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Updated: Aug 23, 2025

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

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Published on: August 14, 2020

An ensemble data assimilation modeling system for operational outdoor microalgae growth forecasting.

Hongxiang Yan¹, Mark S Wigmosta^1,2, Michael H Huesemann³

¹Pacific Northwest National Laboratory, Richland, Washington, USA.

Biotechnology and Bioengineering

|October 29, 2022

Summary

Accurate microalgae biomass forecasting is crucial for biofuel production. This study integrates models and data assimilation to improve short-term growth predictions by 60%, aiding operational planning.

Keywords:

Chlorella sorokiniana Huesemann Algae Biomass Growth Model biomass forecasting data assimilation particle filter

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Last Updated: Aug 23, 2025

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

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Published on: August 14, 2020

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Published on: October 28, 2021

Area of Science:

Biotechnology
Algaculture
Bioenergy

Background:

Microalgae are a promising feedstock for biofuels and biobased products.
Accurate forecasting of microalgae growth is essential for efficient pond management and harvesting strategies.

Purpose of the Study:

To develop and evaluate an integrated biomass forecasting system for microalgae cultivation.
To enhance short-term biomass forecasting accuracy using ensemble data assimilation.

Main Methods:

Integration of the Huesemann Algae Biomass Growth Model (BGM) and Modular Aquatic Simulation System in Two Dimensions (MASS2).
Application of ensemble data assimilation (DA) with Global Ensemble Forecast System (GEFS) meteorological forecasts.
Assimilation of biomass and water temperature measurements to improve model initial conditions.

Main Results:

The integrated forecasting system demonstrated an average improvement of 60% in 7-day biomass forecasting skills compared to methods without ensemble DA.
Successful pseudo-real-time application in three outdoor Chlorella sorokiniana cultivation ponds.
Validation of the system's efficacy in improving short-term biomass prediction accuracy.

Conclusions:

The developed BGM-MASS2-DA forecasting system significantly enhances microalgae biomass prediction capabilities.
The system shows potential for operational use in guiding pond management and harvesting decisions.
Ensemble data assimilation is a key factor in improving the accuracy of microalgae growth forecasts.