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Journals and Conferences
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.… (More)
We propose a “time-biased” and a “space-biased” method for spatiotemporal independent component analysis (ICA). The methods rely on computing an orthogonal approximate joint diagonalizer of a collection of covariance-like matrices. In the time-biased version, the time signatures of the ICA modes are imposed to be white, whereas the space-biased version… (More)
A data mining methodology, the random forests, is applied to analyze pollutant emission from the recovery boiler of a Kraft pulping process. Starting from a large database of raw process data, the goal is to identify the input variables that explain the most output variations.