Statistical analysis of fuidized dense phase conveying of fine particles

Pressure drop in fluidized dense phase pneumatic conveying of fine particles depends upon parameters such as air or solid mass flow rate, pipe diameter or length. For pipeline layouts with definite dimensions, correlations presented by several researchers can be used to predict pressure drop with greater accuracy. However, while estimating pressure drop for an alternative pipeline configuration, majority of these correlations exhibit substantial errors. Straight-line pressure drop was modelled using the response surface methodology in Minitab software under the design of experiment method. On the response straight line pressure drop, a multiple regression model was developed with input parameters such as air mass flow rate, solids mass flow rate, pipeline length, pipeline diameter, particle diameter and solid loading ratio. The experimental data available as input parameters is used to develop a pressure drop correlation and subsequently applied to predict pressure drop for different pipeline configurations +/- 15% error margin. (c) 2022 Elsevier B.V. All rights reserved.

Automated summary

In this study, the response surface methodology was used to model the straight-line pressure drop and a multiple regression model was developed to predict the pressure drop. By utilizing experimental data, the model can be applied to predict the pressure drop for different pipeline configurations.