- You are an enthusiastic WO Master student with experience in Model Predictive Control theory and practice.
- You are looking for a challenging graduation internship for 6 to 9 months.
- Affinity with modelling and systems and control.
- You are results-driven and have an entrepreneurial mindset
- You thrive on working independently in a dynamic and complex organization.
Wat bieden wij jou?
- A paid internship.
- A challenging and instructive internship with good supervision.
- Together with you, a clear assignment will be designed in advance, such that it meets the requirements of your college/university.
- You can gain valuable experience within a large organisation and the Netherlands' only steel manufacturing industry.
- You can count on a no-nonsense and result-oriented organisation, in which you can make a concrete contribution to the realisation of improvement objectives.
- We offer the possibility to make use of our library services.
- You can sign up for 'Interns For Steel'. This is a group set up for trainees by trainees. They organise regular tours, lunch together once a week and there is a monthly get together.
Wat is jouw werkomgeving?
The project will be conducted within R&D department of Tata Steel Ijmuiden with great opportunities for networking and being closely involved in the future developments. We are looking forward to welcoming you in our office where we maintain an energetic, informal working atmosphere. You will get the equipment needed for your work.
- A paid internship.
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Master Graduate Internship Model Predictive Control - Velsen-Noord, Nederland - Tata Steel
Beschrijving
Distributed Model Predictive Control for large-scale processes.
Wat ga je doen?
Background
Tata Steel IJmuiden manufactures high-quality steel strip using numerous successive production plants. The type of installations range from ironmaking, steelmaking and casting to hot rolling, coating, and annealing. These processes are made possible using advanced control techniques such as Model Predictive Control. To further improve the quality of our products and stability of our processes we want to take the next step in advanced process control: distributed model predictive control.
You will work in the Thermal Processes knowledge group in Research and Development alongside twenty researchers from various backgrounds.
What are you going to create?
You will create a proof of concept distributed model predictive (DMPC) controller to optimize the allocation of works arising gasses. The performance of DMPC should be quantitatively compared to regular MPC using monte carlo simulations, assisted where necessary by using historical plant data. You should be able to make (simplified) state-space models of some key processes, be able to implement your own DMPC algorithm, and perform data analysis quantifying performance of the controller compared to a benchmark.
