The present work proposes a novel radially cross-flow multistage solid-liquid circulating fluidized bed （SLCFB）. The SLCFB primarily consists of a single multistage column （having an inner diameter of 100 mm and le...The present work proposes a novel radially cross-flow multistage solid-liquid circulating fluidized bed （SLCFB）. The SLCFB primarily consists of a single multistage column （having an inner diameter of 100 mm and length of 1.40 m）, which is divided into two sections wherein both the steps of utilization or loading （e.g., adsorption and catalytic reaction） and regeneration of the solid phase can be carried out simulta- neously in continuous mode. The hydrodynamic characteristics were studied using ion exchange resin as the solid phase and water as the fluidizing medium. The loading and flooding states were determined for three particle sizes; i.e,. 0.30, 0,42, and 0.61 ram. The effects of the superficial liquid velocity and solid feed rate on the solid hold-up were investigated under loading and flooding conditions. The solid hold-up increases with an increase in the solid feed rate and decreases with an increase in the superficial liquid velocity. An artificial-intelligence formalism, namely the multilayer perceptron neural network （MLPNN）, was employed for the prediction of the solid hold-up. The input space of MLPNN-based model consists of four parameters, representing operating and system parameters of the proposed SLCFB. The developed MLPNN-based model has excellent prediction accuracy and generalization capability.展开更多
文摘The present work proposes a novel radially cross-flow multistage solid-liquid circulating fluidized bed （SLCFB）. The SLCFB primarily consists of a single multistage column （having an inner diameter of 100 mm and length of 1.40 m）, which is divided into two sections wherein both the steps of utilization or loading （e.g., adsorption and catalytic reaction） and regeneration of the solid phase can be carried out simulta- neously in continuous mode. The hydrodynamic characteristics were studied using ion exchange resin as the solid phase and water as the fluidizing medium. The loading and flooding states were determined for three particle sizes; i.e,. 0.30, 0,42, and 0.61 ram. The effects of the superficial liquid velocity and solid feed rate on the solid hold-up were investigated under loading and flooding conditions. The solid hold-up increases with an increase in the solid feed rate and decreases with an increase in the superficial liquid velocity. An artificial-intelligence formalism, namely the multilayer perceptron neural network （MLPNN）, was employed for the prediction of the solid hold-up. The input space of MLPNN-based model consists of four parameters, representing operating and system parameters of the proposed SLCFB. The developed MLPNN-based model has excellent prediction accuracy and generalization capability.
