Investigation of Hydrodynamics Characteristics of Fluidized Bed with back propagation Artificial Neural Network (BPANN)
Department of Mechanical Engineering, College of Engineering, Mustansiriyah University, Baghdad, Iraq.
Research Article
Global Journal of Engineering and Technology Advances, 2024, 18(01), 018–026.
Article DOI: 10.30574/gjeta.2024.18.1.0251
Publication history:
Received on 23 November 2023; revised on 17 January 2024; accepted on 19 January 2024
Abstract:
Correlations have also been developed with system parameters by using dimensional analysis and an artificial neural network approach. The paper describes an investigation for the thermal design of a fluidized bed cooler and prediction of heat transfer rate among the media categories. It is devoted to the thermal design of such equipment and their application in the industrial fields. In the present work, an extensive ANN by using back propagation (BP) has been carried out to correlate the expansion ratio, fluctuation ratio in gas-solid fluidized bed. Back propagation network is the most well-known and widely used among the current types of neural network system, several applications of ANN for modeling of nonlinear process systems and subsequent control were reported. In back-propagation, different ANN structures (I×H×O) with varying number of neurons in the hidden layer were used as a tool for training input and output data for prediction value of hydrodynamic characteristics of the bed system. It was noted that ring models are the best ones in reducing bed expansion ratio and fluctuation ratio around (25%) and (23.22%).
Keywords:
Fluidized Bed; Ring-Promoted; bed expansion ratio; Intelligent Systems
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