Study of Flow Patterns in a Moving Bed Reactor for Chemical Looping Combustion Based on Machine Learning Methods

Abstract

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 discrete element method (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 studied and optimized via machine learning methods. The random Forest regression model is applied to evaluate the importance of each variable to the solid flow pattern, while the feed forward neural network is applied to buildup 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 the established feed forward neural network model, relation between the effective transition position and structural parameters of the moving bed is built, which provides valuable guidance for optimization of the reactor configuration.