Modeling and multi-objective optimization of microalgae biomass production and CO2 biofixation using hybrid intelligence approaches

Hossain, S. M. Zakir; Sultana, Nahid; Razzak, Shaikh A.; Hossain, Mohammad M.

doi:10.1016/j.rser.2021.112016

Cited by 75Algal biology and biofuel productionMicrobial Metabolic Engineering and BioproductionAquatic Ecosystems and Phytoplankton Dynamics

Modeling and multi-objective optimization of microalgae biomass production and CO2 biofixation using hybrid intelligence approaches

Hossain, S. M. Zakir, Sultana, Nahid, Razzak, Shaikh A., Hossain, Mohammad M.·2022·Renewable and Sustainable Energy Reviews

DOI 10.1016/j.rser.2021.112016

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Application of improved ML and DL approaches has become increasingly pertinent in ACB systems, alluring researchers to explore capabilities of data analytics for creating sustainable SACB.
“Furthermore, in terms of smart ADT, the application of improved ML and DL approaches has become increasingly pertinent in ACB systems, alluring researchers to explore the capabilities of data analytics (DA) for creating sustainable SACB [25,96].”
From Reinvigorating algal cultivation for biomass production with digital twin technology - a smart sustainable infrastructure (2024) · cites this paper as [96]
The SVR model outperforms both the BRT and ANN models.
“Nevertheless, the SVR model outperforms both the BRT and ANN models.”
From Reinvigorating algal cultivation for biomass production with digital twin technology - a smart sustainable infrastructure (2024) · cites this paper as [96]
AI-driven models can improve CO2 biofixation efficiency compared to traditional methods.
“Key case studies demonstrate that AI-driven models can increase biomass productivity by up to 15–57%, improve CO2 biofixation efficiency, and enhance lipid and high-value compound yields by more than 20–43% compared to traditional methods.”
From Harnessing Artificial Intelligence to Revolutionize Microalgae Biotechnology: Unlocking Sustainable Solutions for High-Value Compounds and Carbon Neutrality (2025) · cites this paper as [25]
Hossain et al. (2022b) used SVR with Bayesian optimization to enhance C. vulgaris biomass productivity and CO2 biofixation, achieving R2 of 0.911 and optimal conditions of 40 °C, N:P 1:1, 12/12 light-dark, yielding 0.0979 g L−1 d−1 BP and 0.1408 g L−1 d−1 RCO2.
“For instance, Hossain et al. (2022b) [25] used SVR with Bayesian optimization to enhance C. vulgaris biomass productivity (BP) and CO2 biofixation (RCO2), achieving a high R2 of 0.911 and optimizing conditions to 40 °C, an N:P ratio of 1:1, and a 12/12 light–dark cycle, yielding 0.0979 g L−1 d−1 BP and 0.1408 g L−1 d−1 RCO2.”
From Harnessing Artificial Intelligence to Revolutionize Microalgae Biotechnology: Unlocking Sustainable Solutions for High-Value Compounds and Carbon Neutrality (2025) · cites this paper as [25]

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Authors: Hossain, S. M. Z., Sultana, N., Razzak, S. A., & Hossain, M. M.
Affiliations: University of Bahrain (BH)
Imam Abdulrahman Bin Faisal University (SA)
King Fahd University of Petroleum and Minerals (SA)
Corresponding author: Hossain, Mohammad M.
ORCID: Hossain, S. M. Zakir
Sultana, Nahid
Razzak, Shaikh A.
Hossain, Mohammad M.
Venue: Renewable and Sustainable Energy Reviews
Publisher: Elsevier BV
Published: 2022-04
Volume: 157
Pages: 112016
ISSN: 1364-0321 (ISSN-L)
DOI: 10.1016/j.rser.2021.112016
OpenAlex: W4206166121
Cited by: 75
References: 72
Citations by year: 2026: 18 · 2025: 23 · 2024: 15 · 2023: 11 · 2022: 8
FWCI: 2.57
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
doi.org/10.15223/policy-017 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
doi.org/10.15223/policy-012 (stm-asf)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-004 (stm-asf)
Locations: Renewable and Sustainable Energy Reviews (publishedVersion)
RePEc: Research Papers in Economics (submittedVersion)
Funders: King Fahd University of Petroleum and Minerals (DF191050)
Kingdom of Saudi Arabia Ministry of Education
Topics: Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Algal biology and biofuel production
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
Physical Sciences › Environmental Science › Environmental Chemistry › Aquatic Ecosystems and Phytoplankton Dynamics
Keywords: Chlorella vulgaris, microalgae cultivation, CO2 biofixation, N:P ratio, light-dark cycle, temperature effects, support vector regression, boosted regression trees, artificial neural networks, Bayesian optimization, crow search algorithm, multi-objective optimization, artificial intelligence modeling

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