Optimization of Spirulina sp. cultivation using reinforcement learning with state prediction based on LSTM neural network
Open accessAbstract
Spirulina/Arthrospira is well known as a microscopic filamentous cyanobacterium, which is rich in nutrition and minerals. Therefore, Spirulina/Arthrospira cultivation industry has been adopting many optimization methods for enhancing biomass productivity. Recently, machine learning has been an emerging approach thanks to their efficacy. In this paper, a novel time-dependent reinforcement learning (RL) method with state prediction by long short-term memory (LSTM) networks is proposed to optimize the dry-weight yield of Spirulina sp. HH cultivation. The simulation results show that under the same light condition, with the proposed algorithm, the Spirulina sp. HH cultivation system produces a yield 17% higher than that of the traditional cultivation method and 10% higher than that of the threshold-based method. The results of the RL method from this study promise a significant benefit in Spirulina farm production when applying it for proactively planning biomass production and enhancing profit. Graphical abstract
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The cultivation of microalgae is commonly carried out in two ways: open systems, such as tanks or lagoons, or in closed systems, such as photobioreactors. In both cultivation models, Spirulina receives energy directly from natural light sources for photosynthesis.
“The cultivation of microalgae is commonly carried out in two ways: open systems, such as tanks or lagoons, or in closed systems, such as photobioreactors. In both cultivation models, Spirulina receives energy directly from natural light sources for photosynthesis [48].”
From SPIRULINA (ARTHROSPIRA PLATENSIS): AN ALTERNATIVE SOURCE OF NUTRIENTS - A REVIEW (2025) · cites this paper as [48]
DD models are developed using ML, DL, and reinforcement learning algorithms that fall in the AI-integrated process systems domain.
“These DD models are developed using ML, DL, and reinforcement learning (RL) algorithms that fall in the AI-integrated process systems domain [71].”
From Reinvigorating algal cultivation for biomass production with digital twin technology - a smart sustainable infrastructure (2024) · cites this paper as [71]
The Spirulina sp. growth system using the suggested algorithm achieves a yield 17% greater than the conventional cultivation method and 10% greater than the ... under identical lighting conditions.
“The simulation findings report that, when exposed to identical lighting conditions, the Spirulina sp. growth system, when utilizing the suggested algorithm, achieves a yield that is 17 % greater than that of the conventional cultivation method and 10 % greater than that of the”
From Reinvigorating algal cultivation for biomass production with digital twin technology - a smart sustainable infrastructure (2024) · cites this paper as [71]
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,263 · see citing works
Watkins, C., & Dayan, P. (1992) · Machine-mediated learning · doi:10.1023/a:1022676722315 · Cited by 14,277 · see citing works
Reinforcement Learning: An Introduction
Johnson, J. D., Li, J., & Chen, Z. (2000) · Neurocomputing · doi:10.1016/s0925-2312(00)00324-6 · Cited by 8,821 · see citing works
Gaussian Processes in Machine Learning
Rasmussen, C. E. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-28650-9_4 · Cited by 5,727 · see citing works
Bellman, R. (1957) · Indiana University Mathematics Journal · doi:10.1512/iumj.1957.6.56038 · Cited by 2,408 · see citing works
A Deep Reinforced Model for Abstractive Summarization
Paulus, R., Xiong, C., & Socher, R. (2017) · arXiv · doi:10.48550/arxiv.1705.04304 · Cited by 1,182 · see citing works
Bolton, J. J. (2006) · Journal of Experimental Marine Biology and Ecology · doi:10.1016/j.jembe.2006.05.002 · Cited by 644 · see citing works
Spirulina – From growth to nutritional product: A review
Soni, R. A., Sudhakar, K., & Rana, R. S. (2017) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2017.09.010 · Cited by 528 · see citing works
Béchet, Q., Shilton, A., & Guieysse, B. (2013) · Biotechnology Advances · doi:10.1016/j.biotechadv.2013.08.014 · Cited by 430 · see citing works
Growth kinetic models for microalgae cultivation: A review
Lee, E., Jalalizadeh, M., & Zhang, Q. (2015) · Algal Research · doi:10.1016/j.algal.2015.10.004 · Cited by 382 · see citing works
Ma, Y., Kelman, A. D., Daly, A., & Borrelli, F. (2012) · IEEE Control Systems · doi:10.1109/mcs.2011.2172532 · Cited by 347 · see citing works
Virtual to Real Reinforcement Learning for Autonomous Driving
Pan, X., You, Y., Wang, Z., & Lu, C. (2017) · Procedings of the British Machine Vision Conference 2017 · doi:10.5244/c.31.11 · Cited by 326 · cited 1× · see citing works
“…reinforcement learning has been successfully applied in many aspects of human life, such as robotics (Kormushev et al. 2013), self-driving cars (Pan et al. 2017; Liang et al. 2018), natural language processing (Choi et al. 2017; Paulus et al. 2017), healthcare (Schaefer et al. 2005), and…”
Spirulina in Human Nutrition and Health
(2007) · doi:10.1201/9781420052572 · Cited by 322 · see citing works
CIRL: Controllable Imitative Reinforcement Learning for Vision-Based Self-driving
Liang, X., Wang, T., Yang, L., & Xing, E. P. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01234-2_36 · Cited by 294 · see citing works
Reinforcement Learning in Robotics: Applications and Real-World Challenges
Kormushev, P. S., Calinon, S., & Caldwell, D. G. (2013) · Robotics · doi:10.3390/robotics2030122 · Cited by 259 · see citing works
Light management technologies for increasing algal photobioreactor efficiency
Nwoba, E. G., Parlevliet, D. A., Laird, D. W., Alameh, K., & Moheimani, N. R. (2019) · Algal Research · doi:10.1016/j.algal.2019.101433 · Cited by 248 · see citing works
Measuring Growth Rates in Microalgal Cultures
Michelle Wood, A., Everroad, R. C., & Wingard, L. M. (2005) · Algal Culturing Techniques · doi:10.1016/b978-012088426-1/50019-6 · Cited by 243 · see citing works
Optimal electricity supply bidding by Markov decision process
Song, H., Liu, C.-C., Lawarree, J. P., & Dahlgren, R. W. (2000) · IEEE Transactions on Power Systems · doi:10.1109/59.867150 · Cited by 240 · see citing works
Growth kinetics of photosynthetic microorganisms
Aiba, S. (1982) · Advances in Biochemical Engineering/Biotechnology · doi:10.1007/3540116982_3 · Cited by 213 · see citing works
Ogbonna, J. C., & Tanaka, H. (2000) · Journal of Applied Phycology · doi:10.1023/a:1008194627239 · Cited by 189 · see citing works
Muys, M., Sui, Y., Schwaiger, B., Lesueur, C., Vandenheuvel, D., Vermeir, P., & Vlaeminck, S. E. (2019) · Bioresource Technology · doi:10.1016/j.biortech.2018.12.059 · Cited by 175 · see citing works
Coarse-to-Fine Question Answering for Long Documents
Choi, E., Hewlett, D., Uszkoreit, J., Polosukhin, I., Lacoste, A., & Berant, J. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1020 · Cited by 170 · see citing works
Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising
Jin, J., Song, C., Li, H., Gai, K., Wang, J., & Zhang, W. (2018) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3269206.3272021 · Cited by 169 · see citing works
Modeling Medical Treatment Using Markov Decision Processes
Schaefer, A. J., Bailey, M. D., Shechter, S. M., & Roberts, M. S. (2005) · International Series in Operations Research & Management Science · doi:10.1007/1-4020-8066-2_23 · Cited by 155 · see citing works
Nutraceuticals From Algae and Cyanobacteria
Udayan, A., Arumugam, M., & Pandey, A. (2017) · Algal Green Chemistry · doi:10.1016/b978-0-444-63784-0.00004-7 · Cited by 143 · see citing works
Microalgae Potential and Multiple Roles—Current Progress and Future Prospects—An Overview
Ravindran, B., Gupta, S. K., Cho, W.-M., Kim, J., Lee, S., Jeong, K.-H., Lee, D., & Choi, H.-C. (2016) · Sustainability · doi:10.3390/su8121215 · Cited by 124 · see citing works
Phycocyanin from Microalgae: Properties, Extraction and Purification, with Some Recent Applications
de Morais, M. G., Prates, D. D. F., Moreira, J. B., Duarte, J. H., & Costa, J. A. V. (2018) · Industrial Biotechnology · doi:10.1089/ind.2017.0009 · Cited by 109 · see citing works
Jeong, K., Joo, G., Kim, H., Ha, K., & Recknagel, F. (2001) · Ecological Modelling · doi:10.1016/s0304-3800(01)00300-3 · Cited by 104 · see citing works
Kinetics and bioenergetics of light‐limited photoautotrophic growth of Spirulina platensis
Lee, H. Y., Erickson, L. E., & Yang, S. S. (1987) · Biotechnology and Bioengineering · doi:10.1002/bit.260290705 · Cited by 101 · see citing works
Exact Solutions to Time-Dependent MDPs
Boyan, J. A., & Littman, M. L. (2000) · Cited by 97 · see citing works
Economic and Distributed Model Predictive Control: Recent Developments in Optimization-Based Control
Müller, M. A., & Allgöwer, F. (2017) · SICE Journal of Control Measurement and System Integration · doi:10.9746/jcmsi.10.39 · Cited by 97 · see citing works
Fan, H., Wang, K., Wang, C., Yu, F., He, X., Ma, J., & Li, X. (2020) · Environmental Technology & Innovation · doi:10.1016/j.eti.2020.100849 · Cited by 78 · see citing works
Salama, E., Hwang, J.-H., El-Dalatony, M. M., Kurade, M. B., Kabra, A. N., Abou-Shanab, R. A. I., Kim, K., Yang, I.-S., Govindwar, S. P., Kim, S., et al. (2018) · Bioresource Technology · doi:10.1016/j.biortech.2018.02.006 · Cited by 74 · see citing works
MORPHOLOGICAL REVERSION OF SPIRULINA (ARTHROSPIRA) PLATENSIS (CYANOPHYTA): FROM LINEAR TO HELICAL1
Wang, Z., & Zhao, Y. (2005) · Journal of Phycology · doi:10.1111/j.1529-8817.2005.00087.x · Cited by 64 · see citing works
Model-free learning control of neutralization processes using reinforcement learning
Syafiie, S., Tadeo, F., & Martinez, E. C. (2007) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2006.10.009 · Cited by 60 · see citing works
Vu, M. T. T., Douëtte, C., Rayner, T. A., Thoisen, C., Nielsen, S. L., & Hansen, B. W. (2016) · Journal of Applied Phycology · doi:10.1007/s10811-015-0722-2 · Cited by 59 · see citing works
Walter, M., Recknagel, F., Carpenter, C., et al. (2001) · Ecological Modelling · doi:10.1016/s0304-3800(01)00299-x · Cited by 54 · see citing works
Prates, D. D. F., Duarte, J. H., Vendruscolo, R. G., Wagner, R. C., Ballus, C. A., Oliveira, W. D. S., Godoy, H. T., Barcia, M. T., de Morais, M. G., Radmann, E. M., et al. (2020) · Bioresource Technology · doi:10.1016/j.biortech.2020.123184 · Cited by 49 · see citing works
Reinforcement Learning Control for Water-Efficient Agricultural Irrigation
Sun, L., Yang, Y., Hu, J., Porter, D., Marek, T. H., & Hillyer, C. C. (2017) · IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA) · doi:10.1109/ispa/iucc.2017.00203 · Cited by 47 · see citing works
A Solution to Time-Varying Markov Decision Processes
Liu, L., & Sukhatme, G. S. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2801479 · Cited by 43 · see citing works
Two-stage cultivation of a Nannochloropsis mutant for biodiesel feedstock
Doan, Y. T.-T., & Obbard, J. P. (2014) · Journal of Applied Phycology · doi:10.1007/s10811-014-0490-4 · Cited by 31 · see citing works
Self-citationJuneja, A., & Murthy, G. S. (2018) · Bioresource Technology · doi:10.1016/j.biortech.2017.11.047 · Cited by 29 · see citing works
White, P. L. (1978) · American Journal of Agricultural Economics · doi:10.2307/1240094 · Cited by 26 · see citing works
Wang, Y., Álamo, T., Puig, V., et al. (2018) · Energies · doi:10.3390/en11040991 · Cited by 23 · see citing works
Water and Energy-Efficient Irrigation based on Markov Decision Model for Precision Agriculture
Hương, T. T., Thanh, N. H., Vân, N. T. T., et al. (2018) · IEEE International Conference on Communications and Electronics (ICCE) · doi:10.1109/cce.2018.8465723 · Cited by 23 · see citing works
Chung, I., Kim, S., Lee, J., et al. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5773 · Cited by 15 · see citing works
Talib, A., Recknagel, F., Cao, H., et al. (2008) · Mathematics and Computers in Simulation · doi:10.1016/j.matcom.2008.01.037 · Cited by 14 · see citing works
Bousquet, O., von Luxburg, U., & Rätsch, G. (2004) · Springer eBooks · Cited by 9 · see citing works
A hybrid Gaussian process approach to robust economic model predictive control
Rostam, M., Nagamune, R., & Grebenyuk, V. (2020) · Journal of Process Control · doi:10.1016/j.jprocont.2020.06.006 · Cited by 6 · see citing works
Reinforcement Learning Based Controller For Precision Irrigation
Irukula, S. (2015) · OakTrust (Texas A&M University Libraries) · Cited by 4 · see citing works
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Sheik, A. G., Kumar, A., Ansari, F. A., Raj, V., Peleato, N. M., Patan, A. K., Kumari, S., & Bux, F. (2024) · Algal Research · doi:10.1016/j.algal.2024.103779 · Cited by 37 · see citing works
Zhao, N., Chu, Y., Huang, Q., Qin, S., Cui, H., & Zhang, W. (2026) · Algal Research · doi:10.1016/j.algal.2026.104629 · Cited by 2 · see citing works
SPIRULINA (ARTHROSPIRA PLATENSIS): AN ALTERNATIVE SOURCE OF NUTRIENTS - A REVIEW
Motta, D. N., Malosso, M. G., Barbosa, E. P., Medeiros, E. D. M., & Carvalho, R. P. (2025) · International Seven Journal of Health Research · doi:10.56238/isevjhv4n5-005 · Cited by 1 · see citing works
- Authors
- Doan, Y. T.-T., Ho, M.-T., Nguyen, H.-K., & Han, H.-D.
- Affiliations
- Trường Đại học Khoa học và Công nghệ Hà Nội (VN)
- Corresponding author
- Doan, Yen Thi-Thai
- Han, Huy-Dung
- Venue
- Journal of Applied Phycology
- Published
- 2021-06-12
- Volume
- 33
- Issue
- 5
- Pages
- 2733-2744
- ISSN
- 0921-8971 (ISSN-L)
- 1573-5176
- OpenAlex
- W3166646252
- Cited by
- 20
- References
- 52
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 4 · 2023: 4 · 2022: 2 · 2021: 1
- FWCI
- 0.52
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of Applied Phycology (publishedVersion, cc-by)
- Funders
- Hanoi University of Science and Technology (T2017-LN-01)
- Topics
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Algal biology and biofuel production
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Life Sciences › Agricultural and Biological Sciences › Aquatic Science › Innovations in Aquaponics and Hydroponics Systems
- Keywords
- Spirulina, Arthrospira, cyanobacterial cultivation, cultivation condition optimization, reinforcement learning, dry weight yield, biomass productivity, time-dependent control, microalgae cultivation
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