Petsagkourakis, P; Galvanin, F; (2020) Backoff-Based Model-Based Design of Experiments Under Model Mismatch. In: Pierucci, S and Manenti, F and Bozzano, GL and Manca, D, (eds.) Computer Aided Chemical Engineering. (pp. pp. 1777-1782). Elsevier: Milan, Italy.

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Abstract

Kinetic modelling has become an indispensable tool in industry for a quantitative understanding of reaction systems. In the presence of parametric and structural mismatch, the constraints that maintain safe experiments, e.g. solubility of a chemical may well turn out to be violated when that optimally designed experiment is performed, leading in the best case to less informative data sets or worse to an unsafe experiment. In this work, a Gaussian process is utilised to quantify the uncertainty realisation of the physical system to calculate the explicit back-offs through Monte Carlo stochastic simulations. The method provides a theoretical guarantee for the robust satisfaction of the constraints. The proposed method can be used for the design of optimal experiments starting from limited preliminary knowledge of the parameter set, leading to a safe exploration of the parameter space. The performance of this method is demonstrated through an illustrative case study regarding the parameter identification of the transient behaviour a nucleophilic aromatic substitution (SNAr).

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