Process for determining the final point of pulp cooking and...

D - Textiles – Paper – 21 – C

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D21C 7/12 (2006.01) D21C 3/22 (2006.01) G05B 13/02 (2006.01)

Patent

CA 2234221

The aim of the invention is to determine the point in time during pulp cooking at which the viscosity and/or kappa number of the pulp produced have predetermined values. Since these values can be determined only after the cooking process, appropriate models must be used. To that end, use is made of a neural network whose initial value is the desired viscosity and/or kappa number from which the requisite cooking time can be determined and whose initial values are measurement values of the cooking process. According to the invention, the neural network is adapted during the course of a continuous cooking reaction on the basis of a dynamic model of the changing measurement values. Suitable measurement values during pulp cooking are in particular the SO2 content of the cooking acid and the colour value and electrical conductivity or pH value. In the associated arrangement, the model (10) used is a combination of analytical models (101 to 103, 201 to 204) of the process variables with the neuronal network (100, 200).

Lors de la cuisson de la cellulose, il convient de déterminer le moment auquel la viscosité et/ou l'indice kappa de la cellulose produite atteignent les valeurs prédéterminées. Ces valeurs ne pouvant être déterminées qu'au terme de l'opération de cuisson, il convient de prendre en considération certains modèles adéquats. Pour ce faire, on utilise un réseau neuronal ayant, d'une part, comme grandeurs initiales la viscosité et/ou l'indice Kappa à partir desquels on calcule le temps de cuisson nécessaire et, d'autre part, comme grandeurs d'entrée les grandeurs de cuisson mesurées. Selon l'invention, le réseau neuronal est adapté en cours de cuisson. A cet effet, un modèle dynamique des variables à mesurer est pris en considération. Lors de la cuisson de la cellulose, les grandeurs mesurées adéquates sont, en particulier, la teneur en SO¿2? de la lessive de cuisson, sa valeur chromatique, sa conductivité électrique ou son pH. Dans le dispositif associé, on utilise un modèle (10) qui est une combinaison de modèles analytiques (101 à 103, 201 à 204) de variables de processus et du réseau neuronal (100, 200).

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