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Green Algae

Green Algae

Green algae, also referred to as chlorophytes, are different from red algae in having the chloroplasts containing chlorophylls a and b, which give them their distinct green hue. However, they lack phycobiliproteins, preventing them from developing the red or blue-green pigmentation seen in red algae. In terms of photosynthetic pigment composition, green algae closely resemble plants and share a close evolutionary relationship with them. Taxonomically Green algae belong to Phylum Chlorophyta in...

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Coupling Carbon Capture from a Power Plant with Semi-automated Open Raceway Ponds for Microalgae Cultivation

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Improving Microalgal Biomass Productivity Using Weather-Forecast-Informed Operations.

Song Gao¹, Hongxiang Yan², Nathan Beirne¹

¹Marine and Coastal Research Laboratory, Pacific Northwest National Laboratory, Sequim, WA 98382, USA.

|May 14, 2022

Summary

Optimizing microalgal cultivation with a novel tool that uses weather forecasts to adjust dilution rates significantly boosts biomass productivity. This approach enhances yield compared to standard or fixed-rate methods for sustainable algae farming.

Keywords:

biomass productivity dilution rate microalgae photobioreactor weather-forecast-informed

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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

Area of Science:

* Algal biotechnology and cultivation systems.
* Environmental monitoring and predictive modeling for agriculture.

Background:

* Microalgal cultivation systems are sensitive to operational parameters like culture dilution, impacting biomass productivity.
* Outdoor cultivation faces challenges due to variable light and temperature, hindering optimization of growth parameters.
* Determining optimal operational strategies for maximizing biomass yield in dynamic environments is crucial.

Purpose of the Study:

* To develop and evaluate a pond operation optimization tool for microalgal cultivation.
* To identify the optimal dilution rate that maximizes biomass productivity using predictive weather data.
* To compare the effectiveness of weather-forecast-informed dilution against standard and fixed-rate methods.

Main Methods:

* Development of an operation optimization tool predicting biomass growth based on future weather conditions.
* Implementation of three dilution scenarios: standard batch, fixed-rate dilution, and weather-forecast-informed dilution.
* Daily optimization of dilution ratio in the weather-forecast-informed scenario using a 24-hour weather forecast.

Main Results:

* Weather-forecast-informed dilution improved biomass productivity by 47% compared to standard batch cultivation.
* This method also showed a 20% increase in biomass productivity over fixed-rate dilution.
* The optimization tool successfully identified dilution rates that maximize biomass growth under changing conditions.

Conclusions:

* A weather-forecast-informed dilution strategy significantly enhances microalgal biomass productivity in outdoor pond systems.
* The developed pond operation optimization tool is effective for real-time decision-making to maximize algal growth.
* This predictive approach offers a valuable solution for optimizing large-scale microalgal cultivation under variable environmental conditions.