Konferensartikel

An increasing demand on load flexibility in power supply networks is the motivation to look at flexible; and possibly optimal control systems for power plants with carbon capture units. Minimizing the energy demand for carbon dioxide removal under these circumstances reduces the cost disadvantage of carbon capture compared to conventional production. In this work a dynamic model in Modelica of a chemical absorption process run with an aqueous monoethanolamine (MEA) is developed; and used for solving optimal control problems. Starting from a rather detailed dynamic model of the process; model reduction is performed based on physical insight. The reduced model computes distinctly faster; shows similar transient behavior and reflects trends for optimal steady-state operations reported in the literature. The detailed model has been developed in Dymola; and the reduced model is used in JModelica. org; a platform supporting non-linear dynamic optimization. First results are shown on the dynamic optimization of the desorption column; the main cause of energy usage in the process.

CO₂; absorption; model; optimization; nonlinear model predictive control; Modelica; Jmodelica; org