Application of Neural Networks in Pulp Production
This paper presents an application of neural networks in control of the pulp production process. The pulp is produced in a chemical reaction where wood is dissolved by the so called “cooking solution.” One of the main quality indicators of the produced pulp is the permanganate number. The latter strongly depends on the reaction time, temperature, and pressure profiles and is not available for measurement during the production process. The pressure and temperature evolution during the process is usually regulated by process automation system. Consequently, the process time is left as the only remaining variable that has major impact on the process outcome. If the reaction is stopped too soon or too late, the conesponding pulp quality is different from the desired one. Therefore, in order to achieve the desired permanganate number it is essential to make accurate prediction of the reaction time. A neural network model of the process which is used to predict the necessary time of reaction is described herein. This neural model was tested at Cellulose do Caima in Portugal where it proved significantly more accurate (30%) than the previously used analytical model. Furthermore, it was implemented in the commercially available automation system “Teleperm M” produced by Siemens AG.
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