A sensitivity-analysis-based approach for structural process-model mismatch diagnosis in mechanistic models

Structural process-model mismatch represents a major challenge in process systems engineering, as it undermines the reliability of mechanistic models, even after precise parameter estimation. Identifying which modeling assumption or grouped terms most contribute to the observed mismatch remains nontrivial. To address this issue, we propose a novel approach based on Sobol’s global sensitivity analysis for diagnosing structural process-model mismatch. This approach is applied to the mismatch trajectory – which is defined as the difference between model predictions and process data – while predefined input channels (e.g., specific functional groups appearing in model equations) are perturbed within uncertainty ranges. Computed sensitivity indices quantify both individual and interaction effects on mismatch variance, thereby providing interpretable diagnostics of dominant structural sources and revealing potential inflation effects due to interactions. The proposed approach is demonstrated through two in-silico case studies: (i) a fed-batch bioreactor for yeast cultivation, and (ii) an ion-exchange chromatography process for biopharmaceutical manufacturing. Results show that our approach can pinpoint structural assumptions driving the mismatch, providing systematic support for model refinement and streamlining the development of more reliable mechanistic and hybrid models for process simulation.