Bioprocess model identification

Abstract

This text introduces different tasks that are to be performed in a full-fledged model identification exercise. Special emphasis is given to inherent to the typical problems inherent to the modeling of biotechnological processes: 1) insufficient a priori knowledge to deduce model structure so that recurrence is to be made to experimental data; 2) models that are non-linear both in the state variables and the parameters needing complex and computing intensive numerical algorithms to identify and apply the model(parameter .... estimation, simulation); moreover, the study of for instance the theoretical identifiability of the parameters is hampered due to a lack of theoretical development for the non-linear case; 3) insufficient measurement capability emphasizing the desire for optimal experimental design with the purpose of best use of the performed (expensive/labor intensive) experimental runs; 4) time-variance of the process which in the area of bioprocess control gives rise to the need for on-line identification of the models and, hence, imposing real time constraints on the procedures for experimental design and identification. To illustrate the different methodologies taking part in the model identification, a particular case study taken from wastewater treatment processes is developed throughout the text