Analysis of green algal growth via dynamic model simulation and process optimization

Area of Science:

• Biotechnology
• Renewable Energy
• Algal Cultivation

Background:

• Chlamydomonas reinhardtii is a promising microalga for sustainable biofuel generation.
• Process simulation models are crucial for scaling up laboratory findings to industrial applications.
• Dynamic models are needed to accurately predict algal growth under various conditions.

Purpose of the Study:

• To develop and validate dynamic models for simulating photo-autotrophic and photo-mixotrophic growth of Chlamydomonas reinhardtii.
• To determine optimal cultivation parameters and photobioreactor configurations for enhanced biomass production.
• To identify key factors limiting algal growth and optimize cultivation strategies for biofuel applications.

Main Methods:

• Construction of two dynamic models: one for photo-autotrophic and one for photo-mixotrophic growth.
• Application of a novel parameter estimation methodology for model calibration.
• Experimental validation of model predictions.
• Simulation of different photobioreactor configurations and operating modes.

Main Results:

• The photo-mixotrophic model accurately predicted C. reinhardtii growth under varying light intensities and photobioreactor designs.
• Optimal dissolved CO2 concentration was 0.0643 g·L(-1) and optimal light intensity was 47 W·m(-2) for photo-autotrophic growth.
• Cell decay rate was identified as the primary growth limitation, surpassing light attenuation.
• A double-exposure-surface photobioreactor operating at low light intensity (<50 W·m(-2)) is optimal for scale-up.
• Maximum biomass productivity of 0.053 g·L(-1)·hr(-1) was achieved in a continuous stirred-tank reactor under continuous operation.

Conclusions:

• Dynamic modeling provides a robust framework for optimizing Chlamydomonas reinhardtii cultivation for biofuel production.
• Continuous stirred-tank reactors offer the best performance for maximizing biomass productivity and minimizing energy costs.
• The study provides critical insights for the efficient scale-up of algal biofuel processes.