Simulation-based Digital Twins of Industrial Process Plants: A Semi-Automatic Implementation Approach
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School of Electrical Engineering |
Doctoral thesis (article-based)
| Defence date: 2019-06-07
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Authors
Date
2019
Major/Subject
Mcode
Degree programme
Language
en
Pages
83 + app. 89
Series
Aalto University publication series DOCTORAL DISSERTATIONS, 83/2019
Abstract
Dynamic simulation has been used in the process industry during decades for several important applications over the process plant lifecycle. Recent trends on plant digitalization have resulted on the development of Simulation-based Digital Twins (SBDT) of process plants. In a SBDT, a dynamic first-principles simulation model is used to capture the process plant dynamics. In this application, the first-principles model (FPM) of the plant is run in parallel with the process while dynamic model parameter estimation methods adjust the model results by comparing process measurements with model results to continuously drive the simulated state to the current plant state. As a result, the underlying FPM of the SBDT is continuously synchronized with the operational plant. SBDTs can provide non-measured information of the process and they can be used to obtain high-fidelity predictions that are based on the current state of the process. They can also be utilized to develop operator training simulators, trouble-shooting and fault diagnoses systems. Furthermore, they can be applied for offline and online optimization of the plant. SBDTs are a holistic application for operation support of process plants. However, development of their underlying FPM remains laborious and expensive. Although re-utilization of existing models, developed for plant engineering, could reduce implementation effort and time of SBDTs, these models are still created manually. Moreover, integration of SBDTs with the ICT architecture of the plant could leverage on existing industrial operability standard to seamlessly interface different simulation methods and other SBDT system components with the plant. In this thesis, these implementation shortcomings are tackled by utilizing a combination of implementation methods proposed in this work. First, laborious FPMs development is addressed by applying an AMG method based on deriving 3D plant model information for automatically generating the FPM of the SBDT. Furthermore, laborious integration between the simulation system and the process plant is addressed by utilizing a method for implementing a lifecycle-wide tracking simulation architecture.The main results of the thesis show that the model generated using the proposed AMG approach can be successfully applied for implementing SBDTs after its integration into the physical plant. Furthermore, the proposed simulation architecture leverages on the application of industrial interoperability standards for reducing the effort required for configuring the communication between different architecture components and for enabling systematic information exchange between the architecture components and methods.Description
Supervising professor
Vyatkin, Valeriy, Prof., Aalto University, Department of Electrical Engineering and Automation, FinlandThesis advisor
Karhela, Tommi, Dr., Aalto University, Department of Electrical Engineering and Automation, FinlandSierla, Seppo, Dr., Aalto University, Department of Electrical Engineering and Automation, Finland
Keywords
digital twin, dynamic simulation, engineering automation, industrial process simulation
Other note
Parts
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[Publication 1]: G. Santillán Martínez; T. Karhela; T. Miettinen; V. Vyatkin. An OPC UA Based Architecture for Testing Tracking Simulation Methods. In The 13th IEEE International Symposium on Parallel and Distributed Processing with Applications, Helsinki, 2015, pp. 275-280.
DOI: 10.1109/Trustcom.2015.644, August 2015 View at publisher
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[Publication 2]: G. Santillán Martínez; T. Karhela; A. Rossi; C. Pang; V. Vyatkin. A Hybrid Approach for the Initialization of Tracking Simulation Systems. In IEEE 20th Conference on Emerging Technologies and Factory Automation (ETFA), Luxembourg, 2015, pp. 1-8.
DOI: 10.1109/ETFA.2015.7301532 View at publisher
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[Publication 3]: G. Santillán Martínez; A. Aikala; T. Miettinen; J. Savolainen; K. Kondelin; T. Karhela; V. Vyatkin. Parameters Selection in Predictive Online Simulation. In IEEE 14th International Conference on Industrial Informatics (INDIN), Poitiers, 2016, pp. 726-729.
DOI: 10.1109/INDIN.2016.7819254 View at publisher
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[Publication 4]: R. Ruusu; G. Santillán Martínez; T. Karhela; V. Vyatkin. Sliding Mode SISO Control of Model Parameters for Implicit Dynamic Feedback Estimation of Industrial Tracking Simulation Systems. In IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society, Beijing, 2017, pp. 6927-6932.
DOI: 10.1109/IECON.2017.8217211 View at publisher
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[Publication 5]: G. Santillán Martínez; T. Karhela; R. Ruusu; T. Lackman; V. Vyatkin. Towards a Systematic Path for Dynamic Simulation to Plant Operation: OPC UA-enabled Model Adaptation Method for Tracking Simulation. In IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society, Beijing, 2017, pp. 5503-5508. Full Text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-201812106341.
DOI: 10.1109/IECON.2017.8216952 View at publisher
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[Publication 6]: G. Santillán Martínez; R. Ruusu; T. Karhela; S. Sierla; V. Vyatkin. An Integrated Implementation Methodology of a Lifecycle-Wide Tracking Simulation Architecture. Accepted for publication in IEEE Access, vol. 6, pp. 15391-15407, 2018.
DOI: 10.1109/ACCESS.2018.2811845 View at publisher
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[Publication 7]: G. Santillán Martínez; S. Sierla; T. Karhela; J. Lappalainen; V. Vyatkin. Automatic Generation of a High-Fidelity Dynamic Thermal-hydraulic Process Simulation Model from a 3D Plant Model. Accepted for publication in IEEE Access, 2018.
Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-201809064974DOI: 10.1109/ACCESS.2018.2865206 View at publisher
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[Publication 8]: G. Santillán Martínez; S. Sierla; T. Karhela; V. Vyatkin. Automatic Generation of a Simulation-based Digital Twin. In IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, Washington, 2018, October 2018.
Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-201901301491DOI: 10.1109/IECON.2018.8591464 View at publisher