Data-driven soft sensors in pulp refining processes using artificial neural networks

Abstract

Pulp refining processes are most often complicated to describe using linear methodologies, and sometimes an artificial neural network (ANN) is a preferable alternative when assimilating non-linear operating data. In this study, an ANN is used to predict pulp properties, such as shives (wide), fiber length, and freeness. Both traditional process variables (external variables) and refining zone variables (internal variables) are necessary to include as model inputs. The estimation of shives (wide) results achieved an R2 (coefficient of determination) of 0.9 (0.7) for the training and (validation) sets. Corresponding measures for fiber length and freeness can be questioned using this methodology. It is shown that the maximum temperature in the flat zone can be modeled using the external variables motor load and production instead of the specific energy. This resulted in an R2 of approximately 0.9 for the training sets, while the R2 for the validation set did not reach an acceptable level – most likely due to inherent non-linearities in the process. Additional results showed that the consistency profile is difficult to estimate properly using an ANN. Instead, a model-driven sensor is preferred to be used. The main results from this study indicate that shives (wide) should be the prime candidate when introducing advanced pulp property control concepts.