An optimized PSO-ANN model for improved prediction of water treatment desalination plant performance
An accurate prediction of the performance of water treatment desalination plants could directly improve the global socio-economic balance. In this regard, many researchers have been engaged in the various artificial intelligence applied soft computing techniques to predict actual process outcomes. I...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IWA Publishing
2022-03-01
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| Series: | Water Supply |
| Subjects: | |
| Online Access: | http://ws.iwaponline.com/content/22/3/2874 |
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| author | R. Mahadeva M. Kumar S. P. Patole G. Manik |
| author_facet | R. Mahadeva M. Kumar S. P. Patole G. Manik |
| author_sort | R. Mahadeva |
| collection | DOAJ |
| description | An accurate prediction of the performance of water treatment desalination plants could directly improve the global socio-economic balance. In this regard, many researchers have been engaged in the various artificial intelligence applied soft computing techniques to predict actual process outcomes. Inspired by the significance of such techniques, an optimized Particle Swarm Optimization based Artificial Neural Network (PSO-ANN) technique has been proposed herewith to predict an accurate performance of the reverse osmosis (RO) based water treatment desalination plants. Literature suggests that the improvements of the soft computing models depend on their modeling parameters. Therefore, we have included an extended list of nine modeling parameters with a systematic indepth investigation to explore their optimal values. Finally, the model's simulations results (R2 = 99.1%, Error = 0.006) were found superior to the existing ANN models (R2 = 98.8%, Error = 0.060), with the same experimental datasets. Additionally, the simulation results recommend that among many parameters considered, the number of hidden layer nodes (n), swarm sizes (SS), and the weight of inertia (ω) play a major role in the model optimization. This study for a more accurate prediction of the plant's performance shall pave the way for the process design and control engineers to improve the plant efficiency further. HIGHLIGHTS
Proposed parameters to accurately model permeate flux of RO-based desalination plant.;
Rigorous analysis with extended list of nine PSO-ANN parameters to achieve optimal model.;
Model achieved superior results (R2 = 99.1%, Error = 0.006) than existing models (R2 = 98.8%, Error = 0.060).; |
| first_indexed | 2024-04-14T06:59:02Z |
| format | Article |
| id | doaj.art-6a4f96af49224760b9643e645ad3b870 |
| institution | Directory Open Access Journal |
| issn | 1606-9749 1607-0798 |
| language | English |
| last_indexed | 2024-04-14T06:59:02Z |
| publishDate | 2022-03-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Water Supply |
| spelling | doaj.art-6a4f96af49224760b9643e645ad3b8702022-12-22T02:06:48ZengIWA PublishingWater Supply1606-97491607-07982022-03-012232874288210.2166/ws.2021.432432An optimized PSO-ANN model for improved prediction of water treatment desalination plant performanceR. Mahadeva0M. Kumar1S. P. Patole2G. Manik3 Department of Polymer and Process Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India Department of Physics, Khalifa University of Science and Technology, Abu Dhabi 127788, United Arab Emirates Department of Polymer and Process Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India An accurate prediction of the performance of water treatment desalination plants could directly improve the global socio-economic balance. In this regard, many researchers have been engaged in the various artificial intelligence applied soft computing techniques to predict actual process outcomes. Inspired by the significance of such techniques, an optimized Particle Swarm Optimization based Artificial Neural Network (PSO-ANN) technique has been proposed herewith to predict an accurate performance of the reverse osmosis (RO) based water treatment desalination plants. Literature suggests that the improvements of the soft computing models depend on their modeling parameters. Therefore, we have included an extended list of nine modeling parameters with a systematic indepth investigation to explore their optimal values. Finally, the model's simulations results (R2 = 99.1%, Error = 0.006) were found superior to the existing ANN models (R2 = 98.8%, Error = 0.060), with the same experimental datasets. Additionally, the simulation results recommend that among many parameters considered, the number of hidden layer nodes (n), swarm sizes (SS), and the weight of inertia (ω) play a major role in the model optimization. This study for a more accurate prediction of the plant's performance shall pave the way for the process design and control engineers to improve the plant efficiency further. HIGHLIGHTS Proposed parameters to accurately model permeate flux of RO-based desalination plant.; Rigorous analysis with extended list of nine PSO-ANN parameters to achieve optimal model.; Model achieved superior results (R2 = 99.1%, Error = 0.006) than existing models (R2 = 98.8%, Error = 0.060).;http://ws.iwaponline.com/content/22/3/2874artificial neural networkdesalinationmodeling and simulationparticle swarm optimizationsoft computing techniqueswater treatment |
| spellingShingle | R. Mahadeva M. Kumar S. P. Patole G. Manik An optimized PSO-ANN model for improved prediction of water treatment desalination plant performance Water Supply artificial neural network desalination modeling and simulation particle swarm optimization soft computing techniques water treatment |
| title | An optimized PSO-ANN model for improved prediction of water treatment desalination plant performance |
| title_full | An optimized PSO-ANN model for improved prediction of water treatment desalination plant performance |
| title_fullStr | An optimized PSO-ANN model for improved prediction of water treatment desalination plant performance |
| title_full_unstemmed | An optimized PSO-ANN model for improved prediction of water treatment desalination plant performance |
| title_short | An optimized PSO-ANN model for improved prediction of water treatment desalination plant performance |
| title_sort | optimized pso ann model for improved prediction of water treatment desalination plant performance |
| topic | artificial neural network desalination modeling and simulation particle swarm optimization soft computing techniques water treatment |
| url | http://ws.iwaponline.com/content/22/3/2874 |
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