Dynamic Optimization Approach to Estimate Kinetic Parameters of Monod-Based Microalgae Growth Models

Area of Science:

• Biotechnology
• Biochemical Engineering
• Applied Mathematics

Background:

• Microalgae cultivation in photobioreactors is crucial for biomass production.
• Accurate prediction of microalgae growth dynamics is essential for process optimization.
• Monod-based models are widely used for describing microbial growth kinetics.

Purpose of the Study:

• To predict microalgae biomass concentration in photobioreactors using Monod-based models.
• To evaluate kinetic parameters like maximum specific growth rate and light saturation constant.
• To determine optimal control strategies for microalgae cultivation.

Main Methods:

• Application of Monod-based growth models for microalgae.
• Utilizing nonlinear least squares methods to estimate kinetic parameters, minimizing sum of squares error (SSE).
• Employing dynamic optimization and optimal input design for control function development, incorporating state equations, cost, and Hamiltonian functions.

Main Results:

• Kinetic parameters for microalgae growth were successfully evaluated.
• The significance of accurate initial guesses in nonlinear least squares was highlighted.
• A control function for microalgae growth in photobioreactors was established through dynamic optimization.

Conclusions:

• Microalgae biomass production can be reliably predicted using numerical methods.
• Optimal control strategies enhance the efficiency of microalgae cultivation in photobioreactors.
• The developed models and methods provide a framework for precise biomass yield prediction and control.