Artificial neural network model for optimizing operation of a seawater reverse osmosis desalination plant

Young Geun Lee; Yun Seok Lee; Jong June Jeon; Sangho Lee; Dae Ryook Yang; In S. Kim; Joon Ha Kim

doi:10.1016/j.desal.2008.12.023

Artificial neural network model for optimizing operation of a seawater reverse osmosis desalination plant

Young Geun Lee, Yun Seok Lee, Jong June Jeon, Sangho Lee, Dae Ryook Yang, In S. Kim, Joon Ha Kim

Research output: Contribution to journal › Article › peer-review

84 Citations (Scopus)

Abstract

An artificial neural network (ANN) was developed to predict the performance of a seawater reverse osmosis (SWRO) desalination plant, and was then applied to the simulation of feed water temperature. The model consists of five input parameters (i.e., feed temperature, feed total dissolved solids (TDS), trans-membrane pressure (TMP), feed flow rate, and time) and two output parameters (i.e., permeate TDS and flow rate). Then, the one-year operation data (n = 200) from the Fujairah SWRO plant was divided into three data sets (i.e., training, validation, and test data set) to develop the ANN model. The trained ANN model was subsequently found to produce good agreement between the observed and simulated data (TDS: R² = 0.96; flow rate: R² = 0.75) in the test data set. The results of this study show that the variation of the feed water temperature and TMP was found to significantly affect both the permeate TDS and flow rate. From subsequent simulations with various temperature controls, it is further suggested that the permeate TDS can be reduced using a linear increase control (from 27.5 to 29.5°C) for the feed temperature in an SWRO hybrid system with multi-stage flash (MSF) distillation, such as the Fujairah plant.

Original language	English
Pages (from-to)	180-189
Number of pages	10
Journal	Desalination
Volume	247
Issue number	1-3
DOIs	https://doi.org/10.1016/j.desal.2008.12.023
Publication status	Published - 2009 Oct

Bibliographical note

Funding Information:
This research was supported by a grant (Code# C106A1520001-06A085600210) from the Plant Technology Advancement Program funded by the Ministry of Land, Transport and Maritime Affairs of the Korean government. In addition, we would like to thank Doosan Heavy Industries & Construction for assisting our study by providing experimental data from the Fujairah plant in Dubai.

Copyright:
Copyright 2009 Elsevier B.V., All rights reserved.

Keywords

Artificial neural network (ANN)
Hybrid system
Multi-stage flash (MSF)
Seawater reverse osmosis membrane (SWRO)
Temperature control

ASJC Scopus subject areas

General Chemistry
General Chemical Engineering
General Materials Science
Water Science and Technology
Mechanical Engineering

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10.1016/j.desal.2008.12.023

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title = "Artificial neural network model for optimizing operation of a seawater reverse osmosis desalination plant",

abstract = "An artificial neural network (ANN) was developed to predict the performance of a seawater reverse osmosis (SWRO) desalination plant, and was then applied to the simulation of feed water temperature. The model consists of five input parameters (i.e., feed temperature, feed total dissolved solids (TDS), trans-membrane pressure (TMP), feed flow rate, and time) and two output parameters (i.e., permeate TDS and flow rate). Then, the one-year operation data (n = 200) from the Fujairah SWRO plant was divided into three data sets (i.e., training, validation, and test data set) to develop the ANN model. The trained ANN model was subsequently found to produce good agreement between the observed and simulated data (TDS: R2 = 0.96; flow rate: R2 = 0.75) in the test data set. The results of this study show that the variation of the feed water temperature and TMP was found to significantly affect both the permeate TDS and flow rate. From subsequent simulations with various temperature controls, it is further suggested that the permeate TDS can be reduced using a linear increase control (from 27.5 to 29.5°C) for the feed temperature in an SWRO hybrid system with multi-stage flash (MSF) distillation, such as the Fujairah plant.",

keywords = "Artificial neural network (ANN), Hybrid system, Multi-stage flash (MSF), Seawater reverse osmosis membrane (SWRO), Temperature control",

author = "Lee, {Young Geun} and Lee, {Yun Seok} and Jeon, {Jong June} and Sangho Lee and Yang, {Dae Ryook} and Kim, {In S.} and Kim, {Joon Ha}",

note = "Funding Information: This research was supported by a grant (Code# C106A1520001-06A085600210) from the Plant Technology Advancement Program funded by the Ministry of Land, Transport and Maritime Affairs of the Korean government. In addition, we would like to thank Doosan Heavy Industries & Construction for assisting our study by providing experimental data from the Fujairah plant in Dubai. Copyright: Copyright 2009 Elsevier B.V., All rights reserved.",

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language = "English",

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AU - Yang, Dae Ryook

AU - Kim, In S.

AU - Kim, Joon Ha

N1 - Funding Information: This research was supported by a grant (Code# C106A1520001-06A085600210) from the Plant Technology Advancement Program funded by the Ministry of Land, Transport and Maritime Affairs of the Korean government. In addition, we would like to thank Doosan Heavy Industries & Construction for assisting our study by providing experimental data from the Fujairah plant in Dubai. Copyright: Copyright 2009 Elsevier B.V., All rights reserved.

PY - 2009/10

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N2 - An artificial neural network (ANN) was developed to predict the performance of a seawater reverse osmosis (SWRO) desalination plant, and was then applied to the simulation of feed water temperature. The model consists of five input parameters (i.e., feed temperature, feed total dissolved solids (TDS), trans-membrane pressure (TMP), feed flow rate, and time) and two output parameters (i.e., permeate TDS and flow rate). Then, the one-year operation data (n = 200) from the Fujairah SWRO plant was divided into three data sets (i.e., training, validation, and test data set) to develop the ANN model. The trained ANN model was subsequently found to produce good agreement between the observed and simulated data (TDS: R2 = 0.96; flow rate: R2 = 0.75) in the test data set. The results of this study show that the variation of the feed water temperature and TMP was found to significantly affect both the permeate TDS and flow rate. From subsequent simulations with various temperature controls, it is further suggested that the permeate TDS can be reduced using a linear increase control (from 27.5 to 29.5°C) for the feed temperature in an SWRO hybrid system with multi-stage flash (MSF) distillation, such as the Fujairah plant.

AB - An artificial neural network (ANN) was developed to predict the performance of a seawater reverse osmosis (SWRO) desalination plant, and was then applied to the simulation of feed water temperature. The model consists of five input parameters (i.e., feed temperature, feed total dissolved solids (TDS), trans-membrane pressure (TMP), feed flow rate, and time) and two output parameters (i.e., permeate TDS and flow rate). Then, the one-year operation data (n = 200) from the Fujairah SWRO plant was divided into three data sets (i.e., training, validation, and test data set) to develop the ANN model. The trained ANN model was subsequently found to produce good agreement between the observed and simulated data (TDS: R2 = 0.96; flow rate: R2 = 0.75) in the test data set. The results of this study show that the variation of the feed water temperature and TMP was found to significantly affect both the permeate TDS and flow rate. From subsequent simulations with various temperature controls, it is further suggested that the permeate TDS can be reduced using a linear increase control (from 27.5 to 29.5°C) for the feed temperature in an SWRO hybrid system with multi-stage flash (MSF) distillation, such as the Fujairah plant.

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KW - Hybrid system

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KW - Seawater reverse osmosis membrane (SWRO)

KW - Temperature control

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Artificial neural network model for optimizing operation of a seawater reverse osmosis desalination plant

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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