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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Bibliographic Details
Published in:Advances in engineering software (1992) Vol. 175; p. 103315
Main Authors: Alsaiari, Abdulmohsen O., Moustafa, Essam B., Alhumade, Hesham, Abulkhair, Hani, Elsheikh, Ammar
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.01.2023
Subjects:
Artificial intelligence
Artificial rabbits optimizer
Industrial development
Multi-layer perceptrons
Productivity prediction
Solar still
Industrial development
Solar still
Multi-layer perceptrons
Artificial intelligence
Productivity prediction
Artificial rabbits optimizer
ISSN:0965-9978
Online Access:Get full text
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Summary:•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.
ISSN:0965-9978
DOI:10.1016/j.advengsoft.2022.103315