I develop scientific computing software for optimized process control. The software is based on linear and nonlinear model predictive control algorithms which in turn combine algorithms for 1) state estimation and 2) optimal control/dynamic optimization. The software is aimed at a large variety of processes, including processes in 1) biotech, 2) cement plants, 3) oil production, and many others.
In 2018, I received a PhD degree in Applied Mathematics with focus on scientific computing from the Technical University of Denmark (DTU) where I have previously obtained a MSc in Mathematical Modeling and Computation as well as a BSc in Mathematics and Technology.
During my PhD, I worked on computationally efficient algorithms and software for nonlinear model predictive control, optimal control/dynamic optimization, and state estimation of semi-explicit index-1 DAEs with a focus on phase equilibrium processes, e.g. 1) compositional reservoir flow, 2) distillation columns, and 3) flash separation. It is natural to formulate models of such processes using DAEs in this semi-explicit form.