CLR reactor simulation example Sample Clauses

CLR reactor simulation example. A Chemical Looping Reforming (CLR) fuel reactor was simulated to illustrate the capabilities of the model. Methane and steam were fed to the reactor along with an oxygen carrier/catalyst (NiO). Catalytic steam methane reforming and water gas shift reactions as well as heterogeneous reduction of the oxygen carrier by fuel gasses were simulated. Some of the most relevant profiles are presented in Figure 9. Note that the variables were normalized by the maximum value of each variable to allow for visualization of all variables on a single graph. Figure 9 – Relevant Profiles for: Gas density (0.145-0.179 kg/m3); Total void fraction (0.868-0.874); Gas superficial velocity (4.38-5.88 m/s) and Temperature (752-800 °C) Figure 9 shows that the profiles for relevant variables in the reactor system qualitatively behave as expected: Gas density: The gas density decreases during the first 20 cm of the reactor due to the production of H2 (light gas) from the very fast catalytic steam methane reforming reaction. As the reaction system nears equilibrium and the catalytic reaction slows down, the density increases due to the conversion of H2 to H2O in the heterogeneous reduction of NiO to Ni. The decrease in temperature due to endothermic reactions also increases gas density along the height of the reactor. Figure 10 shows the composition profiles of the different species present in the reactor. The NiO is slowly reduced (almost at constant rate) until the end of the reactor where it tends to zero. Over the first 20 cm of the reactor length, the reforming reactions are dominant and occur very fast to produce H2 and consume CH4. After the initial 20 cm, the catalytic reaction approach equilibrium and the heterogeneous reaction dominates with production of steam and consumption of the H2 along the reactor bed. These results show that the model is capable of simulating a CLR reactor system. Any advanced closures (from other work packages in the NanoSim project or the literature) can be easily implemented in the code to further improve predictive capabilities.