Forecasting Algae Growth in Photo-Bioreactors Using Attention LSTMs

Boiar, D; Killich, N; Schulte, L; Hernandez Moreno, V; Deuse, J; Liebig, T

Forecasting Algae Growth in Photo-Bioreactors Using Attention LSTMs

Boiar, D Killich, N Schulte, L Hernandez Moreno, V

Deuse, J

Liebig, T

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Publisher:: Springer Nature
Publication Type:: Chapter
Citation:: Software Engineering and Formal Methods. SEFM 2022 Collocated Workshops, 2023, 13765 LNCS, pp. 26-37
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Boiar, D
dc.contributor.author	Killich, N
dc.contributor.author	Schulte, L
dc.contributor.author	Hernandez Moreno, V https://orcid.org/0000-0002-6038-6959
dc.contributor.author	Deuse, J https://orcid.org/0000-0003-4066-4357
dc.contributor.author	Liebig, T
dc.date.accessioned	2023-11-06T00:49:27Z
dc.date.available	2023-11-06T00:49:27Z
dc.date.issued	2023-01-01
dc.identifier.citation	Software Engineering and Formal Methods. SEFM 2022 Collocated Workshops, 2023, 13765 LNCS, pp. 26-37
dc.identifier.isbn	9783031262357
dc.identifier.uri	http://hdl.handle.net/10453/173021
dc.description.abstract	Sustainability is the current global challenge. This is reflected in the demand for healthy food and CO2 neutrality. These challenges can be met with the industrial cultivation of algae: Algae can be used as food supplements, nutraceuticals, pharmaceuticals, fuel, CO2 sinks, and obtain high relative yield density per area. Current limitations in their large-scale use exists, as scaling up from laboratory environments to pilot applications typically requires more than 5 years, because of highly complex interactions in the growth behavior: They are influenced by current and past environmental conditions. These interactions make current pilot applications inefficient due to insufficient control and monitoring techniques. This limitation can be countered: By using modern communication and evaluation technologies, a “smart” bioreactor can be developed, which evaluates algae growth in real-time, performs process adaptations and thus significantly accelerates algae growth and scale-up. Therefore, an algae bioreactor was established at the University of Technology Sydney. The subject of this paper is the study of algae growth using Long Short-Term Memory Neural Networks (LSTMs). In order to learn the behavior of algae in the shortest possible series of experiments, repetitive change intervals were run by systematically varying the environmental parameters. LSTMs were trained to model algae growth. Attention mechanism is used on variable and temporal direction for importance. The LSTM is compared to a Transformer and an ARIMA. Based on the trained models, the behavior of algae growth is interpreted.
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	Software Engineering and Formal Methods. SEFM 2022 Collocated Workshops
dc.relation.ispartofseries	Lecture Notes in Computer Science
dc.relation.isbasedon	10.1007/978-3-031-26236-4_3
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	46 Information and computing sciences
dc.title	Forecasting Algae Growth in Photo-Bioreactors Using Attention LSTMs
dc.type	Chapter
utslib.citation.volume	13765 LNCS
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAM - Centre for Advanced Manufacturing
utslib.copyright.status	closed_access	*
dc.date.updated	2023-11-06T00:49:23Z
pubs.publication-status	Published
pubs.volume	13765 LNCS

Abstract:

Sustainability is the current global challenge. This is reflected in the demand for healthy food and CO2 neutrality. These challenges can be met with the industrial cultivation of algae: Algae can be used as food supplements, nutraceuticals, pharmaceuticals, fuel, CO2 sinks, and obtain high relative yield density per area. Current limitations in their large-scale use exists, as scaling up from laboratory environments to pilot applications typically requires more than 5 years, because of highly complex interactions in the growth behavior: They are influenced by current and past environmental conditions. These interactions make current pilot applications inefficient due to insufficient control and monitoring techniques. This limitation can be countered: By using modern communication and evaluation technologies, a “smart” bioreactor can be developed, which evaluates algae growth in real-time, performs process adaptations and thus significantly accelerates algae growth and scale-up. Therefore, an algae bioreactor was established at the University of Technology Sydney. The subject of this paper is the study of algae growth using Long Short-Term Memory Neural Networks (LSTMs). In order to learn the behavior of algae in the shortest possible series of experiments, repetitive change intervals were run by systematically varying the environmental parameters. LSTMs were trained to model algae growth. Attention mechanism is used on variable and temporal direction for importance. The LSTM is compared to a Transformer and an ARIMA. Based on the trained models, the behavior of algae growth is interpreted.

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http://hdl.handle.net/10453/173021