Experimental Investigation on Improvement of Wet Cooling Tower Efficiency with Diverse Packing Compaction Using ANN-PSO Algorithm
In this study, a numerical and empirical scheme for increasing cooling tower performance is developed by combining the particle swarm optimization (PSO) algorithm with a neural network and considering the packing’s compaction as an effective factor for higher accuracies. An experimental setup is use...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-12-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/14/1/167 |
| _version_ | 1797543030904848384 |
|---|---|
| author | Hasan Alimoradi Madjid Soltani Pooriya Shahali Farshad Moradi Kashkooli Razieh Larizadeh Kaamran Raahemifar Mohammad Adibi Behzad Ghasemi |
| author_facet | Hasan Alimoradi Madjid Soltani Pooriya Shahali Farshad Moradi Kashkooli Razieh Larizadeh Kaamran Raahemifar Mohammad Adibi Behzad Ghasemi |
| author_sort | Hasan Alimoradi |
| collection | DOAJ |
| description | In this study, a numerical and empirical scheme for increasing cooling tower performance is developed by combining the particle swarm optimization (PSO) algorithm with a neural network and considering the packing’s compaction as an effective factor for higher accuracies. An experimental setup is used to analyze the effects of packing compaction on the performance. The neural network is optimized by the PSO algorithm in order to predict the precise temperature difference, efficiency, and outlet temperature, which are functions of air flow rate, water flow rate, inlet water temperature, inlet air temperature, inlet air relative humidity, and packing compaction. The effects of water flow rate, air flow rate, inlet water temperature, and packing compaction on the performance are examined. A new empirical model for the cooling tower performance and efficiency is also developed. Finally, the optimized performance conditions of the cooling tower are obtained by the presented correlations. The results reveal that cooling tower efficiency is increased by increasing the air flow rate, water flow rate, and packing compaction. |
| first_indexed | 2024-03-10T13:38:53Z |
| format | Article |
| id | doaj.art-937e406a0ea34884a3362a7a8fc59120 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T13:38:53Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-937e406a0ea34884a3362a7a8fc591202023-11-21T03:12:20ZengMDPI AGEnergies1996-10732020-12-0114116710.3390/en14010167Experimental Investigation on Improvement of Wet Cooling Tower Efficiency with Diverse Packing Compaction Using ANN-PSO AlgorithmHasan Alimoradi0Madjid Soltani1Pooriya Shahali2Farshad Moradi Kashkooli3Razieh Larizadeh4Kaamran Raahemifar5Mohammad Adibi6Behzad Ghasemi7Faculty of Mechanical Engineering, K.N. Toosi University of Technology, Tehran 1996715433, IranFaculty of Mechanical Engineering, K.N. Toosi University of Technology, Tehran 1996715433, IranDepartment of Aerospace Engineering, Sharif University of Technology, Tehran 956711155, IranFaculty of Mechanical Engineering, K.N. Toosi University of Technology, Tehran 1996715433, IranFaculty of Industrial Engineering, K.N. Toosi University of Technology, Tehran 193951999, IranCollege of Information Sciences and Technology (IST) Data Science and Artificial Intelligence Program, Penn State University, Pennsylvania, PA 16801, USADepartment of Mechanical Engineering, Isfahan University, Isfahan 8174673441, IranDepartment of Mechanical Engineering, Shahrekord University, Shahrekord 8818634141, IranIn this study, a numerical and empirical scheme for increasing cooling tower performance is developed by combining the particle swarm optimization (PSO) algorithm with a neural network and considering the packing’s compaction as an effective factor for higher accuracies. An experimental setup is used to analyze the effects of packing compaction on the performance. The neural network is optimized by the PSO algorithm in order to predict the precise temperature difference, efficiency, and outlet temperature, which are functions of air flow rate, water flow rate, inlet water temperature, inlet air temperature, inlet air relative humidity, and packing compaction. The effects of water flow rate, air flow rate, inlet water temperature, and packing compaction on the performance are examined. A new empirical model for the cooling tower performance and efficiency is also developed. Finally, the optimized performance conditions of the cooling tower are obtained by the presented correlations. The results reveal that cooling tower efficiency is increased by increasing the air flow rate, water flow rate, and packing compaction.https://www.mdpi.com/1996-1073/14/1/167cooling towerpacking compactionartificial neural network (ANN)-PSOmathematical correlations |
| spellingShingle | Hasan Alimoradi Madjid Soltani Pooriya Shahali Farshad Moradi Kashkooli Razieh Larizadeh Kaamran Raahemifar Mohammad Adibi Behzad Ghasemi Experimental Investigation on Improvement of Wet Cooling Tower Efficiency with Diverse Packing Compaction Using ANN-PSO Algorithm Energies cooling tower packing compaction artificial neural network (ANN)-PSO mathematical correlations |
| title | Experimental Investigation on Improvement of Wet Cooling Tower Efficiency with Diverse Packing Compaction Using ANN-PSO Algorithm |
| title_full | Experimental Investigation on Improvement of Wet Cooling Tower Efficiency with Diverse Packing Compaction Using ANN-PSO Algorithm |
| title_fullStr | Experimental Investigation on Improvement of Wet Cooling Tower Efficiency with Diverse Packing Compaction Using ANN-PSO Algorithm |
| title_full_unstemmed | Experimental Investigation on Improvement of Wet Cooling Tower Efficiency with Diverse Packing Compaction Using ANN-PSO Algorithm |
| title_short | Experimental Investigation on Improvement of Wet Cooling Tower Efficiency with Diverse Packing Compaction Using ANN-PSO Algorithm |
| title_sort | experimental investigation on improvement of wet cooling tower efficiency with diverse packing compaction using ann pso algorithm |
| topic | cooling tower packing compaction artificial neural network (ANN)-PSO mathematical correlations |
| url | https://www.mdpi.com/1996-1073/14/1/167 |
| work_keys_str_mv | AT hasanalimoradi experimentalinvestigationonimprovementofwetcoolingtowerefficiencywithdiversepackingcompactionusingannpsoalgorithm AT madjidsoltani experimentalinvestigationonimprovementofwetcoolingtowerefficiencywithdiversepackingcompactionusingannpsoalgorithm AT pooriyashahali experimentalinvestigationonimprovementofwetcoolingtowerefficiencywithdiversepackingcompactionusingannpsoalgorithm AT farshadmoradikashkooli experimentalinvestigationonimprovementofwetcoolingtowerefficiencywithdiversepackingcompactionusingannpsoalgorithm AT raziehlarizadeh experimentalinvestigationonimprovementofwetcoolingtowerefficiencywithdiversepackingcompactionusingannpsoalgorithm AT kaamranraahemifar experimentalinvestigationonimprovementofwetcoolingtowerefficiencywithdiversepackingcompactionusingannpsoalgorithm AT mohammadadibi experimentalinvestigationonimprovementofwetcoolingtowerefficiencywithdiversepackingcompactionusingannpsoalgorithm AT behzadghasemi experimentalinvestigationonimprovementofwetcoolingtowerefficiencywithdiversepackingcompactionusingannpsoalgorithm |