Comparative study on wavelet functional partial least squares soft sensor for complex batch processes

Jingxiang Liu

doi:10.1016/j.ces.2022.117601

Comparative study on wavelet functional partial least squares soft sensor for complex batch processes

2022-06-01

9

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AI

DOI：10.1016/j.ces.2022.117601

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Abstract

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Conventional data-driven models for batch processes conduct unfolding operations and neglect the continuous property. A novel soft sensor method is proposed based on the wavelet functional partial least squares (WFPLS). The three-dimensional (3D) batch data is transferred into an even two-dimensional function matrix no matter whether it has even-length or uneven-length. The orthogonal wavelet basis functions are selected by the proposed active algorithm to approximate the variables' trajectories. Two versions of WFPLS are developed to predict quality based on different assumptions of the loading function. By comparing one of the proposed methods and the classical multi-way partial least squares (MPLS), the root means square errors are relatively descended by 20.6566% and 63.9925%, R2 are improved by 2.5813% and 83.2518%, and the corresponding computation time of the proposed method is only 5.7% and 41% of the time used by MPLS in the numerical case and in the industrial case, respectively. (c) 2022 Elsevier Ltd. All rights reserved.

Keywords:

Wavelet functional partial least squares

Nonlinear soft sensor

Batch process

Uneven-length problem

Multi-scale active approximation algorithm

Comparative study on wavelet functional partial least squares soft sensor for complex batch processes

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