Study of flow patterns in a moving bed reactor for chemical looping combustion based on machine learning methods


 A tower-type moving bed can be used as the air reactor in a chemical looping combustion system because of its low pressure drop and smooth operation. In our previous simulation, a quasi-two-dimensional numerical model was established using DEM approach to investigate the velocity and solid residence time distributions in the moving bed. In this work, the flow patterns under different operating and structural parameters are further studied and optimized via machine learning. Random Forest Regression model is applied to evaluate the importance of each variable to the solid flow pattern, while the BP neural network is applied to build up a high-accuracy model to predict the solid axial velocity in the moving bed without the requirement to understand the physical mechanisms. Results show that the solid mass flux has the least impact on the mass flow index, while the axial position has the dominant influence and what comes next is the wedge angle, reactor angle and ratio of down-comer diameter to reactor diameter. Further, based on established BP neural network model, relation between the effective transition position and structural parameters of the moving bed is built up, which provide valuable guidance for optimization of the reactor configuration.