Comparison of supervised learning techniques for atmospheric pollutant monitoring in a Kraft pulp mill

In this paper, supervised learning techniques are compared to predict nitro-
gen 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 fo-
cus on NOx emissions during transient operations. This comparison involves
neural network techniques (i.e., multilayer perceptron and NARX network),
tree-based methods and multiple linear regression. We illustrate the potential
of a dynamic neural approach compared to the others in this task.