The green transition requires that carbon dioxide emissions from transportation is eliminated. One way to achieve this goal is to produce green fuels (chemicals) from surplus renewable energy. This technology is called Power-to-X (PtX).
When these green fuels are used in combustion engines or fuel cells, their net contribution to carbon dioxide emission is zero. To reach 70% CO2 emission reduction by 2030 in Denmark, large offshore as well as a portfolio of onshore PtX plants must be constructed and operated.
Digitalization in the form of forecast-based stochastic model predictive control and optimization is required for the dynamic, coordinated, efficient, reliable, and automatic operation of such highly complex and integrated PtX plants that depend on intermittent solar and wind energy sources. In addition, PtX plants must be operated in reliable coordination with the power and district heating system.
Consequently, the dynamic operation of PtX plants is different from operation at the optimal steady state in traditional chemical plants, and therefore new control and optimization methods for the digitalization of PtX plants are required.
The figure shows the main subunits in a PtX plant. These are the air separation unit (cryogenic distillation for large plants and pressure swing adsorption for medium-sized plants), water electrolysis, an ammonia synthesis loop, and a methanol synthesis loop.
The air separation, the electrolysis units, and the fixed chemical bed reactors can all be modeled using stochastic partial differential-algebraic equations and converted to stochastic differential-algebraic equations (SDAEs) by spatial discretization. In this project, we develop new computational methods for the solution of such SDAEs in optimal estimation and control and demonstrate these methods for advanced process control of PtX plants.
Main supervisor
John Bagterp Jørgensen
Co- supervisor(s)
Tobias Kasper Skovborg Ritschel
Henrik Madsen
Jakob Kjøbsted Huusom
Shi You