An Identifiable State Model To Describe Light Intensity Influence on Microalgae Growth.

Area of Science:

• Biotechnology and Bioengineering
• Algal Biotechnology
• Mathematical Modeling

Background:

• Industrial microalgae cultivation faces challenges limiting yield, primarily due to inefficient solar energy harvesting.
• Accurate predictive models are essential for designing and optimizing large-scale microalgae production systems for fuels and chemicals.

Purpose of the Study:

• To develop and validate a first-principles model representing the influence of light regimes on Nannochloropsis salina growth.
• To utilize experimental data, including in vivo fluorescence, for robust model development and parameter estimation.

Main Methods:

• Application of first-principles modeling to simulate microalgae growth under various light conditions.
• Integration of experimental data, specifically in vivo fluorescence measurements, for model calibration and validation.
• Assessment and modification of model structure to ensure identifiability and reliable parameter estimation.

Main Results:

• The developed model accurately describes growth curves and fluorescence data for Nannochloropsis salina.
• The model effectively captures the complex effects of different light regimes on algal growth.
• Model structure modifications ensured robust and reliable estimation of model parameters.

Conclusions:

• First-principles models are effective tools for understanding and optimizing microalgae cultivation systems.
• Accurate modeling can significantly contribute to improving the industrial viability of microalgae as a feedstock for biofuels and chemicals.
• This work provides a reliable modeling framework for Nannochloropsis salina, applicable to enhancing production efficiency.