Novel multistage solid-liquid circulating fluidized bed： Hydrodynamic characteristics

作     者：Prakash V. Chavan Manjusha A. Thombare Sandip B. Bankar Dinesh V. Kalaga Veena A. Patil-Shinde 

作者机构：Department of Chemical Engineering College of Engineering Bharati Vidyapeeth Deemed University Pune 411 043. India Department of Biotechnology and Chemical Technology School of Chemical Technology Alto University P.O. Box 16100. FI-00076 Aalto Finland Department of Chemical Engineering City College of New York CUNY NY USA 

出 版 物：《Particuology》 (颗粒学报（英文版）)

年 卷 期：2018年第16卷第3期

页      面：134-142页

核心收录：

学科分类：082802[工学-农业水土工程] 090707[农学-水土保持与荒漠化防治] 0907[农学-林学] 081705[工学-工业催化] 08[工学] 0817[工学-化学工程与技术] 0828[工学-农业工程] 09[农学] 0815[工学-水利工程] 

基　　金：support from the Department of Science and Technology (DST)  New Delhi  India 

主　　题：Solid-liquid fluidized bed Solid-liquid circulating fluidized bed Hydrodynamics Modeling 

摘      要：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.