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Updated: May 10, 2026

Microalgae Cultivation and Biomass Quantification in a Bench-Scale Photobioreactor with Corrosive Flue Gases

Microalgae Cultivation and Biomass Quantification in a Bench-Scale Photobioreactor with Corrosive Flue Gases

Published on: December 19, 2019

A Tool for On-Line Monitoring Microalgal Bioprocesses Based on Gas Balance Analysis.

Guillaume Cogne¹, Fernando Ferrel Ballestas^1,2, Mariana Titica¹

¹Nantes Université, Oniris, CNRS, GEPEA, UMR 6144, Saint-Nazaire, France.

Biotechnology and Bioengineering

|April 5, 2025

Summary

This study presents a new method to monitor microalgae growth and mass transfer in photobioreactors. It accurately predicts biomass and carbon dynamics, optimizing photobioreactor performance.

Keywords:

biomass estimation elemental balances mass transfer coefficients microalgae cultivation oxygen production rate photobioreactor

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Area of Science:

Biotechnology
Bioprocess Engineering
Algal Biotechnology

Background:

Photobioreactors are crucial for microalgae cultivation.
Accurate monitoring of microalgae growth and mass transfer is essential for process optimization.
Current methods may be limited in controlled, non-limiting conditions.

Purpose of the Study:

To develop and validate a novel indirect monitoring method for microalgae growth.
To evaluate the mass transfer efficiency in photobioreactors.
To provide a tool for enhanced process control and optimization.

Main Methods:

Utilized elemental composition, gas transfer rates, and oxygen production data.
Employed indirect measurements to estimate biomass concentration, total inorganic carbon, and nitrogen.
Calculated gas-liquid mass transfer coefficients.

Main Results:

Accurately predicted microalgae biomass growth.
Successfully characterized carbon dynamics within the photobioreactor.
Effectively determined gas-liquid mass transfer coefficients.
Demonstrated the method's robustness under controlled conditions.

Conclusions:

The novel method provides accurate insights into microalgae growth and carbon dynamics.
It enables effective characterization of mass transfer coefficients in photobioreactors.
This approach offers a valuable tool for optimizing photobioreactor performance and control.