Friso, Andrea; Galvanin, Federico; (2023) An optimization-free Fisher information driven approach for online design of experiment. In: Kokossis, Antonios and Georgiadis, Michael and Pistikopoulos, Stratos, (eds.) Computer Aided Chemical Engineering. (pp. pp. 13-18). Elsevier: Athens, Greece.

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Abstract

Developing mathematical models used to describe reaction kinetics is pivotal for the design, control and optimization of chemical processes. One of the most challenging tasks in the model development procedure is the identification of the unknown parameters within the model. This problem can be addressed using Model-Based Design of Experiment (MBDoE) techniques that allow experiments to be designed in such a way that parameters can be precisely estimated with the minimum number of runs and analytical resources. However, MBDoE techniques rely on an optimization procedure that is affected by the uncertainty related to the identified parameters and can be computationally expensive and prone to local optimality issues. MBDoE techniques are also applied to online procedures for faster identification of the kinetic model in autonomous platforms, and for this reason it is necessary to ensure a fast convergence and avoid numerical convergence issues during the operation. In this paper, a new optimization-free technology is proposed to tackle the above-mentioned problems.

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