Hybrid Digital Twin for process industry using Apros simulation environment

Mohammad Azangoo; Joonas Salmi; Iivo Yrjola; Jonathan Bensky; Gerardo Santillan; Nikolaos Papakonstantinou; Seppo Sierla; Valeriy Vyatkin

doi:10.1109/ETFA45728.2021.9613416

Hybrid Digital Twin for process industry using Apros simulation environment

Mohammad Azangoo, Joonas Salmi, Iivo Yrjola, Jonathan Bensky, Gerardo Santillan, Nikolaos Papakonstantinou, Seppo Sierla, Valeriy Vyatkin

BA6403 Cybersecurity engineering & automation

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

9 Citations (Scopus)

Abstract

Making an updated and as-built model plays an important role in the life-cycle of a process plant. In particular, Digital Twin models must be precise to guarantee the efficiency and reliability of the systems. Data-driven models can simulate the latest behavior of the sub-systems by considering uncertainties and life-cycle related changes. This paper presents a step-by-step concept for hybrid Digital Twin models of process plants using an early implemented prototype as an example. It will detail the steps for updating the first-principles model and Digital Twin of a brownfield process system using data-driven models of the process equipment. The challenges for generation of an as-built hybrid Digital Twin will also be discussed. With the help of process history data to teach Machine Learning models, the implemented Digital Twin can be continually improved over time and this work in progress can be further optimized.

Original language	English
Title of host publication	Proceedings - 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2021
Publisher	IEEE Institute of Electrical and Electronic Engineers
Number of pages	4
ISBN (Electronic)	978-1-7281-2989-1
DOIs	https://doi.org/10.1109/ETFA45728.2021.9613416
Publication status	Published - 7 Sept 2021
MoE publication type	A4 Article in a conference publication
Event	IEEE 26th International Conference on Emerging Technologies and Factory Automation, ETFA 2021 - Vasteras, Sweden Duration: 7 Sept 2021 → 10 Sept 2021

Conference

Conference	IEEE 26th International Conference on Emerging Technologies and Factory Automation, ETFA 2021
Period	7/09/21 → 10/09/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/ETFA45728.2021.9613416

Cite this

Azangoo, M., Salmi, J., Yrjola, I., Bensky, J., Santillan, G., Papakonstantinou, N., Sierla, S., & Vyatkin, V. (2021). Hybrid Digital Twin for process industry using Apros simulation environment. In Proceedings - 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2021 IEEE Institute of Electrical and Electronic Engineers. https://doi.org/10.1109/ETFA45728.2021.9613416

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Azangoo, M, Salmi, J, Yrjola, I, Bensky, J, Santillan, G, Papakonstantinou, N, Sierla, S & Vyatkin, V 2021, Hybrid Digital Twin for process industry using Apros simulation environment. in Proceedings - 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2021. IEEE Institute of Electrical and Electronic Engineers, IEEE 26th International Conference on Emerging Technologies and Factory Automation, ETFA 2021, 7/09/21. https://doi.org/10.1109/ETFA45728.2021.9613416

Hybrid Digital Twin for process industry using Apros simulation environment. / Azangoo, Mohammad; Salmi, Joonas; Yrjola, Iivo et al.
Proceedings - 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2021. IEEE Institute of Electrical and Electronic Engineers, 2021.

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

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AU - Salmi, Joonas

AU - Yrjola, Iivo

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AU - Santillan, Gerardo

AU - Papakonstantinou, Nikolaos

AU - Sierla, Seppo

AU - Vyatkin, Valeriy

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AB - Making an updated and as-built model plays an important role in the life-cycle of a process plant. In particular, Digital Twin models must be precise to guarantee the efficiency and reliability of the systems. Data-driven models can simulate the latest behavior of the sub-systems by considering uncertainties and life-cycle related changes. This paper presents a step-by-step concept for hybrid Digital Twin models of process plants using an early implemented prototype as an example. It will detail the steps for updating the first-principles model and Digital Twin of a brownfield process system using data-driven models of the process equipment. The challenges for generation of an as-built hybrid Digital Twin will also be discussed. With the help of process history data to teach Machine Learning models, the implemented Digital Twin can be continually improved over time and this work in progress can be further optimized.

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Hybrid Digital Twin for process industry using Apros simulation environment

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