By Topic

Using Design of Experiments and Virtual Instrumentation to Evaluate the Tribocharging of Pulverulent Materials in Compressed-Air Devices

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

6 Author(s)
Dascalescu, L. ; Electrohydrodynamic (EHD) Group, Univ. of Poitiers, Angouleme ; Medles, K. ; Das, S. ; Younes, M.
more authors

Tribocharging of pulverulent materials in compressed-air devices is a typical multifactorial process. Quantification of the effects of the factors and of their interactions is a prerequisite for the development of new tribocharging devices for industrial applications. This paper aims at demonstrating the interest of using the design of experiments methodology in association with virtual instrumentation for the study of such processes, in view of their modeling and optimization. A classical 23 full-factorial design followed by a composite design were employed for conducting experiments simulating the tribocharging conditions of starch powder. The response function was the charge/mass ratio of the material collected in a modified Faraday cage, at the exit of the tribocharging device, the factors under investigation being the injection pressure, the dillution pressure, and the vortex pressure. The charge measurements were performed using a digital electrometer connected to a personal computer equiped with a data acquisition system. The data were processed by a custom-designed LabView virtual instrument. By using appropriate design of experiments software, it was possible to estimate the effects of these factors and then derive the model of the process as a quadratic polynomial function.

Published in:

Industry Applications, IEEE Transactions on  (Volume:44 ,  Issue: 1 )