Modelling of submerged membrane flocculation hybrid systems using statistical and artificial neural networks methods

Erdei, Laszlo and Vigneswaran, Saravanamuthu and Kandasamy, Jaya K. (2010) Modelling of submerged membrane flocculation hybrid systems using statistical and artificial neural networks methods. Journal of Water Supply: Research and Technology, 59 (2-3). pp. 198-208. ISSN 1606-9935

Abstract

Hybrid membrane filtration processes involve complex physical, chemical and biological phenomena, thus their mechanistic modelling is challenging. The chief advantages of statistical and artificial neural networks (ANN) models (data-driven models) are that they do not require assumptions and simplifications to establish relationships from data. This paper investigates the characteristics and performance of several data-driven methods to model a hybrid membrane system. The focus is on the application of regression analysis and artificial intelligence based methods to a steady-state system. Among empirically based approaches, ANN neural networks methods were found to be very useful to predict permeate quality and membrane fouling. In the past multivariate nonlinear regression had barely been investigated for process modelling in water and waste water treatment. In this study polynomial multivariate nonlinear regression showed a superior performance. Multivariate parametric nonlinear models could match the performance of the nonparametric ANN models in the empirical modelling of complex systems, especially when combined with advanced optimization methods. This paper gives the methodology of how one could optimize a membrane hybrid system using ANN, validating it with one set of data. The same procedure/methodology can be applied to similar systems.


Statistics for USQ ePrint 19135
Statistics for this ePrint Item
Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to publsihed version due to publisher copyright restrictions.
Depositing User: epEditor USQ
Faculty / Department / School: Historic - Faculty of Engineering and Surveying - Department of Agricultural, Civil and Environmental Engineering
Date Deposited: 14 Jun 2011 03:05
Last Modified: 02 Aug 2014 02:36
Uncontrolled Keywords: artificial neural networks; flocculation; mathematical modelling; membrane hybrid systems; multivariate parametric nonlinear regression; organics
Fields of Research (FOR2008): 09 Engineering > 0905 Civil Engineering > 090508 Water Quality Engineering
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
09 Engineering > 0913 Mechanical Engineering > 091307 Numerical Modelling and Mechanical Characterisation
Socio-Economic Objective (SEO2008): E Expanding Knowledge > 97 Expanding Knowledge > 970109 Expanding Knowledge in Engineering
Identification Number or DOI: doi: 10.2166/aqua.2010.064
URI: http://eprints.usq.edu.au/id/eprint/19135

Actions (login required)

View Item Archive Repository Staff Only