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

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

6 Citations (Scopus)
118 Downloads (Pure)

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 languageEnglish
Title of host publicationProceedings - 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2021
PublisherIEEE
Pages1-4
Number of pages4
ISBN (Electronic)978-1-7281-2989-1
ISBN (Print)978-1-7281-2990-7
DOIs
Publication statusPublished - 30 Nov 2021
MoE publication typeA4 Conference publication
EventIEEE International Conference on Emerging Technologies and Factory Automation - Västerås, Sweden
Duration: 7 Sept 202110 Sept 2021
Conference number: 26

Conference

ConferenceIEEE International Conference on Emerging Technologies and Factory Automation
Abbreviated titleETFA
Country/TerritorySweden
CityVästerås
Period07/09/202110/09/2021

Keywords

  • Industries
  • Uncertainty
  • Digital twin
  • Prototypes
  • Machine learning
  • Hybrid power systems
  • Data models

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