Supplementary information files for "A comprehensive framework for model evaluation and refinement using MBDoE estimability and structural identifiability: application to a crystallization process"

Supplementary files for article "A comprehensive framework for model evaluation and refinement using MBDoE estimability and structural identifiability: application to a crystallization process"

This paper presents a novel framework for the systematic evaluation, discrimination, and calibration of mathematical models. A diverse set of model candidates is first constructed to capture a broad range of underlying physical and kinetic phenomena. Structural identifiability analysis is then employed to determine whether unique parameter estimates can be derived from ideal, noise-free data, enabling early-stage model screening. A single model is then selected based on arigorous model discrimination criterion. To calibrate and refine the selected model, parameter estimability analysis is integrated with Model-Based Design of Experiments (MBDoE), ensuring optimal sampling strategies, data-rich experiments, and reduced prediction uncertainty. To maximize the effectiveness of MBDoE, a novel temperature cycling protocol is introduced, enabling a single, well-designed, information-rich experiment that minimizes experimental effort. This methodology is demonstrated through a cooling crystallization case study using paracetamol. The results show that the proposed framework significantly enhances model discrimination, parameter precision, and estimability resulting in amodel with superior predictive performance, as proven by the additional post-MBDoE validation experiment operated under different conditions and designed independently from the MBDoE framework. The proposed framework bridges the gaps between the different modeling pillars, reduces experimental efforts, and lays the foundation for more effective modeling and optimal design of experiments for the crystallization systems and beyond.

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