(73c) Development of a Model Reduction Approach for a Pressure-Driven Column Model Using Digital Twin Technology | AIChE

(73c) Development of a Model Reduction Approach for a Pressure-Driven Column Model Using Digital Twin Technology


Kender, R. - Presenter, Technical University of Munich
Stops, L., Technical University of Munich
Wunderlich, B., Linde Aktiengesellschaft, Linde Engineering
Pottmann, M., Linde Engineering
Ecker, A. M., Linde Aktiengesellschaft, Linde Engineering
Rehfeldt, S., Technical University of Munich
Klein, H., Technical University of Munich
In recent years, an increase in the volatility of the German energy market was apparent caused by the rising share of renewable energies in the power supply. Thus, major energy consumers such as cryogenic air separation units (ASUs) need to adapt their operations to stabilize the power grid. The high potential of flexible ASU operation is investigated in the Kopernikus project “SynErgie - FlexASU” funded by the German Federal Ministry of Education and Research (BMBF).

A major aspect of efficient and flexible plant operation is the development of advanced control strategies. Kender et al. 2021 [1] introduced a digital twin of an ASU which contains a pressure-driven detailed dynamic plant model, the virtual ASU, as a core component.

The digital twin allows for the in-silico development of novel control strategies considering the whole operating range of the plant. However, the dynamic plant simulation requires a high computational effort and is therefore not suitable for real-time applications. To address this issue, the adapted edmister model (AEM) presented by Ecker et al. 2019 [2] is used as a basis to develop a model reduction approach for a pressure-driven column model. The resulting dynamic edmister model (DEM) fulfills the high requirements regarding robustness and simulation accuracy while reducing the computational time. In addition, it can be applied for arbitrary column configurations and is incorporated inherently into the virtual ASU to reduce its model granularity utilizing it for applications such as dynamic optimization and nonlinear model predictive control.

[1] Kender R., Kaufmann F., Rößler F., Wunderlich B., Golubev D., Thomas I., Ecker A. M., Rehfeldt S., Klein H.: Development of a Digital Twin for a Flexible Air Separation Unit Using a Pressure-Driven Simulation Approach. Computers & Chemical Engineering, 151, 107349, 2021

[2] Ecker A. M., Thomas I., Häfele M., Wunderlich B., Obermeier A., Ferstl J., Klein H., Peschel A.: Development of a new column shortcut model and its application in process optimisation. Chemical Engineering Science, 196, 538-551, 2019