Using multiple machine learning techniques to enhance the performance prediction of heat pump-driven solar desalination unit
Solar stills are sustainable devices that generate freshwater through solar-powered desalination. However, traditional solar stills often struggle with variability in environmental conditions. This study proposes a predictive model using machine learning (ML) techniques to improve the accuracy and a...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
|
| Series: | Desalination and Water Treatment |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1944398624204264 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850077248402489344 |
|---|---|
| author | Swellam W. Sharshir Abanob Joseph Mohamed S. Abdalzaher A.W. Kandeal A.S. Abdullah Zhanhui Yuan Huizhong Zhao Mahmoud M. Salim |
| author_facet | Swellam W. Sharshir Abanob Joseph Mohamed S. Abdalzaher A.W. Kandeal A.S. Abdullah Zhanhui Yuan Huizhong Zhao Mahmoud M. Salim |
| author_sort | Swellam W. Sharshir |
| collection | DOAJ |
| description | Solar stills are sustainable devices that generate freshwater through solar-powered desalination. However, traditional solar stills often struggle with variability in environmental conditions. This study proposes a predictive model using machine learning (ML) techniques to improve the accuracy and adaptability of active solar still performance. The study focuses on a modern heat pump-operated solar still, where the heat pump's cold side facilitates condensation, and its condenser provides additional heat to complement solar radiation. Furthermore, five ML regressors namely, extra trees (ET), adaptive boosting (Adaboost), random forest (RF), K-nearest neighbors (KNN), and light gradient boosting (LGB) are employed to model and forecast cumulative yield, total exergy, and thermal efficiencies. Besides, four separate train-test splits, 95 %:5 %, 90 %:10 %, 80 %:20 %, and 70 %:30 %, are employed to assess the performance of each regressor in terms of R-squared (R2), mean squared error (MSE), and mean absolute error (MAE). All the models showed prediction accuracy enhancement with increasing the train dataset size. The best perdition accuracy was achieved by the Extra Trees model as the model exhibited MSE of 0.0020, 0.0027, and 0.0033 for total yield, total exergy, and thermal efficiencies forecasting, respectively. |
| format | Article |
| id | doaj-art-4fc9fb407ddb4b32acc95cdfcf9d1ac9 |
| institution | DOAJ |
| issn | 1944-3986 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Desalination and Water Treatment |
| spelling | doaj-art-4fc9fb407ddb4b32acc95cdfcf9d1ac92025-08-20T02:45:50ZengElsevierDesalination and Water Treatment1944-39862025-01-0132110091610.1016/j.dwt.2024.100916Using multiple machine learning techniques to enhance the performance prediction of heat pump-driven solar desalination unitSwellam W. Sharshir0Abanob Joseph1Mohamed S. Abdalzaher2A.W. Kandeal3A.S. Abdullah4Zhanhui Yuan5Huizhong Zhao6Mahmoud M. Salim7College of Materials Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China; Mechanical Engineering Department, Faculty of Engineering, Kafrelsheikh University, Kafrelsheikh 33516, Egypt; Corresponding author at: College of Materials Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China.Mechanical Engineering Department, Faculty of Engineering, Kafrelsheikh University, Kafrelsheikh 33516, EgyptSeismology Department, National Research Institute of Astronomy and Geophysics, Helwan, Cairo 11421, Egypt; Electrical Engineering Department, Collage of Engineering, American University of Sharjah, Sharjah, United Arab Emirates; Corresponding author at: Seismology Department, National Research Institute of Astronomy and Geophysics, Helwan, Cairo 11421, Egypt.Mechanical Engineering Department, Faculty of Engineering, Kafrelsheikh University, Kafrelsheikh 33516, EgyptMechanical Power Engineering Department, Faculty of Engineering, Tanta University, Tanta City, EgyptCollege of Materials Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China; Corresponding author.Merchant Marine College, Shanghai Maritime University, Shanghai 201306, ChinaDepartment of Electronics and Electrical Communications, October 6 University, 6 of October City, 12585 Giza, Egypt; Center for Communications Systems and Sensing, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran 31261, Saudi ArabiaSolar stills are sustainable devices that generate freshwater through solar-powered desalination. However, traditional solar stills often struggle with variability in environmental conditions. This study proposes a predictive model using machine learning (ML) techniques to improve the accuracy and adaptability of active solar still performance. The study focuses on a modern heat pump-operated solar still, where the heat pump's cold side facilitates condensation, and its condenser provides additional heat to complement solar radiation. Furthermore, five ML regressors namely, extra trees (ET), adaptive boosting (Adaboost), random forest (RF), K-nearest neighbors (KNN), and light gradient boosting (LGB) are employed to model and forecast cumulative yield, total exergy, and thermal efficiencies. Besides, four separate train-test splits, 95 %:5 %, 90 %:10 %, 80 %:20 %, and 70 %:30 %, are employed to assess the performance of each regressor in terms of R-squared (R2), mean squared error (MSE), and mean absolute error (MAE). All the models showed prediction accuracy enhancement with increasing the train dataset size. The best perdition accuracy was achieved by the Extra Trees model as the model exhibited MSE of 0.0020, 0.0027, and 0.0033 for total yield, total exergy, and thermal efficiencies forecasting, respectively.http://www.sciencedirect.com/science/article/pii/S1944398624204264Active solar stillHeat pumpSustainabilityMachine learningPrediction modellingExtra trees |
| spellingShingle | Swellam W. Sharshir Abanob Joseph Mohamed S. Abdalzaher A.W. Kandeal A.S. Abdullah Zhanhui Yuan Huizhong Zhao Mahmoud M. Salim Using multiple machine learning techniques to enhance the performance prediction of heat pump-driven solar desalination unit Desalination and Water Treatment Active solar still Heat pump Sustainability Machine learning Prediction modelling Extra trees |
| title | Using multiple machine learning techniques to enhance the performance prediction of heat pump-driven solar desalination unit |
| title_full | Using multiple machine learning techniques to enhance the performance prediction of heat pump-driven solar desalination unit |
| title_fullStr | Using multiple machine learning techniques to enhance the performance prediction of heat pump-driven solar desalination unit |
| title_full_unstemmed | Using multiple machine learning techniques to enhance the performance prediction of heat pump-driven solar desalination unit |
| title_short | Using multiple machine learning techniques to enhance the performance prediction of heat pump-driven solar desalination unit |
| title_sort | using multiple machine learning techniques to enhance the performance prediction of heat pump driven solar desalination unit |
| topic | Active solar still Heat pump Sustainability Machine learning Prediction modelling Extra trees |
| url | http://www.sciencedirect.com/science/article/pii/S1944398624204264 |
| work_keys_str_mv | AT swellamwsharshir usingmultiplemachinelearningtechniquestoenhancetheperformancepredictionofheatpumpdrivensolardesalinationunit AT abanobjoseph usingmultiplemachinelearningtechniquestoenhancetheperformancepredictionofheatpumpdrivensolardesalinationunit AT mohamedsabdalzaher usingmultiplemachinelearningtechniquestoenhancetheperformancepredictionofheatpumpdrivensolardesalinationunit AT awkandeal usingmultiplemachinelearningtechniquestoenhancetheperformancepredictionofheatpumpdrivensolardesalinationunit AT asabdullah usingmultiplemachinelearningtechniquestoenhancetheperformancepredictionofheatpumpdrivensolardesalinationunit AT zhanhuiyuan usingmultiplemachinelearningtechniquestoenhancetheperformancepredictionofheatpumpdrivensolardesalinationunit AT huizhongzhao usingmultiplemachinelearningtechniquestoenhancetheperformancepredictionofheatpumpdrivensolardesalinationunit AT mahmoudmsalim usingmultiplemachinelearningtechniquestoenhancetheperformancepredictionofheatpumpdrivensolardesalinationunit |