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.