An integrated growth kinetics and computational fluid dynamics model for the analysis of algal productivity in open raceway ponds
Open accessAbstract
An integrated growth kinetic, light transfer and computational fluid dynamics (CFD) model was developed to simulate the algal growth in open raceway ponds (ORP). C. vulgaris was used as a model algal strain. The coefficients of the growth kinetics were experimentally determined for the prediction of the growth of C. vulgaris as a function of environmental factors of light intensity, temperature and pH value. Experiments were conducted to grow C. vulgaris in lab-scale ORPs with medium depths of 0.20, 0.25 and 0.30 m to validate the mathematical model. The final measured biomass concentration after the 3-week growth were 0.48, 0.41, and 0.35 g/L for the ORPs with the medium depths of 0.20, 0.25, and 0.30 m, respectively. The predicted algal productivities for a 3-week cultivation were 7.3, 7.4, and 7.5 g/m²/day for depths of 0.20, 0.25, and 0.30 m, respectively, which well agreed with the measured values of 6.8, 7.2 and 7.4 g/m²/day, respectively. The biomass productivity decreased with the increase of growth time due to the increase of cell concentration. The model was further used to analyze the effects of different harvesting strategies on the algal productivity in ORPs. The algal productivity for the 3-week cultivation in the ORP with a 0.2 m depth by harvesting 50% algae at the target 0.2 g/L cell density was 10.5 g/m²/day, which was 43.8% higher than 7.3 g/m²/day for the 3-week cultivation under the same condition without harvesting at a final cell density of 0.48 g/L. The average algal productivity decreased with the increase of harvesting cell density.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Some researchers selected solving the full RTE numerically in one or more dimensions.
“Instead of using simplified or analytical models, some researchers selected the second approach, which is solving the full RTE numerically in one or more dimensions [18,21,26-28,34].”
From Modeling and Simulation of Photobioreactors with Computational Fluid Dynamics—A Comprehensive Review (2022) · cites this paper as [34]
Microalgae cells are mostly modeled as discrete objects in photobioreactor CFD models.
“In contrast, microalgae cells are mostly modeled as discrete objects [23,24,26-29,31,34,36-38,136].”
From Modeling and Simulation of Photobioreactors with Computational Fluid Dynamics—A Comprehensive Review (2022) · cites this paper as [34]
Cites
Lin, S. (2005) · Journal of Phycology · doi:10.1111/j.1529-8817.2005.00114.x · Cited by 1,333 · see citing works
Andersen, R. A., & Phycological Society of America (2005) · Choice Reviews Online · doi:10.5860/choice.43-0925 · Cited by 1,299 · see citing works
Jorquera, O., Kiperstok, A., Sales, E. A., Embiruçu, M., & Ghirardi, M. L. (2010) · Bioresource Technology · doi:10.1016/j.biortech.2009.09.038 · Cited by 863 · see citing works
(2013) · doi:10.1007/978-94-007-5479-9 · Cited by 670 · see citing works
Convenient Model To Describe the Combined Effects of Temperature and pH on Microbial Growth
Rosso, L., Lobry, J. R., Bajard, S., & Flandrois, J. (1995) · Applied and Environmental Microbiology · doi:10.1128/aem.61.2.610-616.1995 · Cited by 607 · see citing works
Validation of a simple model accounting for light and temperature effect on microalgal growth
Bernard, O., & Rémond, B. (2012) · Bioresource Technology · doi:10.1016/j.biortech.2012.07.022 · Cited by 349 · see citing works
Chiaramonti, D., Prussi, M., Casini, D., Tredici, M. R., Rodolfi, L., Bassi, N., Zittelli, G. C., & Bondioli, P. (2012) · Applied Energy · doi:10.1016/j.apenergy.2012.07.040 · Cited by 229 · see citing works
Wastewater treatment high rate algal ponds (WWT HRAP) for low-cost biofuel production
Mehrabadi, A., Craggs, R. J., & Farid, M. M. (2014) · Bioresource Technology · doi:10.1016/j.biortech.2014.11.004 · Cited by 194 · see citing works
Modeling Algae Growth in an Open-Channel Raceway
James, S. C., & Boriah, V. (2010) · Journal of Computational Biology · doi:10.1089/cmb.2009.0078 · Cited by 194 · see citing works
CONTROLLING GREENHOUSE LIGHT TO A CONSISTENT DAILY INTEGRAL
Albright, L. D., Both, A. J., & Chiu, A. J. (2000) · Transactions of the ASAE · doi:10.13031/2013.2721 · Cited by 119 · see citing works
Almohammed, N., Alobaid, F., Breuer, M., et al. (2014) · Powder Technology · doi:10.1016/j.powtec.2014.05.024 · Cited by 104 · see citing works
Simulation of algae growth in a bench-scale bubble column reactor
Wu, X., & Merchuk, J. C. (2002) · Biotechnology and Bioengineering · doi:10.1002/bit.10350 · Cited by 93 · see citing works
Liffman, K., Paterson, D. A., Liovic, P., & Bandopadhayay, P. C. (2012) · Process Safety and Environmental Protection · doi:10.1016/j.cherd.2012.08.007 · Cited by 81 · see citing works
Marsullo, M., Mian, A., Ensinas, A. V., Manente, G., Lazzaretto, A., & Marechal, F. M. A. (2015) · Frontiers in Energy Research · doi:10.3389/fenrg.2015.00041 · Cited by 78 · see citing works
Berberoglu, H., Gomez, P. S., & Pilon, L. (2009) · Journal of Quantitative Spectroscopy and Radiative Transfer · doi:10.1016/j.jqsrt.2009.04.005 · Cited by 76 · see citing works
Kong, B., & Vigil, R. D. (2014) · Bioresource Technology · doi:10.1016/j.biortech.2014.01.052 · Cited by 64 · see citing works
Amini, H., Wang, L., & Shahbazi, A. (2016) · Chemical Engineering Science · doi:10.1016/j.ces.2016.06.025 · Cited by 42 · see citing works
Self-citationGreen biorefinery of fresh cattail for microalgal culture and ethanol production
Rahman, Q. M., Wang, L., Zhang, B., Xiu, S., & Shahbazi, A. (2015) · Bioresource Technology · doi:10.1016/j.biortech.2015.03.013 · Cited by 38 · see citing works
Self-citationAmini, H., Hashemisohi, A., Wang, L., Shahbazi, A., Bikdash, M. U., KC, D. B., & Yuan, W. W. (2016) · Chemical Engineering Science · doi:10.1016/j.ces.2016.09.003 · Cited by 37 · see citing works
Self-citationOptical behavior of algae particles in photobioreactors
Hannis, K. (2013) · Research Repository (Delft University of Technology) · Cited by 6 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Photobioreactor configurations in cultivating microalgae biomass for biorefinery
Barboza-Rodríguez, R., Rodríguez-Jasso, R. M., Rosero-Chasoy, G., Rosales Aguado, M. L., & Ruiz, H. A. (2024) · Bioresource Technology · doi:10.1016/j.biortech.2023.130208 · Cited by 97 · see citing works
Modeling and Simulation of Photobioreactors with Computational Fluid Dynamics—A Comprehensive Review
Luzi, G., & McHardy, C. (2022) · Energies · doi:10.3390/en15113966 · Cited by 44 · see citing works
“[34] RTE DOM 3D L, S E-E FVM 3D RANS RNG k-ε ODE OEM Inostroza et al.”
BackgroundA hybrid approach for photobioreactor modeling in CO2 biofixation using microalgae cultivation
Shahhoseyni, S., Rahmani, M., & Sivaram, A. (2026) · Algal Research · doi:10.1016/j.algal.2025.104503 · Cited by 3 · see citing works
- Authors
- Amini, H., Wang, L., Hashemisohi, A., Shahbazi, A., Bikdash, M., KC, D., & Yuan, W.
- Affiliations
- North Carolina Agricultural and Technical State University (US)
- North Carolina State University (US)
- Corresponding author
- Wang, Lijun
- ORCID
- Venue
- Computers and Electronics in Agriculture
- Publisher
- Elsevier BV
- Published
- 2018-02
- Volume
- 145
- Pages
- 363-372
- ISSN
- 0168-1699 (ISSN-L)
- OpenAlex
- W2792225385
- Cited by
- 23
- References
- 20
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 4 · 2023: 6 · 2022: 3 · 2021: 2 · 2020: 2 · 2019: 1 · 2018: 1
- FWCI
- 0.35
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Computers and Electronics in Agriculture (acceptedVersion)
- Funders
- U.S. Department of Agriculture (2013-38821-21141, NC.X-294-5-15-130-1)
- Topics
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Algal biology and biofuel production
- Physical Sciences › Environmental Science › Environmental Chemistry › Aquatic Ecosystems and Phytoplankton Dynamics
- Physical Sciences › Engineering › Biomedical Engineering › Fluid Dynamics and Mixing
- Keywords
- raceway ponds, algal productivity, Chlorella vulgaris, algal growth kinetics, computational fluid dynamics, harvest strategies, cell density, pond depth, biomass productivity
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
