Abstract
Good runnability of paper and board machines is often limited by issues such as web flutter, wrinkles, quality defects, web breaks, and problems with tail threading. When runnability problems are addressed by using unnecessarily high draws, it leads to an increase in web breaks, thereby reducing the machine's efficiency and productivity. One of the most crucial areas where numerous issues arise is the point where the paper transitions from the top hot cylinder to the bottom cylinder, known as the opening nip position. To design effective runnability solutions that counteract the negative forces affecting the paper web as it transitions to the drying section, advanced technologies are needed. Mathematical modeling in the paper industry heavily relies on physical models that include many empirical correlations and assumptions. However, even well-developed mathematical models struggle to accurately describe systems with high degrees of nonlinearity. This study attempted to simulate the opening nip underpressure using Advanced Neural Network application. The findings indicated a strong alignment between the simulated and experimental data, implying that Advanced Neural Networks could serve as a valuable tool for both designing and diagnosing the performance of existing runnabilty components in paper machines.
Published Version
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