure in the 6th group of drying cylinders and mass flow side of the bottom layer of the formation of paperboard.