A Review of Algorithms and Trends in Kinetic Model Identification for Chemical and Biochemical Systems

G. Maria

Abstract
Simulation of complex (bio)chemical reactions plays an important role in a process kinetics characterisation. However, detailed kinetic modelling is a difficult task because the model has to reflect the process complexity under variate operating conditions, starting from a limited number of observed variables, (non-)conventional data recorded with a limited sampling frequency, and often with a low reproducibility. Extensive investigations can lead to structured models of complexity depending on the utilisation scope. To overcome weak results, the identification problem must be well formulated, data consistent, numerical estimation appropriate and effective, and the estimate quality analysis adequate. While statistical estimation theory has been extensively developed in terms of objective function choice and solution analysis, numerical algorithm application to (bio)chemical kinetic systems presents particularities and difficulties. The present paper aims to review the main steps and trends in solving the kinetic model identification problem. Rules for a suitable problem formulation vs. modelling objectives, advanced numerical algorithms for obtaining a reliable solution, and an estimate of suitable analysis are shortly summarised.

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Keywords
(bio)chemical kinetic model identification, estimation algorithms