Productivity Modeling Enhancement of a Solar Desalination Unit with Nanofluids Using Machine Learning Algorithms Integrated with Bayesian Optimization
Abdallah W. Kandeal
State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology, Wuhan, 430074 China
Mechanical Engineering Department, Faculty of Engineering, Kafrelsheikh University, Kafrelsheikh, 33516 Egypt
Search for more papers by this authorCorresponding Author
Meng An
College of Mechanical and Electrical Engineering, Shaanxi University of Science and Technology, Xi'an, 710021 China
Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Department of Engineering Mechanics, Tsinghua University, Beijing, 100084 China
Search for more papers by this authorXiangquan Chen
College of Mechanical and Electrical Engineering, Shaanxi University of Science and Technology, Xi'an, 710021 China
Search for more papers by this authorAlmoataz M. Algazzar
Mechanical Engineering Department, Faculty of Engineering, Kafrelsheikh University, Kafrelsheikh, 33516 Egypt
Search for more papers by this authorAmrit Kumar Thakur
Department of Mechanical Engineering, KPR Institute Engineering and Technology, Arasur, Coimbatore, Tamil Nadu, 641407 India
Search for more papers by this authorXiaoyu Guan
College of Bioresources Chemical and Materials Engineering, National Demonstration Center for Experimental Light Chemistry Engineering Education, Shaanxi University of Science & Technology, Xi'an, 710021 P. R. China
Search for more papers by this authorJianyong Wang
College of Mechanical and Electrical Engineering, Shaanxi University of Science and Technology, Xi'an, 710021 China
Search for more papers by this authorMohamed R. Elkadeem
Electrical Power and Machines Engineering Department, Faculty of Engineering, Tanta University, Tanta, 31521 Egypt
Search for more papers by this authorCorresponding Author
Weigang Ma
Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Department of Engineering Mechanics, Tsinghua University, Beijing, 100084 China
Search for more papers by this authorCorresponding Author
Swellam W. Sharshir
State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology, Wuhan, 430074 China
Mechanical Engineering Department, Faculty of Engineering, Kafrelsheikh University, Kafrelsheikh, 33516 Egypt
Search for more papers by this authorAbdallah W. Kandeal
State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology, Wuhan, 430074 China
Mechanical Engineering Department, Faculty of Engineering, Kafrelsheikh University, Kafrelsheikh, 33516 Egypt
Search for more papers by this authorCorresponding Author
Meng An
College of Mechanical and Electrical Engineering, Shaanxi University of Science and Technology, Xi'an, 710021 China
Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Department of Engineering Mechanics, Tsinghua University, Beijing, 100084 China
Search for more papers by this authorXiangquan Chen
College of Mechanical and Electrical Engineering, Shaanxi University of Science and Technology, Xi'an, 710021 China
Search for more papers by this authorAlmoataz M. Algazzar
Mechanical Engineering Department, Faculty of Engineering, Kafrelsheikh University, Kafrelsheikh, 33516 Egypt
Search for more papers by this authorAmrit Kumar Thakur
Department of Mechanical Engineering, KPR Institute Engineering and Technology, Arasur, Coimbatore, Tamil Nadu, 641407 India
Search for more papers by this authorXiaoyu Guan
College of Bioresources Chemical and Materials Engineering, National Demonstration Center for Experimental Light Chemistry Engineering Education, Shaanxi University of Science & Technology, Xi'an, 710021 P. R. China
Search for more papers by this authorJianyong Wang
College of Mechanical and Electrical Engineering, Shaanxi University of Science and Technology, Xi'an, 710021 China
Search for more papers by this authorMohamed R. Elkadeem
Electrical Power and Machines Engineering Department, Faculty of Engineering, Tanta University, Tanta, 31521 Egypt
Search for more papers by this authorCorresponding Author
Weigang Ma
Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Department of Engineering Mechanics, Tsinghua University, Beijing, 100084 China
Search for more papers by this authorCorresponding Author
Swellam W. Sharshir
State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology, Wuhan, 430074 China
Mechanical Engineering Department, Faculty of Engineering, Kafrelsheikh University, Kafrelsheikh, 33516 Egypt
Search for more papers by this authorAbstract
Herein, double slope solar still (DSSS) performance is accurately forecast with the aid of four different machine learning (ML) models, namely, artificial neural network (ANN), random forest (RF), support vector regression (SVR), and linear SVR. Furthermore, the tuning of ML models is optimized using the Bayesian optimization algorithm (BOA) to get the optimal performance of all models and identify the best predictive one. All the models are trained, tested, and validated depending on experimental data acquired under Egyptian climatic conditions. The results reveal that ML models can be a powerful tool to forecast DSSS performance. Among them, RF is the most potent ML model obtaining the highest determination coefficient (R2) and the lowest absolute error percentage of 0.997% and 2.95%, respectively. Furthermore, the experimental results also show that the mean value of accumulated (daily) freshwater productivity from DSSS is 4.3 L m−2.
Conflict of Interest
The authors declare no conflict of interest.
Open Research
Data available on request from the authors.
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