Development of soft-sensor using locally weighted PLS with adaptive similarity measure

作者：Kim Sanghong; Okajima Ryota; Kano Manabu^*; Hasebe Shinji

来源：Chemometrics and Intelligent Laboratory Systems, 2013, 124: 43-49.

DOI：10.1016/j.chemolab.2013.03.008

• Soft-sensor
• Just-in-time model
• Locally weighted partial least squares
• Locally weighted regression
• Distillation process

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.

• 出版日期2013-5-15