Integrating Machine Learning for Predictive Modeling of Carbon Sequestration in Mixed Microalgal Systems
5 affiliations
- 1Chongqing University, Ministry of Education , , ,
- 2Chongqing University , , ,
- 3CMCU Engineering Co., Ltd. , ,
- 4Cranfield University , , ,
- 5University of Houston , , , ,
Abstract
Microalgal carbon sequestration offers an energy-efficient solution to global carbon emissions. Mixed microalgae outperform pure cultures in adaptability and carbon fixation. This study carried out mixed microalgae cultivation experiments under different experimental conditions, focusing on examining the effects of five key factors: inoculation concentration, initial inoculation ratio, nitrate concentration, CO2 gas flow rate, and CO2 concentration. In the single-variable experiment where the CO2 gas flow rate was adjusted, the maximum carbon sequestration of 5.35 g/L was achieved. An experimental data set of carbon sequestration performance characteristics of mixed microalgae under five culture conditions. The carbon sequestration performance of mixed microalgae was predicted using 13 machine learning models. The results showed that the linear model (R2 = 0.782) performed the poorest, followed by the nonlinear model (R2 = 0.941). The ensemble model (R2 = 0.950) significantly outperformed both, with random forest exhibiting the best performance.
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BibTeX
@article{yan2026integrating,
title = {{Integrating Machine Learning for Predictive Modeling of Carbon Sequestration in Mixed Microalgal Systems}},
author = {Huchao Yan and Hanwen Zhang and Xinggan Peng and Cheng Chen and Ao Xia and Yun Huang and Vinod Kumar and Xun Zhu and Qiang Liao},
journal = {ACS omega},
year = {2026},
month = oct,
doi = {10.1021/acsomega.6c07718},
url = {https://europepmc.org/article/PMC/PMC13670254},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Integrating Machine Learning for Predictive Modeling of Carbon Sequestration in Mixed Microalgal Systems
AU - Huchao Yan
AU - Hanwen Zhang
AU - Xinggan Peng
AU - Cheng Chen
AU - Ao Xia
AU - Yun Huang
AU - Vinod Kumar
AU - Xun Zhu
AU - Qiang Liao
PY - 2026
DA - 2026/10/02
JO - ACS omega
DO - 10.1021/acsomega.6c07718
UR - https://europepmc.org/article/PMC/PMC13670254
AB - Microalgal carbon sequestration offers an energy-efficient solution to global carbon emissions. Mixed microalgae outperform pure cultures in adaptability and carbon fixation. This study carried out mixed microalgae cultivation experiments under different experimental conditions, focusing on examining the effects of five key factors: inoculation concentration, initial inoculation ratio, nitrate concentration, CO2 gas flow rate, and CO2 concentration. In the single-variable experiment where the CO2 gas flow rate was adjusted, the maximum carbon sequestration of 5.35 g/L was achieved. An experimental data set of carbon sequestration performance characteristics of mixed microalgae under five culture conditions. The carbon sequestration performance of mixed microalgae was predicted using 13 machine learning models. The results showed that the linear model (R2 = 0.782) performed the poorest, followed by the nonlinear model (R2 = 0.941). The ensemble model (R2 = 0.950) significantly outperformed both, with random forest exhibiting the best performance.
SN - 2470-1343
ER -