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

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

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 publication2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )
PublisherIEEE Institute of Electrical and Electronic Engineers
Number of pages4
ISBN (Electronic)978-1-7281-2989-1
DOIs
Publication statusPublished - 7 Sep 2021
MoE publication typeA4 Article in a conference publication
Event IEEE 26th International Conference on Emerging Technologies and Factory Automation, ETFA 2021 - Vasteras, Sweden
Duration: 7 Sep 202110 Sep 2021

Conference

Conference IEEE 26th International Conference on Emerging Technologies and Factory Automation, ETFA 2021
Period7/09/2110/09/21

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