Prediction Of Permeate Flux Decline In Crossflow Membrane Filtration Of Colloidal Suspension: A Radial Basis Function Neural Network Approach

Date
2005-08
Authors
Chen, Huaiqun
Contributor
Advisor
Department
Instructor
Depositor
Speaker
Researcher
Consultant
Interviewer
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
Ending Page
Alternative Title
Abstract
The capability of a Radial Basis Function Neural Network (RBFNN) to predict long-term permeate flux decline in crossflow membrane filtration was investigated. Operating conditions such as transmembrane pressure and filtration time along with feed water parameters of particle radius, solution pH, and ionic strength were used as inputs to predict the permeate flux. Simulation results indicated that one single RBFNN accurately predicted the permeate flux decline under various experimental conditions of colloidal membrane filtrations and produced better predictability compared to those of the multiple regression method and regular multi-layer feed-forward Back Propagation Neural Network (BPNN), due to the more sophisticated training system of RBFNN. Further development of the artificial neural network approach to membrane filtration will enable the design of full or large scale processes with lab or pilot scale experiments.
Description
Keywords
Citation
Extent
Format
Geographic Location
Time Period
Related To
Theses for the degree of Master of Science (University of Hawaii at Manoa). Civil Engineering; no. 3974
Rights
All UHM dissertations and theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission from the copyright owner.
Rights Holder
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.