Improving the performance of a condensation water production system through support vector machine modeling and genetic algorithm optimization
Water scarcity is recognized as a critical global concern and one viable solution involves extracting water from atmospheric humidity by leveraging subterranean coldness. This study meticulously evaluates the operational efficacy of a water production system by examining four pivotal factors: buried...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IWA Publishing
2024-03-01
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| Series: | Water Supply |
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| Online Access: | http://ws.iwaponline.com/content/24/3/847 |
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| author | Shayan Hajinajaf Shaban Ghavami Jolandan Hassan Masoudi Abbas Rohani |
| author_facet | Shayan Hajinajaf Shaban Ghavami Jolandan Hassan Masoudi Abbas Rohani |
| author_sort | Shayan Hajinajaf |
| collection | DOAJ |
| description | Water scarcity is recognized as a critical global concern and one viable solution involves extracting water from atmospheric humidity by leveraging subterranean coldness. This study meticulously evaluates the operational efficacy of a water production system by examining four pivotal factors: buried pipe length (TL), air flow rate (AFR), air temperature (AT), and air humidity (AH). A positive correlation between these variables and water vapor production is established, with AT exerting the most pronounced influence. Significantly, the analysis of variance reveals the main and interactive effects of the variables, except for TL × AFR, at a 5% significance level. To enhance understanding of the intricate interplay among these factors, a proficient least squares support vector machines model is devised, employing a radial basis function kernel. This model demonstrates an impressive 98% concurrence between projected and empirical data, with a minimal error of 0.66 mL and 5.99%. An in-depth sensitivity analysis underscores the differential impact of AT, AH, TL, and AFR on water vapor (WV) prediction. Optimal values of 3.98 m, 6.89 m3/h, 46.30 °C, and 86.62% for TL, AFR, AT, and AH, respectively, are obtained through subsequent optimization of independent variables using genetic algorithms, resulting in a notable water production of 23.61 mL.
HIGHLIGHTS
Air humidity can address rural water scarcity.;
Air temperature, humidity, flow rate, and pipe length affect water volume production.;
The support vector machine model with radial basis function kernel is the best solution for predicting water volume.;
The least squares support vector machines model shows a 98% agreement between predicted and experimental data.;
Genetic algorithms resulted in optimized independent variable values and 23.61 mL water production.; |
| first_indexed | 2024-04-24T07:35:00Z |
| format | Article |
| id | doaj.art-4a7308c2429844aba9bf07c4ffd70aeb |
| institution | Directory Open Access Journal |
| issn | 1606-9749 1607-0798 |
| language | English |
| last_indexed | 2024-04-24T07:35:00Z |
| publishDate | 2024-03-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Water Supply |
| spelling | doaj.art-4a7308c2429844aba9bf07c4ffd70aeb2024-04-20T06:36:22ZengIWA PublishingWater Supply1606-97491607-07982024-03-0124384786410.2166/ws.2024.034034Improving the performance of a condensation water production system through support vector machine modeling and genetic algorithm optimizationShayan Hajinajaf0Shaban Ghavami Jolandan1Hassan Masoudi2Abbas Rohani3 Department of Biosystems Engineering, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran Department of Biosystems Engineering, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran Department of Biosystems Engineering, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran Department of Biosystems Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran Water scarcity is recognized as a critical global concern and one viable solution involves extracting water from atmospheric humidity by leveraging subterranean coldness. This study meticulously evaluates the operational efficacy of a water production system by examining four pivotal factors: buried pipe length (TL), air flow rate (AFR), air temperature (AT), and air humidity (AH). A positive correlation between these variables and water vapor production is established, with AT exerting the most pronounced influence. Significantly, the analysis of variance reveals the main and interactive effects of the variables, except for TL × AFR, at a 5% significance level. To enhance understanding of the intricate interplay among these factors, a proficient least squares support vector machines model is devised, employing a radial basis function kernel. This model demonstrates an impressive 98% concurrence between projected and empirical data, with a minimal error of 0.66 mL and 5.99%. An in-depth sensitivity analysis underscores the differential impact of AT, AH, TL, and AFR on water vapor (WV) prediction. Optimal values of 3.98 m, 6.89 m3/h, 46.30 °C, and 86.62% for TL, AFR, AT, and AH, respectively, are obtained through subsequent optimization of independent variables using genetic algorithms, resulting in a notable water production of 23.61 mL. HIGHLIGHTS Air humidity can address rural water scarcity.; Air temperature, humidity, flow rate, and pipe length affect water volume production.; The support vector machine model with radial basis function kernel is the best solution for predicting water volume.; The least squares support vector machines model shows a 98% agreement between predicted and experimental data.; Genetic algorithms resulted in optimized independent variable values and 23.61 mL water production.;http://ws.iwaponline.com/content/24/3/847condensationgenetic algorithmsupport vector machinewater production system |
| spellingShingle | Shayan Hajinajaf Shaban Ghavami Jolandan Hassan Masoudi Abbas Rohani Improving the performance of a condensation water production system through support vector machine modeling and genetic algorithm optimization Water Supply condensation genetic algorithm support vector machine water production system |
| title | Improving the performance of a condensation water production system through support vector machine modeling and genetic algorithm optimization |
| title_full | Improving the performance of a condensation water production system through support vector machine modeling and genetic algorithm optimization |
| title_fullStr | Improving the performance of a condensation water production system through support vector machine modeling and genetic algorithm optimization |
| title_full_unstemmed | Improving the performance of a condensation water production system through support vector machine modeling and genetic algorithm optimization |
| title_short | Improving the performance of a condensation water production system through support vector machine modeling and genetic algorithm optimization |
| title_sort | improving the performance of a condensation water production system through support vector machine modeling and genetic algorithm optimization |
| topic | condensation genetic algorithm support vector machine water production system |
| url | http://ws.iwaponline.com/content/24/3/847 |
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