The renewable energy transition from fossil fuels to green energy is an ongoing global process. In Denmark, the goal is to reduce 70% of CO2 emissions in 2030 and 100% reduction of CO2 emissions in 2050. Wind and solar energy can in some cases directly replace fossil fuels, e.g., for heating households and using electric cars instead of petrol cars. However, for periods without wind and sun, it is essential that renewable energy can be stored efficiently. Additionally, to meet these goals, green energy in the form of green hydrogen, green methanol, and green ammonia is needed for powering busses, trucks, and large ships as well as those areas within the industry that cannot be powered directly by electricity. One way to solve these problems is to use renewable energy from wind and solar cells, water, and air to produce these green fuels. This technology is called Power-to-X (PtX), where “Power” refers to the renewable energy that is converted to “X”, with “X” representing a range of chemicals and fuels e.g., hydrogen, methanol, and ammonia.
PtX is already achievable today but there are energy losses associated with these processes thus making PtX expensive to use compared to fossil fuels. In addition, due to the intermittent energy solar and wind energy sources PtX plants must be operated dynamically, which is different from operation at the optimal steady-state in traditional chemical plants.
Digitalization in the form of forecast-based stochastic model predictive control (MPC) and optimization is required for the dynamic, coordinated, efficient, reliable, and automatic operation of such highly complex and integrated PtX plants.
In this project, we combine two of DTU's strategic research areas: digitalization and sustainability. We do this by investigating and developing new methodologies for advanced process control (APC) e.g., MPC, for PtX. The Figure presents the main subunits in a PtX plant. These subunits 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 and we will model these using stochastic partial differential-algebraic equations and convert them to stochastic differential-algebraic equations (SDAEs) by spatial discretization. We will develop new computational methods for the solution of such SDAEs in optimal estimation and control and we will use these techniques in an APC framework for real-time control of a PtX plant.