Article ID Journal Published Year Pages File Type
4639387 Journal of Computational and Applied Mathematics 2013 6 Pages PDF
Abstract

In this paper, supervised 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., 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.

Related Topics
Physical Sciences and Engineering Mathematics Applied Mathematics
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