Comparison of a physical and a data-driven model of a Packed Bed Regenerator for industrial applications

Hofmann, René; Halmschlager, Verena; Koller, Martin; Scharinger-Urschitz, Georg; Birkelbach, Felix; Walter, Heimo

doi:10.1016/j.est.2019.04.015

Cited by 10 publications

(9 citation statements)

References 27 publications

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“…To assess the thermodynamic conditions in the test rig's storage vessel, it is equipped with a total of 18 calibrated thermocouples, as well as mass flow and pressure measurement sensors at the inlet and outlet. For a detailed description of the test rig and it's measurement instrumentation, please refer to [35,36]. Various models were developed to simulate the thermodynamic behavior of the PBTES, i.e., the measured values for intrinsic and outlet temperatures given the input values for temperature and mass flow.…”

Section: Packed-bed Thermal Energy Storage Test Rigmentioning

confidence: 99%

“…A grey box model using recurrent neural networks was published in [38]. Furthermore, physical and data-driven modeling approaches for PBTES were compared and evaluated regarding prediction accuracy and modeling, as well as the computational effort [35]. In general, each of these evaluated simulation models can be reused in the Simulation Service of the DT.…”

Section: Packed-bed Thermal Energy Storage Test Rigmentioning

confidence: 99%

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Generic Digital Twin Architecture for Industrial Energy Systems

et al. 2020

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Digital Twins have been in the focus of research in recent years, trying to achieve the vision of Industry 4.0. In the domain of industrial energy systems, they are applied to facilitate a flexible and optimized operation. With the help of Digital Twins, the industry can participate even stronger in the ongoing renewable energy transition. Current Digital Twin implementations are often application-specific solutions without general architectural concepts and their structures and namings differ, although the basic concepts are quite similar. For this reason, we analyzed concepts, architectures, and frameworks for Digital Twins in the literature to develop a technology-independent Generic Digital Twin Architecture (GDTA), which is aligned with the information technology layers of the Reference Architecture Model Industry 4.0 (RAMI4.0). This alignment facilitates a common naming and understanding of the proposed architectural structure. A proof-of-concept shows the application of Semantic Web technologies for instantiating the proposed GDTA for a use case of a Packed-Bed Thermal Energy Storage (PBTES).

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Section: Packed-bed Thermal Energy Storage Test Rigmentioning

confidence: 99%

Section: Packed-bed Thermal Energy Storage Test Rigmentioning

confidence: 99%

Generic Digital Twin Architecture for Industrial Energy Systems

et al. 2020

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“…As a use-case, the approach was applied to an industrial evaporation plant. Finally, in the authors' previous works [17,18], a sensible thermal energy storage, a packed-bed-regenerator (PBR), was modeled using Neural Networks and physical considerations. Although the Neural Network models showed good performance and high accuracy, their robustness/reliability was limited due to their mainly data-driven nature.…”

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confidence: 99%

“…This way, a physically based model is built, while using far fewer equations than a traditional whitebox model. Compared to the authors' previously published mainly data-driven and solely physical models of the PBR [17,18], the proposed mechanistic grey-box modeling approach is preliminary based on physical knowledge and uses data for refinement.…”

mentioning

confidence: 99%

“…Thus, as the main research goal, this work presents a novel, robust and efficient modeling method for dynamic industrial systems and in-depth investigates the mechanistic greybox modeling approach. Additionally, the presented grey-box model is a major addition to the authors' previous publication [18], where the PBR was modeled with a primarily data-driven modeling approach using Neural Networks and also with a purely physical modeling approach. The new mechanistic grey-box model can be seen as an in-between approach of the previously used methods, using advantages of both physical and datadriven modeling.…”

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Mechanistic Grey-Box Modeling of a Packed-Bed Regenerator for Industrial Applications

2021

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Thermal energy storage is essential to compensate for energy peaks and troughs of renewable energy sources. However, to implement this storage in new or existing industries, robust and accurate component models are required. This work examines the development of a mechanistic grey-box model for a sensible thermal energy storage, a packed-bed regenerator. The mechanistic grey-box model consists of physical relations/equations and uses experimental data to optimize specific parameters of these equations. Using this approach, a basic model and two models with extensions I and II, which vary in their number from Equations (3) to (5) and parameters (3 to 6) to be fitted, are proposed. The three models’ results are analyzed and compared to existing models of the regenerator, a data-driven and a purely physical model. The results show that all developed grey-box models can extrapolate and approximate the physical behavior of the regenerator well. In particular, the extended model II shows excellent performance. While the existing data-driven model lacks robustness and the purely physical model lacks accuracy, the hybrid grey-box models do not show significant disadvantages. Compared to the data-driven and physical model, the grey-box models especially stands out due to their high accuracy, low computational effort, and high robustness.

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