Comparison of Machine Learning techniques for atmospheric pollutant monitoring in a Kraft pulp mill

In this paper, machine learning techniques are compared to predict nitrogen oxide (NOx) pollutant emission from the recovery boiler of a Kraft pulp mill. Starting from a large database of
raw process data related to a Kraft recovery boiler, we consider a regression problem in which we are trying to predict the value of a continuous variable. Generalization is done on the worst case configuration possible to make sure the model is adequate: the training period concerns stationary operations while test periods mainly focus on NOx emissions during transient operations.
This comparison involves neural network techniques (i.e., static multilayer perceptron and dynamic NARX network), tree-based methods and multiple linear regression. We illustrate the potential of a dynamic neural approach compared to the others in this prediction task.