Automated data transfer for digital twin applications : Two case studies
Molin, Hanna LU ; Wärff, Christoffer LU ; Lindblom, Erik LU ; Arnell, Magnus LU ; Carlsson, Bengt ; Mattsson, Per ; Bäckman, Jonas and Jeppsson, Ulf LU
(2024)
In Water Environment Research
96(7).
- Abstract
Digital twins have been gaining an immense interest in various fields over the last decade. Bringing conventional process simulation models into (near) real time are thought to provide valuable insights for operators, decision makers, and stakeholders in many industries. The objective of this paper is to describe two methods for implementing digital twins at water resource recovery facilities and highlight and discuss their differences and preferable use situations, with focus on the automated data transfer from the real process. Case 1 uses a tailor-made infrastructure for automated data transfer between the facility and the digital twin. Case 2 uses edge computing for rapid automated data transfer. The data transfer lag from process... (More)
Digital twins have been gaining an immense interest in various fields over the last decade. Bringing conventional process simulation models into (near) real time are thought to provide valuable insights for operators, decision makers, and stakeholders in many industries. The objective of this paper is to describe two methods for implementing digital twins at water resource recovery facilities and highlight and discuss their differences and preferable use situations, with focus on the automated data transfer from the real process. Case 1 uses a tailor-made infrastructure for automated data transfer between the facility and the digital twin. Case 2 uses edge computing for rapid automated data transfer. The data transfer lag from process to digital twin is low compared to the simulation frequency in both systems. The presented digital twin objectives can be achieved using either of the presented methods. The method of Case 1 is better suited for automatic recalibration of model parameters, although workarounds exist for the method in Case 2. The method of Case 2 is well suited for objectives such as soft sensors due to its integration with the SCADA system and low latency. The objective of the digital twin, and the required latency of the system, should guide the choice of method. Practitioner Points: Various methods can be used for automated data transfer between the physical system and a digital twin. Delays in the data transfer differ depending on implementation method. The digital twin objective determines the required simulation frequency. Implementation method should be chosen based on the required simulation frequency.
(Less)
- author
- Molin, Hanna
LU
; Wärff, Christoffer
LU
; Lindblom, Erik
LU
; Arnell, Magnus
LU
; Carlsson, Bengt
; Mattsson, Per
; Bäckman, Jonas
and Jeppsson, Ulf
LU
- organization
- publishing date
- 2024-07
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- digital twins, edge computing, process modeling, real-time simulation, wastewater treatment, water resource recovery facility
- in
- Water Environment Research
- volume
- 96
- issue
- 7
- article number
- e11074
- publisher
- Water Environment Federation
- external identifiers
-
- scopus:85198645643
- pmid:39015947
- ISSN
- 1061-4303
- DOI
- 10.1002/wer.11074
- language
- English
- LU publication?
- yes
- id
- e5dc7e78-1eb6-488a-b8f6-98e6492f4216
- date added to LUP
- 2024-09-30 15:02:29
- date last changed
- 2025-01-21 03:42:09
@article{e5dc7e78-1eb6-488a-b8f6-98e6492f4216,
abstract = {{<p>Digital twins have been gaining an immense interest in various fields over the last decade. Bringing conventional process simulation models into (near) real time are thought to provide valuable insights for operators, decision makers, and stakeholders in many industries. The objective of this paper is to describe two methods for implementing digital twins at water resource recovery facilities and highlight and discuss their differences and preferable use situations, with focus on the automated data transfer from the real process. Case 1 uses a tailor-made infrastructure for automated data transfer between the facility and the digital twin. Case 2 uses edge computing for rapid automated data transfer. The data transfer lag from process to digital twin is low compared to the simulation frequency in both systems. The presented digital twin objectives can be achieved using either of the presented methods. The method of Case 1 is better suited for automatic recalibration of model parameters, although workarounds exist for the method in Case 2. The method of Case 2 is well suited for objectives such as soft sensors due to its integration with the SCADA system and low latency. The objective of the digital twin, and the required latency of the system, should guide the choice of method. Practitioner Points: Various methods can be used for automated data transfer between the physical system and a digital twin. Delays in the data transfer differ depending on implementation method. The digital twin objective determines the required simulation frequency. Implementation method should be chosen based on the required simulation frequency.</p>}},
author = {{Molin, Hanna and Wärff, Christoffer and Lindblom, Erik and Arnell, Magnus and Carlsson, Bengt and Mattsson, Per and Bäckman, Jonas and Jeppsson, Ulf}},
issn = {{1061-4303}},
keywords = {{digital twins; edge computing; process modeling; real-time simulation; wastewater treatment; water resource recovery facility}},
language = {{eng}},
number = {{7}},
publisher = {{Water Environment Federation}},
series = {{Water Environment Research}},
title = {{Automated data transfer for digital twin applications : Two case studies}},
url = {{http://dx.doi.org/10.1002/wer.11074}},
doi = {{10.1002/wer.11074}},
volume = {{96}},
year = {{2024}},
}