Artificial neural networks prediction and optimization based on four light regions for light utilization from Synechocystis sp. PCC 6803
Abstract
Light is crucial in microalgae growth. However, dividing the microalgae growth region into light and dark regions has limitations. In this study, the light response of Synechocystis sp. PCC 6803 was investigated to define four light regions (FLRs): light compensation region, light limitation region, light saturation region, and photoinhibition region. The proportions of cells' residence time in the FLRs and the number of times cells (NTC) passed through the FLRs in photobioreactors were calculated by using MATLAB. Based on the FLRs and NTC passed through the FLRs, a growth model was established by using artificial neural network (ANN).The ANN model had a validation R 2 value of 0.97, which was 76.36% higher than the model based on light-dark regions. The high accuracy of the ANN model was further verified through dynamic adjustment of light intensity experiments.This study confirmed the importance of the FLRs for studying microalgae growth dynamics.
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The ANN model by Yu et al. was validated using a separate dataset, demonstrating prediction error less than 5%.
“The model was validated using a separate dataset, demonstrating a prediction error of less than 5%, which highlights its robustness and reliability in optimizing microalgae growth, thereby demonstrating its effectiveness in optimizing microalgae growth.”
From Harnessing Artificial Intelligence to Revolutionize Microalgae Biotechnology: Unlocking Sustainable Solutions for High-Value Compounds and Carbon Neutrality (2025) · cites this paper as [21]
Yu et al. (2024) developed an ANN growth model for Synechocystis sp. PCC 6803 based on four light regions, achieving validation R2 of 0.97, 76.36% more accurate than traditional light-dark models.
“For example, Yu et al. (2024) [21] developed an artificial neural network (ANN) growth model for Synechocystis sp. PCC 6803 based on four light regions (FLRs), achieving a validation R2 value of 0.97, 76.36% more accurate than traditional light–dark models.”
From Harnessing Artificial Intelligence to Revolutionize Microalgae Biotechnology: Unlocking Sustainable Solutions for High-Value Compounds and Carbon Neutrality (2025) · cites this paper as [21]
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Wu, Y., Shan, L., Zhao, W., & Lu, X. (2025) · Marine Drugs · doi:10.3390/md23050184 · Cited by 59 · see citing works
- Authors
- Yu, T., Fan, F., Huang, L., Wang, W., Wan, M., & Li, Y.
- Venue
- Bioresource Technology
- Publisher
- Elsevier BV
- Published
- 2024-02
- Volume
- 394
- Pages
- 130166
- ISSN
- 0960-8524
- Cited by
- 15
- References
- 7
- Language
- en
- Licence
- Funders
- National Natural Science Foundation of China (31372548)
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