Development of an artificial neural network EMMS drag model for the simulation of fluidized beds in chemical looping combustion

The current work presents an Artificial Neural Network EMMS (ANN-EMMS) drag scheme, developed specifically for applications related to fluidized bed (FB) reactors in chemical looping combustion (CLC); the examined particles correspond to the main bed materials encountered in CLC: ilmenite, hematite and titanium oxide. The data that feed the model are generated by a custom-built MATLAB code that solves the EMMS equations. The developed ANN-EMMS model, along with three other drag models, i.e. the Gidaspow, the Wen-Yu and the conventional EMMS, are tested for their performance in CFD simulations for the conditions of the 1 MWth pilot scale FB air reactor of TU Darmstadt. The results of the simulations utilizing the ANN-EMMS followed by those of the conventional EMMS are the closest to published experimental data for the pressure profile in the reactor, while the ilmenite conversion as predicted by both models is in good agreement with published numerical data.