(372k) Development of a Black-Box Parameter Estimation Methodology of a Batch Anti-Solvent Protein Crystallisation Process with Sparse Measurements
AIChE Annual Meeting
2024
2024 AIChE Annual Meeting
Computing and Systems Technology Division
10B: Interactive Session: Systems and Process Control
Tuesday, October 29, 2024 - 3:30pm to 5:00pm
Two models were developed with Classical Nucleation Theory (CNT) kinetics, and empirical and birth-and-spread (BpS) growth kinetics respectively to assess which scheme models lysozyme crystallisation more accurately (referred to as CNT-Emp and CNT-BpS respectively). Global Sensitivity Analysis (GSA) was performed to calculate Sobolâ sensitivity indices of the developed models across batch operation and investigate which parameters dominate output variation. The critical parameters were estimated using data generated from nine in-vitro lysozyme crystallisation experiments. For the parameter estimation, a gradient-free, algorithm was developed, based on a maximum likelihood estimation (MLE) loss function with nonconstant measurement variance, using a differential evolution algorithm. The parameterised model was validated using experiments at intermediate and out-of-bounds initial concentrations. The CNT-Emp model developed had lower likelihood estimation compared to CNT-BpS, however none of the tested combinations can predict error-free crystallisation behaviour. Approximate Bayesian Computation was used to investigate parameter uncertainty and approximate the parameterâs posterior distributions, avoiding model linearisation or the need of an analytical function of the likelihood.
The presented methodology allows for a-priori calculation of time-indexed sensitivity indices supports decision-making regarding in-vitro measurement sampling, while the calculated likelihood and approximated parameter posterior distributions are used to assess and ultimately select the parametrised models.
Acknowledgments
This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) for the Imperial College London Doctoral Training Partnership (DTP) and by AstraZeneca UK Ltd through a CASE studentship award.
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