Background and activities

PhD title: Stochastic nonlinear model predictive control of batch processes

PhD description: My PhD projects deals with the optimal operation of batch processes employing nonlinear model predictive control, which describes a popular control technique that directly utilizes a dynamic model of the process. Advantages compared to conventional control approaches include increased productivity and safer operation. Industrial-scale dynamic models are often however affected by significant uncertainties, such as heat exchanger fouling, which may lead to worse product qualities and violation of safety margins. The aim in my project is the development and implementation of novel nonlinear model predictive control formulations accounting for these uncertainties directly to improve performance. The expected outcome of my project is a novel nonlinear model predictive framework that considers uncertainties directly and is simple enough to be solved online. In addition, new data-driven tools are being developed to reduce the uncertainties present using available measurements during and after the batch process.

Main Supervisor: Lars Imsland

Co-Supervisors:  Bjarne Foss, Marcus Reble (BASF)

Short CV: 

2015–2016 MPhil Chemical Engineering, University of Cambridge.
2011–2015 MEng Chemical Engineering, Imperial College London,
First Class Honours.

Acknowledgements:

The research is carried out as part of the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 675215.

I gratefully appreciate BASF for hosting my placement over the
course of the Marie-Curie project