Hybrid Digital Twin for process industry using Apros simulation environment

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

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

7 Sitaatiot (Scopus)
141 Lataukset (Pure)

Abstrakti

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.
AlkuperäiskieliEnglanti
OtsikkoProceedings - 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2021
KustantajaIEEE
Sivut1-4
Sivumäärä4
ISBN (elektroninen)978-1-7281-2989-1
ISBN (painettu)978-1-7281-2990-7
DOI - pysyväislinkit
TilaJulkaistu - 30 marrask. 2021
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE International Conference on Emerging Technologies and Factory Automation - Västerås, Ruotsi
Kesto: 7 syysk. 202110 syysk. 2021
Konferenssinumero: 26

Conference

ConferenceIEEE International Conference on Emerging Technologies and Factory Automation
LyhennettäETFA
Maa/AlueRuotsi
KaupunkiVästerås
Ajanjakso07/09/202110/09/2021

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