An optimization-free Fisher information driven approach for online design of experiments

• Published in: Computers & chemical engineering 2024-08, Vol.187, p.108724, Article 108724
• Main Authors: Friso, Andrea, Galvanin, Federico
• Format: Article
• Language: English
• Subjects: Fisher information; Optimal design of experiments
• ISSN: 0098-1354; 1873-4375
• DOI: 10.1016/j.compchemeng.2024.108724

Developing mathematical models used to elucidate reaction kinetics plays a crucial role in the design, control, and optimization of chemical processes. One of the most challenging tasks in kinetic model identification is the precise estimation of unknown kinetic model parameters. This challenge can be effectively addressed through the application of Model-Based Design of Experiments (MBDoE) techniques, which enable the design of experiments facilitating precise model parameter estimation with minimal runs and analytical resources. Nevertheless, MBDoE techniques rely on an optimization procedure that is susceptible to parametric uncertainty, making the design procedure computationally intensive and prone to issues of local optimality. MBDoE techniques are also employed in online procedures to expedite the identification of kinetic models in autonomous platforms. As a result, ensuring rapid convergence becomes imperative to mitigate numerical issues during operational processes. In this paper a Fisher Information Matrix Driven (FIMD) approach is introduced to tackle these challenges. The methodology integrates a sampling-based experimental design approach with experiment ranking based on FIM to select the most informative experiment at each iteration. The effectiveness of the proposed design methodology is examined and discussed via two different case studies of increasing complexity: a fed-batch reactor in which the fermentation of baker’s yeast is carried out and a nucleophilic aromatic substitution in a flow reactor system. •A Fisher Information matrix driven (FIMD) method is proposed for optimization-free experimental design.•Iterative selection of the most informative experiments is leveraged by a ranking of experiments approach.•The method allows to achieve a fast reduction of uncertainty on model parameters.•FIMD method outperforms standard MBDoE techniques in terms of required computational time.•The method is tested on two case studies characterized by models of increasing complexity.