Journal
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
Volume 124, Issue -, Pages 43-49Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.chemolab.2013.03.008
Keywords
Soft-sensor; Just-in-time model; Locally weighted partial least squares; Locally weighted regression; Distillation process
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Funding
- Japan Society for the Promotion of Science (JSPS) [21560793]
- Grants-in-Aid for Scientific Research [24560940, 21560793] Funding Source: KAKEN
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Recently, just-in-time (JIT) modeling, such as locally weighted partial least squares (LW-PLS), has attracted much attention because it can cope with changes in process characteristics as well as nonlinearity. Since JIT modeling derives a local model from past samples similar to a query sample, it is crucial to appropriately define the similarity between samples. In this work, a new similarity measure based on the weighted Euclidean distance is proposed in order to cope with nonlinearity and to enhance estimation accuracy of LW-PLS. The proposed method can adaptively determine the similarity according to the strength of the nonlinearity between each input variable and an output variable around a query sample. The usefulness of the proposed method is demonstrated through numerical examples and a case study of a real cracked gasoline fractionator of an ethylene production process. (C) 2013 Elsevier B.V. All rights reserved.
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