Incorporating Process Knowledge into Latent Variable Models: An Application to Root Cause Analysis in Bioprocesses

Incorporating process knowledge from various sources often presents challenges in process development, optimization, and control. To utilize available knowledge, linking existing process mo-dels is a viable approach. This work introduces a methodology using latent variable models, specifically sequential and orthogonalized partial least squares (SO-PLS), to capture and quantify the contribution of first-principles knowledge in process models. Applied to a continuously stirred tank reactor (CSTR) case study, the methodology demonstrates how available knowledge can be quantified and how structural and parametric errors in first-principles are addressed using measured data. The methodology is discussed in relation to root cause analysis in bioprocesses.