Real-time sensor data profile-based deep learning method applied to open raceway pond microalgal productivity prediction
Open accessAbstract
High Resolution Image Download MS PowerPoint Slide Microalgal biotechnology holds the potential for renewable biofuels, bioproducts, and carbon capture applications due to unparalleled photosynthetic efficiency and diversity. Outdoor open raceway pond (ORP) cultivation enables utilization of sunlight and atmospheric carbon dioxide to drive microalgal biomass synthesis for production of bioproducts including biofuels; however, environmental conditions are highly dynamic and fluctuate both diurnally and seasonally, making ORP productivity prediction challenging without time-intensive physical measurements and location-specific calibrations. Here, for the first time, we present an image-based deep learning method for the prediction of ORP productivity. Our method is based on parameter profile plot images of sensor parameters, including pH, dissolved oxygen, temperature, photosynthetically active radiation, and total dissolved solids. These parameters can be remotely monitored without physical interaction with ORPs. We apply the model to data we generated during the Unified Field Studies of the Algae Testbed Public-Private-Partnership (ATP 3 UFS), the largest publicly available ORP data set to date, which includes millions of sensor records and 598 productivities from 32 ORPs operated in 5 states in the United States. We demonstrate that this approach significantly outperforms an average value based traditional machine learning method ( R 2 = 0.77 ≫ R 2 = 0.39) without considering bioprocess parameters (e.g., biomass density, hydraulic retention time, and nutrient concentrations). We then evaluate the sensitivity of image and monitoring data resolutions and input parameter variations. Our results demonstrate ORP productivity can be effectively predicted from remote monitoring data, providing an inexpensive tool for microalgal production and operational forecasting.
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Algal digital twin has progressed and expanded in tandem with AI, IoT, and big data in the algal cultivation for biomass production sector to sustain the environment.
“Correspondingly, ADT has also progressed and expanded in tandem with AI, IoT, and BD in the ACB sector to sustain the environment [1,11 -14].”
From Reinvigorating algal cultivation for biomass production with digital twin technology - a smart sustainable infrastructure (2024) · cites this paper as [1]
The Methods (Data Set Description) describe more than one million sensor records from open raceway ponds, and the Results and Discussion (Implications of IBM Methodology) report a deep-learning method for predicting microalgal biomass productivity from monitoring data. These passages support the use of AI and large sensor datasets in algal cultivation, but they do not establish that algal digital twins have progressed alongside AI, IoT, and big data, or that this progression sustains the environment. No page numbers are available in our copy. (LLM, conf=0.83)
Experimental — accuracy not yet measuredThis methodology surpasses a conventional machine learning method based on average values (R2 = 0.77 >> R2 = 0.39) when bioprocess factors are considered.
“This study provides evidence that this methodology surpasses a conventional machine learning method based on average values (R2 = 0.77 ≫ R2 = 0.39) when bioprocess factors (such as biomass density, hydraulic”
From Reinvigorating algal cultivation for biomass production with digital twin technology - a smart sustainable infrastructure (2024) · cites this paper as [1]
The Results and Discussion section (‘Implications of IBM Methodology’) reports that the image-based model outperformed the average-value model on test-set R² (0.7668 versus 0.3883). But it says this was achieved without providing fundamental bioprocess parameters, including biomass density and hydraulic retention time, to the model—not when those factors were considered. (LLM, conf=0.89)
Experimental — accuracy not yet measuredIndustry 5.0 incorporates human, environmental, and social factors as key returns to the decision-making process.
“Industry 5.0 strives to incorporate human, environmental, and social factors as key returns to the decision-making process [1].”
From Reinvigorating algal cultivation for biomass production with digital twin technology - a smart sustainable infrastructure (2024) · cites this paper as [1]
The Results and Discussion section, under “Implications of IBM Methodology” (page unavailable), discusses microalgal productivity prediction and possible operational and environmental applications, not Industry 5.0. Searches for Industry 5.0 and for human, environmental, and social factors in decision-making found no passage addressing the attributed claim. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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- Authors
- Igou, T., Zhong, S., Reid, E., & Chen, Y.
- Affiliations
- Georgia Institute of Technology (US)
- East China Normal University (CN)
- Corresponding author
- Chen, Yongsheng
- Venue
- Environmental Science & Technology
- Publisher
- American Chemical Society (ACS)
- Published
- 2023-05-26
- Volume
- 57
- Issue
- 46
- Pages
- 17981-17989
- ISSN
- 0013-936X (ISSN-L)
- 1520-5851
- PMC
- PMC10666538
- OpenAlex
- W4378348444
- Cited by
- 31
- References
- 47
- Citations by year
- 2026: 11 · 2025: 12 · 2024: 7 · 2023: 1
- FWCI
- 1.48
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Environmental Science & Technology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- National Institute of Food and Agriculture (2018-68011-28371, 2020-67021-31526)
- Biomass Program (DE-EE0005996, DE-EE0008520)
- Division of Engineering Education and Centers (1936928)
- Division of Civil, Mechanical and Manufacturing Innovation (2112533)
- 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
- Life Sciences › Agricultural and Biological Sciences › Aquatic Science › Innovations in Aquaponics and Hydroponics Systems
- Keywords
- raceway ponds, deep learning image analysis, remote monitoring, microalgal biotechnology, biofuels, photosynthetically active radiation, dissolved oxygen, pH, total dissolved solids, operational forecasting
- MeSH terms
- Deep Learning, Sunlight, Biomass, Biofuels, Microalgae, Ponds
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