Incorporating prior knowledge about structural constraints in model identification

TL;DR

This paper introduces Structural Principal Component Analysis (SPCA), a novel method that incorporates partial structural knowledge into model identification, improving estimation accuracy in chemical industry applications.

Contribution

The paper presents SPCA, a new PCA-based technique that leverages structural information for better model estimates, reducing computational costs compared to sparsity-based methods.

Findings

SPCA outperforms traditional PCA in synthetic and industrial case studies.

Incorporating structural knowledge improves model estimation accuracy.

SPCA offers a computationally efficient alternative to sparsity constraints.

Abstract

Model identification is a crucial problem in chemical industries. In recent years, there has been increasing interest in learning data-driven models utilizing partial knowledge about the system of interest. Most techniques for model identification do not provide the freedom to incorporate any partial information such as the structure of the model. In this article, we propose model identification techniques that could leverage such partial information to produce better estimates. Specifically, we propose Structural Principal Component Analysis (SPCA) which improvises over existing methods like PCA by utilizing the essential structural information about the model. Most of the existing methods or closely related methods use sparsity constraints which could be computationally expensive. Our proposed method is a wise modification of PCA to utilize structural information. The efficacy of the proposed approach is demonstrated using synthetic and industrial case-studies.