Modeling and optimization of biomass productivity of Chlorella vulgaris using response surface methodology, analysis of variance and machine learning for carbon dioxide capture
Abstract
This study explores bioremediation's effectiveness in reducing carbon emissions through the use of microalgae Chlorella vulgaris, known for capturing carbon dioxide and producing biomass. The impact of temperature and light intensity on productivity and carbon dioxide capture was investigated, and cultivation conditions were optimized in a photobioreactor using response surface methodology (RSM), analysis of variance (ANOVA), and deep neural networks (DNN). The optimal conditions determined were 28.74 °C and 225 μmol/m 2/s with RSM, and 29.55 °C and 226.77 μmol/m 2/s with DNN, closely aligning with literature values (29 °C and 225 μmol/m 2/s). DNN demonstrated superior performance compared to RSM, achieving higher accuracy due to its capacity to process larger datasets using epochs and batches. The research serves as a foundation to further in this field by demonstrating the potential of utilizing diverse mathematical models to optimize bioremediation conditions, and offering valuable insights to improve carbon dioxide capture efficiency in microalgae cultivation.
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The studied literature expressed concerns about the synchronization (real-time) of data-gathering devices and unresolved difficulties due to the variation in data update rates.
“The studied literature expressed concerns about the synchronization (realtime) of data-gathering devices and unresolved difficulties involved due to the variation in data update rates [61].”
From Reinvigorating algal cultivation for biomass production with digital twin technology - a smart sustainable infrastructure (2024) · cites this paper as [61]
The performance of DNN was found to be superior to that of RSM, as it achieved higher accuracy by leveraging its ability to handle larger datasets.
“The performance of DNN was found to be superior to that of RSM, as it achieved higher accuracy by leveraging its ability to handle larger datasets through the use of epochs and batches.”
From Reinvigorating algal cultivation for biomass production with digital twin technology - a smart sustainable infrastructure (2024) · cites this paper as [61]
The DNN model achieved prediction accuracy of 95% with error within ±1 °C.
“The DNN model achieved a prediction accuracy of 95% with a prediction error within ±1 °C, demonstrating its precision in temperature optimization, leading to enhanced CO2 capture efficiency and biomass productivity, in turn emphasizing the critical role of precise temperature control in microalgae cultivation systems.”
From Harnessing Artificial Intelligence to Revolutionize Microalgae Biotechnology: Unlocking Sustainable Solutions for High-Value Compounds and Carbon Neutrality (2025) · cites this paper as [30]
The DNN model achieved a 15% increase in biomass productivity compared to traditional temperature control methods.
“Specifically, the DNN model achieved a 15% increase in biomass productivity compared to traditional temperature control methods, highlighting the significant benefits of AI-driven temperature optimization and control.”
From Harnessing Artificial Intelligence to Revolutionize Microalgae Biotechnology: Unlocking Sustainable Solutions for High-Value Compounds and Carbon Neutrality (2025) · cites this paper as [30]
Janjua et al. (2024) identified optimal temperature of 29.55 °C for C. vulgaris CO2 capture using DNN, outperforming RSM prediction of 28.74 °C.
“Their study identified an optimal temperature of 29.55 °C with DNN, outperforming RSM’s prediction of 28.74 °C.”
From Harnessing Artificial Intelligence to Revolutionize Microalgae Biotechnology: Unlocking Sustainable Solutions for High-Value Compounds and Carbon Neutrality (2025) · cites this paper as [30]
AI-driven models can increase microalgae biomass productivity by up to 15-57% 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 [30]
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- Authors
- Janjua, M. Y., Azfar, A., Asghar, Z., & Shehzad Quraishi, K.
- Affiliations
- Pakistan Institute of Engineering and Applied Sciences (PK)
- Corresponding author
- Azfar, Azaam
- ORCID
- Azfar, Azaam
- Venue
- Bioresource Technology
- Publisher
- Elsevier BV
- Published
- 2024-05
- Volume
- 400
- Pages
- 130687
- ISSN
- 0960-8524 (ISSN-L)
- OpenAlex
- W4394789402
- Cited by
- 26
- References
- 23
- Citations by year
- 2026: 6 · 2025: 17 · 2024: 3
- FWCI
- 1.74
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Bioresource Technology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Quaid-i-Azam University
- Pakistan Institute of Engineering and Applied Sciences (PIEAS-DChE/FYP/19-23/06)
- Topics
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Algal biology and biofuel production
- Physical Sciences › Engineering › Mechanical Engineering › Carbon Dioxide Capture Technologies
- Physical Sciences › Environmental Science › Environmental Chemistry › Aquatic Ecosystems and Phytoplankton Dynamics
- Keywords
- Chlorella vulgaris, microalgae cultivation, CO2 capture, biomass productivity, temperature optimization, light intensity, photobioreactors, bioremediation, carbon emission reduction, deep neural network, response surface methodology, cultivation condition optimization
- MeSH terms
- Machine Learning, Analysis of Variance, Biodegradation, Environmental, Carbon Dioxide, Light, Models, Biological, Temperature, Biomass, Chlorella vulgaris, Microalgae, Photobioreactors
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