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 | |
| Publication status | Published - 2009 Oct 1 |
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Keywords
- Artificial neural network (ANN)
- Hybrid system
- Multi-stage flash (MSF)
- Seawater reverse osmosis membrane (SWRO)
- Temperature control
ASJC Scopus subject areas
- Chemical Engineering(all)
- Mechanical Engineering
- Chemistry(all)
- Materials Science(all)
- Water Science and Technology
Cite this
Artificial neural network model for optimizing operation of a seawater reverse osmosis desalination plant. / Lee, Young Geun; Lee, Yun Seok; Jeon, Jong June; Lee, Sangho; Yang, Dae Ryook; Kim, In S.; Kim, Joon Ha.
In: Desalination, Vol. 247, No. 1-3, 01.10.2009, p. 180-189.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Artificial neural network model for optimizing operation of a seawater reverse osmosis desalination plant
AU - Lee, Young Geun
AU - Lee, Yun Seok
AU - Jeon, Jong June
AU - Lee, Sangho
AU - Yang, Dae Ryook
AU - Kim, In S.
AU - Kim, Joon Ha
PY - 2009/10/1
Y1 - 2009/10/1
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.
KW - Artificial neural network (ANN)
KW - Hybrid system
KW - Multi-stage flash (MSF)
KW - Seawater reverse osmosis membrane (SWRO)
KW - Temperature control
UR - http://www.scopus.com/inward/record.url?scp=69349086550&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=69349086550&partnerID=8YFLogxK
U2 - 10.1016/j.desal.2008.12.023
DO - 10.1016/j.desal.2008.12.023
M3 - Article
AN - SCOPUS:69349086550
VL - 247
SP - 180
EP - 189
JO - Desalination
JF - Desalination
SN - 0011-9164
IS - 1-3
ER -