A spatial-temporal LWPLS for adaptive soft sensor modeling and its application for an industrial hydrocracking process

Abstract Locally weighted partial least squares (LWPLS) is a widely used just-in-time learning (JITL) modeling algorithm for adaptive soft sensor development. In LWPLS, spatial variable distance is used to measure similarity and assign weights for historical samples, which is very effective to handle process time-varying problems of abrupt changes. However, the gradual process changes are not effectively handled in traditional LWPLS. To cope with this problem, a novel similarity is proposed for temporal distance measurement by introducing a temporal variable of sampling instant, in which newest sampled data can get large weights since they represent the more recent process state. Then, both spatial and temporal similarities are considered to construct a spatial-temporal adaptive LWPLS modeling framework in this paper. The effectiveness of the proposed algorithm is validated on an industrial hydrocracking process.

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