Skip navigation
Please use this identifier to cite or link to this item:
Title: Modeling, simulation, and optimization of the membrane performance of seawater reverse osmosis desalination plant using neural network and fuzzy based soft computing techniques
Authors: Mahadeva R.
Kumar M.
Manik, Gaurav
Patole S.P.
Published in: Desalination and Water Treatment
Abstract: One of the challenging tasks in desalination plants is to manage and optimize their real-time performance. In this direction, soft computing techniques have demonstrated superior efficiency compared to conventional techniques in overcoming this problem and predict optimal process conditions. In this paper, artificial neural network (ANN), particle swarm optimization assisted ANN (PSO-ANN), fuzzy inference system (FIS), and adaptive neuro-fuzzy inference system (ANFIS) models have been developed to predict the membrane performance of the seawater reverse osmosis (SWRO) desalination plants. All developed models consisted of four input parameters: Feed temperature (5°C–30°C), feed pressure (45–65 kgf/cm2), feed flow rate (~30 L/min), and feed total dissolved solids (TDS) (~32,000 ppm) with two output parameters: Permeate flow rate (2.8–8.8 L/min) and permeate TDS (45–121.6 ppm). The models so obtained and trained produced a fairly good agreement between the experimental and predicted dataset. Amongst all models simulated, the PSO-ANN model provides superior performance for permeate flow rate and TDS (R2 = 0.998, 0.997) with minimum errors (MSE = 0.007, 1.783) compared to other models (ANN, FIS, and ANFIS). Future results suggested that models may serve as perfect diagnostic tools for designing SWRO desalination plants to reduce the Capex, Opex, time, and energy. © 2021 Desalination Publications. All rights reserved.
Citation: Desalination and Water Treatment, 229: 17-30
Issue Date: 2021
Publisher: Desalination Publications
Keywords: Adaptive neuro-fuzzy inference system
Artificial neural network
Fuzzy inference system
Particle swarm optimization assisted ANN
Seawater reverse osmosis
ISSN: 19443994
Author Scopus IDs: 57204158068
Author Affiliations: Mahadeva, R., Department of Polymer and Process Engineering, Indian Institute of Technology, Roorkee, Uttarakhand, 247667, India
Kumar, M., Department of Electrical Engineering, Indian Institute of Technology, Roorkee, Uttarakhand, 247667, India
Manik, G., Department of Polymer and Process Engineering, Indian Institute of Technology, Roorkee, Uttarakhand, 247667, India
Patole, S.P., Department of Physics, Khalifa University of Science and Technology, Abu Dhabi, 127788, United Arab Emirates
Funding Details: SPP would like to acknowledge the financial support from Khalifa University through FSU-2018-29, and the Department of Education and Knowledge under “The ADEK Award for Research Excellence (AARE) 2018: AARE18-136”. Khalifa University of Science, Technology and Research, KU: FSU-2018-29; Department of Education and Knowledge, ADEK: AARE18-136
Corresponding Author: Manik, G.; Department of Polymer and Process Engineering, India; email:
Appears in Collections:Journal Publications [PE]

Files in This Item:
There are no files associated with this item.
Show full item record

Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.