Artificial neural networks model for estimating growth of polyculture microalgae in an open raceway pond
Abstract
Microalgae have potential as biomass energy sources with higher photosynthetic efficiency compared to terrestrial plants. The use of polyculture systems such as native microalgae communities for microalgae cultivation has several advantages, as well as challenges due to indeterminate species composition and growth rate variation between species. This paper presents an artificial neural network (ANN) model to estimate the growth of polyculture microalgae in a semi-continuous open raceway pond (ORP). The model was comprised of a multilayer backpropagation neural network with eight input parameters, one hidden layer, and one output parameter. The model was developed using datasets collected from the cultivation of polyculture microalgae in Minamisoma City, Fukushima Prefecture, Japan. The input parameters are as follows: initial algal concentration, harvesting period (between two and three days after the growth have begun), hydraulic retention time, addition of sodium acetate, average solar radiation (μmole m−2 s−1), average temperature (oC), pH condition, and nitrate ion (NO3−) concentration. The output variable is the microalgae concentration observed during the cultivation period. The output is represented using a single neuron. The result of the study showed that the designed three-layer ANN achieved a high prediction accuracy (R2 = 0.93) for all combinations of inputs.
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Researchers are concerned about enormous power and storage consumption caused by ADT processes, particularly immense computations needed for DD models and ADT architecture with huge IoT networks.
“Researchers are concerned about the enormous power and storage consumption caused by ADT processes, particularly when it comes to the immense quantities of computations needed for DD models as well as the ADT architecture with its huge IoT networks [72,80].”
From Reinvigorating algal cultivation for biomass production with digital twin technology - a smart sustainable infrastructure (2024) · cites this paper as [80]
The three-layer ANN created attained a notably high level of accuracy in predicting microalgae concentration.
“The study ' s findings demonstrated that the three-layer ANN that was created attained a notably high level of accuracy in”
From Reinvigorating algal cultivation for biomass production with digital twin technology - a smart sustainable infrastructure (2024) · cites this paper as [80]
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- Authors
- Supriyanto, Noguchi, R., Ahamed, T., Rani, D. S., Sakurai, K., Nasution, M. A., Wibawa, D. S., Demura, M., & Watanabe, M. M.
- Affiliations
- University of Tsukuba (JP)
- Corresponding author
- Noguchi, Ryozo
- ORCID
- Venue
- Biosystems Engineering
- Publisher
- Elsevier BV
- Published
- 2019-01
- Volume
- 177
- Pages
- 122-129
- ISSN
- 1537-5110 (ISSN-L)
- OpenAlex
- W2896676120
- Cited by
- 88
- References
- 20
- Citations by year
- 2026: 12 · 2025: 10 · 2024: 20 · 2023: 13 · 2022: 12 · 2021: 9 · 2020: 9 · 2019: 2 · 2018: 1
- FWCI
- 1.21
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Biosystems Engineering (publishedVersion)
- Funders
- University of Tsukuba
- 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
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- artificial neural networks, raceway ponds, biomass energy, BP neural network, hydraulic retention time, solar radiation, nitrate content, harvest time, algal concentration
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