Application of Artificial Neural Network for Single Horizontal Bare Tube and Bare Tube Bundles in Gas-Solid (Air-Solid) Fluidized Bed of Small and Large Particles: Heat Transfer Predictions

Abstract

This paper presents heat transfer analysis of single horizontal bare tube and in-line arrangement of bare tube bundles in gas-solid (air-solid) fluidized bed and predictions are done by using Artificial Neural Network (ANN) based on the experimental data. Measurement of average heat transfer coefficient was made by local thermal simulation technique in a cold square bubbling air-fluidized bed of size 0.305m × 0.305m. Studies were conducted for single bare tube and bare tube bundles of in–line arrangement using beds of small (average particle diameter less than 1mm) silica sand particles and of large (average particle diameter greater than 1mm) particle (raagi and mustard). Within the range of experimental conditions influence of bed particle diameter (Dp), fluidizing velocity (U) were studied, which are significant parameters affecting heat transfer. Artificial neural networks (ANNs) have been receiving an increasing attention for simulating engineering systems due to some interesting characteristics such as learning capability, fault tolerance, and non-linearity. Here, feed-forward architecture and trained by back-propagation technique is adopted to predict heat transfer analysis found from experimental results. The ANN is designed to suit the present system which has 3 inputs and 2 outputs. The network predictions are found to be in very good agreement with the experimental observed values of bare tube heat transfer coefficient (hb) and Nusselt number of bare tube (Nub).