Modelling the Hydrodynamic Characteristics of Gas-Liquid-Solid Fluidized Bed using Artificial Neural Networks

Abstract

Gas–liquid–solid fluidized beds are used extensively in the refining, petrochemical, pharmaceutical, biotechnology, food and environmental industries. The fundamental characteristics of a three-phase fluidized bed have been recently studied extensively. The reviews indicate the importance of the information of phase holdup and bed voidage characteristics, in the optimal design of a three-phase fluidized bed reactor.
The various hydrodynamic parameters of three phase fluidized bed have been modeled using Artificial Neural Networks (ANNs). ANNs are good at modeling of non linear parameters, with the ability to generalize the relationships among the data. The data for developing the models has been generated using various correlations available from literature. These correlations are valid for different ranges of the variables. So, artificial neural networks are trained using this vast data range and a generalized model for the hydrodynamic parameters is developed.
This project report can be divided mainly into three parts. The first part discusses about importance of gas-liquid-solid fluidized bed, their modes of operation, important hydrodynamic properties those have been studied either related to modeling and applications of gas-liquid-solid fluidized bed. The second part gives an overview of the basics of Artificial Neural Networks (ANNs) and the various architectures of neural networks that are commonly used for modeling. The third part consists of the details of the problem description and the approach used by ANN to model the hydrodynamic characteristics. The results show that the model has been effective in generalizing the relationship of various hydrodynamic characteristics with their respective independent variables.