Simultaneous versus sequential optimal experiment design for the identification of multi-parameter microbial growth...

Area of Science:

• * Bioprocess Engineering
• * Mathematical Modeling
• * Microbial Kinetics

Background:

• * Optimal experiment design for parameter estimation (OED/PE) is crucial for accurate kinetic model parameter determination.
• * Estimating multiple parameters simultaneously can lead to convergence issues and limited experimental information.
• * Alternative strategies involve reducing the problem to series of two-parameter estimations.

Purpose of the Study:

• * To evaluate and compare three OED/PE strategies for identifying the Cardinal Temperature Model with Inflection (CTMI).
• * To assess the impact of different OED/PE design strategies on the accuracy of CTMI parameter estimation.
• * To determine the most effective OED/PE approach for kinetic model parameterization.

Main Methods:

• * Implementation of three OED/PE strategies: single, global, and sequential two-parameter estimation.
• * Application of OED/PE to the four-parameter Cardinal Temperature Model with Inflection (CTMI).
• * Validation through simulation studies and experimental data from bioreactor implementation.

Main Results:

• * Simulation results indicated that the sequential OED/PE strategy produced parameter estimates deviating more from true values compared to single and global strategies.
• * Experimental data comparison showed comparable estimates between single and global OED/PE strategies.
• * Global OED/PE generally provided more accurate and reliable parameter estimates than the single strategy.

Conclusions:

• * The single and global OED/PE strategies are more effective for CTMI parameter estimation than the sequential approach.
• * Global OED/PE offers a robust and accurate method for kinetic model parameter identification.
• * OED/PE is a valuable tool for optimizing experimental design in bioprocess modeling.