Explicit prediction models for brackish water electrodialysis desalination plants: Energy consumption and membrane area

M. U. Siddiqui, Muhammad M. Generous, Naef A.A. Qasem, Syed M. Zubair*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

With the demand for desalination systems to mitigate freshwater scarcity worldwide, there is a need for a reliable and straightforward model for the design and evaluation of desalination systems. Two critical metrics for membrane-based desalination systems are specific energy consumption and the required membrane size. The current study presents a strategy to develop simple explicit models for electrodialysis desalination systems. The procedure starts with determining important independent parameters using global sensitivity analysis on a dataset generated using a detailed analytical model of the electrodialysis system. Multi regression models are then developed using the model inputs standardized using their means and standard deviations. The set procedure was applied to an electrodialysis system configuration taken from the literature. After developing regression models for specific energy consumption and the total required membrane area, statistical and residual analyses were carried out to analyze the performance of the developed models. The developed models work for feedwater with salinity ranging from 1000 to 15000 ppm, while the permeate salinity is fixed at 200 ppm. The results showed that the developed regression models predict the specific energy consumption and the required membrane area with mean absolute percentage errors of 2.6% and 3.6%, respectively.

Original languageEnglish
Article number115656
JournalEnergy Conversion and Management
Volume261
DOIs
StatePublished - 1 Jun 2022

Bibliographical note

Publisher Copyright:
© 2022 Elsevier Ltd

Keywords

  • Electrodialysis
  • Explicit model
  • Membrane area
  • Regression
  • Specific energy consumption

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Nuclear Energy and Engineering
  • Fuel Technology
  • Energy Engineering and Power Technology

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