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Comparative assessment of microalgal growth kinetic models based on light intensity and biomass concentration.

Authors :
Esteves AF
Gonçalves AL
Vilar VJ
Pires JCM
Source :
Bioresource technology [Bioresour Technol] 2024 Feb; Vol. 394, pp. 130167. Date of Electronic Publication: 2023 Dec 13.
Publication Year :
2024

Abstract

The comprehensive evaluation and validation of mathematical models for microalgal growth dynamics are essential for improving cultivation efficiency and optimising photobioreactor design. A considerable gap in comprehending the relation between microalgal growth, light intensity and biomass concentration arises since many studies focus solely on associating one of these factors. This paper compares microalgal growth kinetic models, specifically focusing on the combined impact of light intensity and biomass concentration. Considering a dataset (experimental results and literature values) concerning Chlorella vulgaris, nine kinetic models were assessed. Bannister and Grima models presented the best fitting performance to experimental data (RMSE ≤ 0.050 d <superscript>-1</superscript> ; R <superscript>2</superscript> ≥0.804; d <subscript>2</subscript> ≥0.943). Cultivation conditions conducting photoinhibition were identified in some kinetic models. After testing these models on independent datasets, Bannister and Grima models presented superior predictive performance (RMSE = 0.022-0.023 d <superscript>-1</superscript> ; R <superscript>2</superscript>  = 0.878-0.884; d <subscript>2</subscript> : 0.976-0.975). The models provide valuable tools for predicting microalgal growth and optimising operational parameters, reducing the need for time-consuming and costly experiments.<br />Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br /> (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Details

Language :
English
ISSN :
1873-2976
Volume :
394
Database :
MEDLINE
Journal :
Bioresource technology
Publication Type :
Academic Journal
Accession number :
38101550
Full Text :
https://doi.org/10.1016/j.biortech.2023.130167