PA - Development of an Integrated Framework for Simulating Chemical Phenomena and Electrical Measurements in Reactors

Entwicklung eines integrierten System zur Simulation von chemischen Phänomenen und elektrischen Messungen in Reaktoren

Development of an Integrated Framework for Simulating Chemical Phenomena and Electrical Measurements in Reactors

General Setting

Electrical Impedance Tomography (EIT) is a non-invasive imaging technology based where a conductivity profile inside a medium is reconstructed based on voltage measurements on its boundary. At iMEK, within the context of the Collaborative Research Center CRC1615, a 3D EIT system to monitor chemical processes is being developed. In order to taylor the conductivity reconstruction to the arising phenomena electrical and chemical simulations should be coupled.

Task Description:

The project's aim is to create a computational framework that integrates the capabilities of OpenFOAM, MATLAB EIDORS, and Python to simulate and analyze chemical phenomena by evaluating conductivity variations in reactors using Electrical Impedance Tomography (EIT). This involves simulating the chemical processes, measuring conductivity changes, and utilizing machine learning for data analysis to boost predictions and insights in chemical engineering.

Key Tasks:

1. Literature Review and Phenomenon Selection

   • Conduct a comprehensive review of current techniques in chemical reactor simulations and machine learning in this context.

   • Select a specific chemical phenomenon characterized by conductivity changes.

2. Simulation in OpenFOAM

   • Develop an OpenFOAM simulation model focusing on the identified chemical phenomenon and conductivity changes.

3. Integration with EIDORS

   • Extract conductivity distribution data from OpenFOAM.

   • Implement the data in MATLAB EIDORS to simulate electrical conductivity behaviors.

4. Simulating Measurements in EIDORS

   • Simulate the EIT measurement process in EIDORS to produce and analyze synthetic data.

5. Data Storage Solution Implementation

   • Identify and implement a storage solution for efficient data management.

6. Data Extraction and Python Integration

   • Transfer EIDORS data into Python for processing.

7. Machine Learning Model Development

   • Develop and train a machine learning model in Python to analyze and classify the phenomenon based on conductivity data.

8. Framework Integration and Documentation

   • Integrate all components into a unified framework and provide comprehensive documentation.

9. Report Writing and Presentation

   • Compile and present a detailed report and deliver a presentation summarizing the project's contributions.

Deliverables:

• A simulation model of the chosen chemical phenomenon developed in OpenFOAM.

• Simulated electrical conductivity distribution implemented in MATLAB EIDORS.

• Synthetic EIT data and its analysis.

• Implemented and documented data storage solution.

• Machine learning model trained on simulation data.

• A fully integrated and documented computational framework incorporating OpenFOAM, EIDORS, and Python.

• A comprehensive project report and an oral presentation summarizing methods, findings, and contributions.

Zeitplanung:

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Checklist

• Introduction / tour in M4

• Urheberrechtsvereinbarung signed

• if applicable: signed confidential agreement

• official registration

Helpful links:

• Thesis Work / Studentische Arbeiten 

• Criteria for the evaluation of Bachelor's and Master's theses, handout version

• Laborordnung

• Spotlights / Kurzvorträge

• Templates (Latex, ppt etc.)

Document Upload Final Thesis / Dokumentenabgabe Abschlussdokument

File of final presentation / Dokumentenabgabe Abschlusspräsentation

Link for further files / Link für weitere Dokumente