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

• Autores: Matthieu Sainlez, Georges Heyen
• Localización: Journal of computational and applied mathematics, ISSN 0377-0427, Vol. 246, Nº 1, 2013, págs. 329-334
• Idioma: inglés
• DOI: 10.1016/j.cam.2012.06.026
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• Resumen

  • 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.