Digital Twin Framework for Energy Efficient Greenhouse Industry 4.0

Daniel Anthony Howard; Zheng Ma; Bo Nørregaard Jørgensen

doi:10.1007/978-3-030-58356-9_34

Digital Twin Framework for Energy Efficient Greenhouse Industry 4.0

Daniel Anthony Howard, Zheng Ma, Bo Nørregaard Jørgensen

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

273 Downloads (Pure)

Abstract

This paper introduces the ongoing research conducted on enabling industrial greenhouse growers to optimize production using multi-agent systems and digital twin technology. The project seeks to develop a production process framework for greenhouses, based on several case studies, that can be applied to different greenhouse facilities to enable a broad implementation in the industrial horticulture sector. The research will incorporate AI technology to support the production process agent in forecasting and learning optimal operating conditions within set parameters that will be feedback to the grower through a common information model. Furthermore, the production agent will communicate with other process agents to co-optimize the essential aspects of production. In turn, this allows the growers to optimize the production cost with minimal risk to product quality while aiding in upholding grid stability. The findings in this research project may be beneficial for developing industry-specific energy flexibility solutions incorporating product and process constraints.

Original language	English
Title of host publication	Ambient Intelligence – Software and Applications : 11th International Symposium on Ambient Intelligence
Editors	Paulo Novais, Gianni Vercelli, Josep L. Larriba-Pey, Francisco Herrera, Pablo Chamoso
Publisher	Springer
Publication date	2021
Pages	293-297
ISBN (Print)	978-3-030-58355-2
ISBN (Electronic)	978-3-030-58356-9
DOIs	https://doi.org/10.1007/978-3-030-58356-9_34
Publication status	Published - 2021

Series	Advances in Intelligent Systems and Computing
Volume	1239
ISSN	2194-5357

Keywords

Digital twin
Greenhouse
Industry 4.0
Multi-agent system

Documents & Links

10.1007/978-3-030-58356-9_34

Open Access VersionAccepted manuscript, 302 KB

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1 Ph.D. thesis

Generic Digital Twin Development Framework for Enhancing Energy Efficiency and Flexibility in Industrial Production Processes
Howard, D. A., 16. Oct 2023, Syddansk Universitet. Det Tekniske Fakultet. 274 p.
Research output: Thesis › Ph.D. thesis

Open Access
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393 Downloads (Pure)

1 Finished

Digital Twin Framework for Energy Efficient Greenhouse Industry 4.0
Howard, D. A. (Project participant)
01/09/2019 → 31/08/2023
Project: PhD Project

Cite this

Howard, D. A., Ma, Z., & Jørgensen, B. N. (2021). Digital Twin Framework for Energy Efficient Greenhouse Industry 4.0. In P. Novais, G. Vercelli, J. L. Larriba-Pey, F. Herrera, & P. Chamoso (Eds.), Ambient Intelligence – Software and Applications: 11th International Symposium on Ambient Intelligence (pp. 293-297). Springer. https://doi.org/10.1007/978-3-030-58356-9_34

Howard, Daniel Anthony ; Ma, Zheng ; Jørgensen, Bo Nørregaard. / Digital Twin Framework for Energy Efficient Greenhouse Industry 4.0. Ambient Intelligence – Software and Applications: 11th International Symposium on Ambient Intelligence. editor / Paulo Novais ; Gianni Vercelli ; Josep L. Larriba-Pey ; Francisco Herrera ; Pablo Chamoso. Springer, 2021. pp. 293-297 (Advances in Intelligent Systems and Computing, Vol. 1239).

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title = "Digital Twin Framework for Energy Efficient Greenhouse Industry 4.0",

abstract = "This paper introduces the ongoing research conducted on enabling industrial greenhouse growers to optimize production using multi-agent systems and digital twin technology. The project seeks to develop a production process framework for greenhouses, based on several case studies, that can be applied to different greenhouse facilities to enable a broad implementation in the industrial horticulture sector. The research will incorporate AI technology to support the production process agent in forecasting and learning optimal operating conditions within set parameters that will be feedback to the grower through a common information model. Furthermore, the production agent will communicate with other process agents to co-optimize the essential aspects of production. In turn, this allows the growers to optimize the production cost with minimal risk to product quality while aiding in upholding grid stability. The findings in this research project may be beneficial for developing industry-specific energy flexibility solutions incorporating product and process constraints.",

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author = "Howard, {Daniel Anthony} and Zheng Ma and J{\o}rgensen, {Bo N{\o}rregaard}",

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}

Howard, DA , Ma, Z & Jørgensen, BN 2021, Digital Twin Framework for Energy Efficient Greenhouse Industry 4.0. in P Novais, G Vercelli, JL Larriba-Pey, F Herrera & P Chamoso (eds), Ambient Intelligence – Software and Applications: 11th International Symposium on Ambient Intelligence. Springer, Advances in Intelligent Systems and Computing, vol. 1239, pp. 293-297. https://doi.org/10.1007/978-3-030-58356-9_34

Digital Twin Framework for Energy Efficient Greenhouse Industry 4.0. / Howard, Daniel Anthony ; Ma, Zheng ; Jørgensen, Bo Nørregaard.
Ambient Intelligence – Software and Applications: 11th International Symposium on Ambient Intelligence. ed. / Paulo Novais; Gianni Vercelli; Josep L. Larriba-Pey; Francisco Herrera; Pablo Chamoso. Springer, 2021. p. 293-297 (Advances in Intelligent Systems and Computing, Vol. 1239).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

TY - GEN

T1 - Digital Twin Framework for Energy Efficient Greenhouse Industry 4.0

AU - Howard, Daniel Anthony

AU - Ma, Zheng

AU - Jørgensen, Bo Nørregaard

PY - 2021

Y1 - 2021

N2 - This paper introduces the ongoing research conducted on enabling industrial greenhouse growers to optimize production using multi-agent systems and digital twin technology. The project seeks to develop a production process framework for greenhouses, based on several case studies, that can be applied to different greenhouse facilities to enable a broad implementation in the industrial horticulture sector. The research will incorporate AI technology to support the production process agent in forecasting and learning optimal operating conditions within set parameters that will be feedback to the grower through a common information model. Furthermore, the production agent will communicate with other process agents to co-optimize the essential aspects of production. In turn, this allows the growers to optimize the production cost with minimal risk to product quality while aiding in upholding grid stability. The findings in this research project may be beneficial for developing industry-specific energy flexibility solutions incorporating product and process constraints.

AB - This paper introduces the ongoing research conducted on enabling industrial greenhouse growers to optimize production using multi-agent systems and digital twin technology. The project seeks to develop a production process framework for greenhouses, based on several case studies, that can be applied to different greenhouse facilities to enable a broad implementation in the industrial horticulture sector. The research will incorporate AI technology to support the production process agent in forecasting and learning optimal operating conditions within set parameters that will be feedback to the grower through a common information model. Furthermore, the production agent will communicate with other process agents to co-optimize the essential aspects of production. In turn, this allows the growers to optimize the production cost with minimal risk to product quality while aiding in upholding grid stability. The findings in this research project may be beneficial for developing industry-specific energy flexibility solutions incorporating product and process constraints.

KW - Digital twin

KW - Greenhouse

KW - Industry 4.0

KW - Multi-agent system

U2 - 10.1007/978-3-030-58356-9_34

DO - 10.1007/978-3-030-58356-9_34

M3 - Article in proceedings

SN - 978-3-030-58355-2

T3 - Advances in Intelligent Systems and Computing

SP - 293

EP - 297

BT - Ambient Intelligence – Software and Applications

A2 - Novais, Paulo

A2 - Vercelli, Gianni

A2 - Larriba-Pey, Josep L.

A2 - Herrera, Francisco

A2 - Chamoso, Pablo

PB - Springer

ER -

Howard DA , Ma Z , Jørgensen BN. Digital Twin Framework for Energy Efficient Greenhouse Industry 4.0. In Novais P, Vercelli G, Larriba-Pey JL, Herrera F, Chamoso P, editors, Ambient Intelligence – Software and Applications: 11th International Symposium on Ambient Intelligence. Springer. 2021. p. 293-297. (Advances in Intelligent Systems and Computing, Vol. 1239). Epub 2020 Sept 10. doi: 10.1007/978-3-030-58356-9_34

Digital Twin Framework for Energy Efficient Greenhouse Industry 4.0

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Keywords

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Related research output

Generic Digital Twin Development Framework for Enhancing Energy Efficiency and Flexibility in Industrial Production Processes

Related projects

Digital Twin Framework for Energy Efficient Greenhouse Industry 4.0

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