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Identifying Process Dynamics through a Two-Level Factorial Experiment

Summary: [This abstract is based on the authors' abstract.] Dynamic processes undergo a transition time when changing experimental factors and therefore an experimenter is often interested in estimating effect dynamics alongside effect sizes. This article illustrates an eight-step analysis procedure for model identification of a multiple-input transfer function–noise model for the response from a two-level factorial experiment in a blast furnace process. Because real data often are affected by disturbances and missing observations, the proposed procedure deals with these problems and results in a transfer function–noise model that captures system dynamics and provides effect estimates from the experiment.

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  • Topics: Data Quality
  • Keywords: Missing data, Time series, Transfer function models, Noise, Factorial designs, Process dynamics
  • Author: Lundkvist, Peder; Vanhatalo, Erik;
  • Journal: Quality Engineering