Recurrent neural network prediction of steam production in a Kraft recovery boiler

In this paper, 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.