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

• A new similarity measure based on weighted distance was developed for LW-PLS.
• The weight on each input should correspond to the strength of nonlinearity.
• The weight can be the variance of regression coefficients of local linear models.
• An industrial application demonstrates the practicability of the proposed LW-PLS.
• The proposed LW-PLS outperforms conventional methods in the estimation accuracy.

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.