Abstract
Abstract The goal of this paper is to model the pulp properties fiber length, shives width and freeness. This will be done utilizing specific energy, flat zone inlet consistency and the internal variables, consistencies and fiber residence times estimated from refining zone soft sensors. The models are designed using more than 3600 hours of data from a RGP82CD refiner. The pulp properties are sampled using a measurement device positioned after the latency chest. Such measurements are noisy and irregularly sampled which opens for a number of challenges to overcome in modeling procedures. In this paper it is shown that the models for shives width and fiber length are capable of predicting most of the major dynamics. However, for freeness no reliable linear models can be derived. When estimating fiber length, the specific energy together with flat zone inlet consistency, fiber residence times and the consistency in the conical zone were the dominant inputs. For shives width it was found that a similar set of inputs resulted in the best models, except that the consistencies during normal process conditions did not significantly influence shives width. Furthermore, fiber residence times were shown to have considerably more pronounced impact on fiber length compared with shives width estimates.
Highlights
It is well-known that pulp properties are often difficult to measure reliably in TMP- and CTMP-processes
Specific energy and flat zone (FZ) inlet consistency both have a Variance Inflation Factors (VIF) below 4 which indicates that the interdependencies are most likely not problematic
ARX Static gain they are quite similar with only minor differences in behaviour. From these results we propose a model of fiber length, utilizing specific energy, fiber residence times, consistency in the conical zone and FZ inlet consistency
Summary
It is well-known that pulp properties are often difficult to measure reliably in TMP- and CTMP-processes. As the measurement quality of these devices is questioned from a control engineering perspective, considerable benefits could be achieved if models are implemented to predict pulp properties based on process conditions sampled before the latency chest. The soft sensor’s outputs can be seen as estimates of internal variables (such as fiber residence time, consistency profile, forces on bars, distributed defibration, thermodynamic work etc.) which are difficult to measure directly in the process. Such soft sensors are non-linear but still have become important for advanced process optimization (Karlström and Hill 2017a, 2017b, 2017c, Bengtsson et al 2020)
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