[en] In this study, neural networks approaches are
compared for predicting the high pressure
(HP) steam flow rate from a Kraft recovery
boiler. We apply two types of neural networks:
a static multilayer perceptron and a dynamic
Elman’s recurrent neural network.
Starting from a one-day database of raw
process data related to the boiler, the goal
is to model and predict the next 12-hours of
HP steam flow production from the boiler to
the steam turbine. The results illustrate the
potential of the dynamic approach in this task.
