Diagnosing process/model mismatch in first-principles models by latent variable modeling

A methodology is proposed to diagnose the causes for the process/model mismatch (PMM) that may arise when a process is simulated using a first-principles (FP) model. To this purpose, a latent variable model is used to assess the consistency between the correlation structure of a historical operation dataset and that of a similar dataset generated using the FP model. Inconsistencies between the two correlation structures are analyzed by means of diagnostic indices. Engineering judgment is then used to pinpoint which equations or parameters of the FP model are mostly responsible for the observed PMM. The proposed methodology is tested on two simulated case studies, and it is shown to provide clear indications on where the mismatch originates from.