| Time: | December 6, 2023, 2:00 p.m. (CET) |
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The Special Interest Group Data Infrastructure offers a forum to interested working groups that want to set up or further develop an RDM infrastructure at working group or institute level. We invite you to a monthly SIGDIUS seminar, to which we invite internal and external experts for presentations and discussions. SIGDIUS members will have the opportunity to exchange their experiences with concrete RDM infrastructures.
We cordially invite all interested parties to our next meeting on 6 December 2023 at 2 pm. This seminar will be held as an online seminar. For participation, please send an e-mail to Juergen.Pleiss@itb.uni-stuttgart.de.
Olaf Deutschmann
KIT
Digital twins of catalytic reactors: from archiving data to reactor simulations
Microkinetic modeling is a widely used tool in the domain of heterogeneous catalysis and reaction engineering to gain valuable insights about the fundamental surface kinetics, crucial to designing improved catalysts and chemical and electrochemical processes. Nevertheless, the development of a microkinetic model is a multi-step process that demands expertise, substantial computational resources, and extensive time and effort. In light of these challenges, automation within catalysis research is becoming increasingly important to allow exploration of a broader range of catalytic systems in a shorter timeframe. To this extent, a variety of digital tools and software have been developed to accelerate the development of microkinetic and reactor models. This seminar aims to highlight a selection of these tools that address the various challenges confronting the researchers in this field. These tools address diverse aspects, from the efficient storage of research data that allows easy retrieval and reuse, to the establishment of automated workflows that harness state-of-the-art numerical solvers and algorithms that reduce manual effort.
Refs.:
H. Gossler, J. Riedel, E. Daymo, R. Chacko, S. Angeli, , O. Deutschmann. Chemie Ingenieur Technik
94 (2022) 1798
H. Gossler, L. Maier, S. Angeli, S. Tischer, O. Deutschmann. PhysChemChemPhys 20 (2018) 10857;
Catalysts 9 (2019) 227;
Richard Schömig
(SFB1333)
Research Data for Miniplants
The FAIR data principles stipulate that research data must be handled such that it is findable, accessible, interoperable, and reusable. In many chemical research fields, their implementation is pretty much straightforward, however, there are branches where much more effort is needed to achieve this. One of these exceptions is given in technical chemistry: where in the past pilot plant were used to bring a laboratory scale experiment to industrial maturity, more recent approaches rely on so called miniplants, at maximum miniaturized versions of industrial process plants, for this purpose. They are compact, provide more flexibility and are much more affordable. However, ensuring “F.A.I.R.ness” in this field demands for a strong cohesion between the plant setup and the experimental results obtained by their integrated analysis and monitoring devices. Furthermore, miniplants are often designed as individual prototypes that contain customized components, and the analysis of the experimental results is willingly done by badly documented scripts or excel spreadsheets. Lastly, different output formats of data often need to be manipulated or converted manually to be readable by the desired program, which can be time-consuming and erroneous. In my upcoming presentation I will present ways to overcome these obstacles by showcasing our recent work that was done in collaboration within the SFB1333: A workflow platform based on a Jupyter notebook was established that utilizes a format independent data model as a hub and interface to facilitate data parsing, data analysis and data storage. Transfer of already existing data exchange formats is exploited to create a digital twin of the plant that is linked to the data. Outsourcing of python modules and interactive widgets in the notebook provide a user-friendly interface, while still allowing for individual adjustments, even for users without strong coding background.
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