A coupled artificial neural network with artificial rabbits optimizer for predicting water productivity of different designs of solar stills
•A coupled multi-layer perceptrons model with artificial Rabbits optimizer is developed.•The model was employed to predict the water productivity of different designs of solar stills.•The investigated solar stills are conventional, stepped, pyramid, and tubular.•Artificial Rabbits optimizer outperfo...
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| Vydané v: | Advances in engineering software (1992) Ročník 175; s. 103315 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
01.01.2023
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| ISSN: | 0965-9978 |
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| Abstract | •A coupled multi-layer perceptrons model with artificial Rabbits optimizer is developed.•The model was employed to predict the water productivity of different designs of solar stills.•The investigated solar stills are conventional, stepped, pyramid, and tubular.•Artificial Rabbits optimizer outperformed other optimizers.
In this study, a coupled multi-layer perceptrons (MLP) model with an artificial rabbits optimizer (ARO) is developed to predict the water productivity of different designs of solar stills (SSs). The investigated SSs are conventional, stepped, pyramid, and tubular SSs. The accuracy of the developed model was compared with three other MLP models optimized with conventional optimizers, namely gradient descent algorithm, particle swarm optimizer (PSO), and genetic algorithm (GA) optimizer. All models were trained and tested using the experimental data of the four SSs under the meteorological conditions of different cities. The performance of all models was evaluated using different statistical measures for all investigated cases. The MLP-ARO outperformed the other three models. The computed root mean square deviation values for all SSs’ designs are 130.79, 57.07, 40.28, and 2.82 ml for MLP, MLP-GA, MLP-PSO, and MLP-ARO models, respectively. |
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| AbstractList | •A coupled multi-layer perceptrons model with artificial Rabbits optimizer is developed.•The model was employed to predict the water productivity of different designs of solar stills.•The investigated solar stills are conventional, stepped, pyramid, and tubular.•Artificial Rabbits optimizer outperformed other optimizers.
In this study, a coupled multi-layer perceptrons (MLP) model with an artificial rabbits optimizer (ARO) is developed to predict the water productivity of different designs of solar stills (SSs). The investigated SSs are conventional, stepped, pyramid, and tubular SSs. The accuracy of the developed model was compared with three other MLP models optimized with conventional optimizers, namely gradient descent algorithm, particle swarm optimizer (PSO), and genetic algorithm (GA) optimizer. All models were trained and tested using the experimental data of the four SSs under the meteorological conditions of different cities. The performance of all models was evaluated using different statistical measures for all investigated cases. The MLP-ARO outperformed the other three models. The computed root mean square deviation values for all SSs’ designs are 130.79, 57.07, 40.28, and 2.82 ml for MLP, MLP-GA, MLP-PSO, and MLP-ARO models, respectively. |
| ArticleNumber | 103315 |
| Author | Alhumade, Hesham Abulkhair, Hani Alsaiari, Abdulmohsen O. Moustafa, Essam B. Elsheikh, Ammar |
| Author_xml | – sequence: 1 givenname: Abdulmohsen O. surname: Alsaiari fullname: Alsaiari, Abdulmohsen O. organization: Department of Mechanical Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia – sequence: 2 givenname: Essam B. surname: Moustafa fullname: Moustafa, Essam B. organization: Department of Mechanical Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia – sequence: 3 givenname: Hesham surname: Alhumade fullname: Alhumade, Hesham organization: Chemical and Materials Engineering Department, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia – sequence: 4 givenname: Hani surname: Abulkhair fullname: Abulkhair, Hani organization: Department of Mechanical Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia – sequence: 5 givenname: Ammar orcidid: 0000-0003-0944-4938 surname: Elsheikh fullname: Elsheikh, Ammar email: ammar_elsheikh@f-eng.tanta.edu.eg organization: Production Engineering and Mechanical Design Department, Faculty of Engineering, Tanta University, Tanta, Egypt |
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| Keywords | Industrial development Solar still Multi-layer perceptrons Artificial intelligence Productivity prediction Artificial rabbits optimizer |
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