PhD scholarship in Model Predictive Control of Zero Emission Industrial Processes is available at DTU Compute, Technical University of Denmark, Denmark
Deadline to Apply
May 11, 2021 (23: 59 GMT +1)
|No. of Position(s)||1|
|Research Area||– Electrical Engineering|
– Computer Science
|Scholarship||“According to standard norms”|
Technical University of Denmark
|Contract Period||3 Years|
|Starting date||Jun 01, 2021|
- Two-year master’s degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master’s degree.
- We are looking for a person with a mathematical background and in particular a strong knowledge in model predictive control that should be documented by scientific publications and experience in working in a research team that develop model predictive control technology.
- Master’s degree in engineering with a strong focus on automation and control (e.g. Electrical Engineering)
- Advanced knowledge of model predictive control.
- Foundations in mathematical modelling.
- Strong programming skills in Python, Matlab, C/C++, Docker, SQL.
- Experience with machine learning techniques.
Soft Skills Requirements
- Fluent in spoken and written English. Minimum proficiency level is B2, C1 is definitely preferred.
- Self-reliant working style
- Curious and ambitious personality.
- In this project, you will develop simulation models for the individual unit operations in offshore oil facilities, i.e riser flow (slug flow), 3-phase separator, hydro-cyclones, de-gasser/flotation, and membrane filtration
- You will also develop novel numerical algorithm that enables systematic construction of models from existing and generated data using active learning principles.
- The active learning is based on systematic combination of optimal experimental design, parameter- and state-estimation, and predictive control for dynamical systems.
- The developed methods must be published and applied industrially.
- Therefore, it is important that you can document experience with industrial MPC applications and projects as well as with publication of the used methods and obtained results in such activities
How to Apply?
To apply, please open the link “Apply online”, fill out the online application form, and attach all your materials in English in one PDF file.
Applications must be submitted as one PDF file containing all materials to be given consideration. The file must include:
- A letter motivating the application (cover letter)
- Curriculum vitae
- Grade transcripts and BSc/MSc diploma
- Excel sheet with translation of grades to the Danish grading system (see guidelines and Excel spreadsheet here)
About the project/department
The main goal of this PhD project is to develop an MPC based advanced process control system that significantly reduces the oil in the produced water discharge from off-shore installations. We will do this by implementing plant-wide economic nonlinear model predictive controllers. This advanced process control system enables zero emissions of oil-in-water and high oil production. This PhD project will develop simulation models and reduced-order models for the model-based controllers. We will test the developed controllers in the pilot plant facilities located at Aalborg University-Esbjerg. The project will also use machine learning tools for systematic data mining. These methods must facilitate automatic model idenfication for the MPCs and automatic tuning of the MPCs.
The PhD project is carried out in close collaboration with the Danish Hydrocarbon Research and Technology Centre (DHRTC) and Aalborg University Esbjerg (AAU). In addition, ABB and Total will provide advice regarding industrial requirements. Therefore, it is essential that the candidate has very good team player skills to be able work efficiently with the many stakeholders in the project. This could be documented by previous research work in a large research project.
- If you are applying from abroad, you may find useful information on working in Denmark and at DTU at DTU – Moving to Denmark.
- All interested candidates irrespective of age, gender, race, disability, religion or ethnic background are encouraged to apply.
John Bagterp Jørgensen