Incorporating prior knowledge about structural constraints in model identification
This work addresses model identification for chemical industries by enabling the incorporation of structural constraints, though it appears incremental as a modification of PCA.
The authors tackled the problem of model identification in chemical industries by proposing Structural Principal Component Analysis (SPCA), which incorporates prior structural knowledge to improve estimates, demonstrating efficacy through synthetic and industrial case-studies.
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.