Predicting the performance of solar dish Stirling power plant using a hybrid random vector functional link/chimp optimization model

Zayed, Mohamed E.; Zhao, Jianlin; Li, Wenjia; Elsheikh, Ammar H.; Elaziz, Mohamed Abd; Yousri, Dalia; Zhong, Shengyuan; Mingxi, Zhu

doi:10.1016/j.solener.2021.03.087

Cited by 67 publications

(14 citation statements)

References 57 publications

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“…Therefore, by linking it, solar energy is used for power production or heating source for seawater desalination methods such as reverse osmosis and multi-effect evaporators [3][4][5][6][7][8][9]. Although the reverse osmosis technique has the highest freshwater production, it consumes much power [10][11][12][13][14][15][16][17][18][19][20]. This requires finding other sources for desalination of seawater with low energy and relatively high productivity.…”

Section: Symbolsmentioning

confidence: 99%

Modeling and Energy Analysis of the Hybrid Performance of Solar Dish Concentrator Integrated with Multi-Effect Desalination Unit

Aboelmaaref

Zhao

Zayed

et al. 2023

Journal of Advanced Engineering Trends

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In this study, a hybrid commercial solar dish concentrator (SDC) integrated with a multi-effect distillation (MED) unit is thermodynamically modeled for the desalination process. A mathematical model implemented in MATLAB software is developed to simulate the proposed SDC/MED system to analyze its performance under the conditions of Tianjin (TJ), China. An energy analysis viability for assessing the life cycle of the SDC/MED system is also performed. The results demonstrated that, despite the site's solar radiation changes throughout the day, the desalinated water output for the proposed SDC/MED system is sufficient. In addition, the TJ-SDC/MED system is more cost-efficient than comparable standalone desalination systems. The simulation of the SDC with the MED unit of five effects yielded average productivity of roughly 95.5 kg/s, a specific cooling water flow rate of 1.689, and an overall performance ratio of 223.3. It is recommended that the hybrid SDC/MED system is an efficient choice for producing potable water in large scale desalination applications.

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Section: Symbolsmentioning

confidence: 99%

Modeling and Energy Analysis of the Hybrid Performance of Solar Dish Concentrator Integrated with Multi-Effect Desalination Unit

Aboelmaaref

Zhao

Zayed

et al. 2023

Journal of Advanced Engineering Trends

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“…However, exhaustive grid search has several drawbacks. Some literature implements evolutionary algorithms for hyper-parameters optimization, such as levy flight based PSO [107] and chimp optimization [106]. The evolutionary algorithms encode the hyper-parameters into an individual, obtaining the optimal configuration after many generations.…”

Section: Hyper-parameters Optimization For Single-layer Rvflmentioning

confidence: 99%

“…For instance, in [180], the multikernel RVFL whose kernel parameters are optimized by water cycle algorithm is proposed to forecast short-term solar power. In [106], Chimp Optimization Algorithm (CHOA) is utilized to determine RVFL's hyper-parameters for predicting output power and the monthly power production of a solar dish/Stirling power plant. Some researchers integrate signal decomposition, evolutionary optimization, and RVFL together to boost forecasting accuracy.…”

Section: Solar Powermentioning

confidence: 99%

Random vector functional link network: recent developments, applications, and future directions

Malik¹,

Gao²,

Ganaie³

et al. 2022

Preprint

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Neural networks have been successfully employed in various domain such as classification, regression and clustering, etc. Generally, the back propagation (BP) based iterative approaches are used to train the neural networks, however, it results in the issues of local minima, sensitivity to learning rate and slow convergence. To overcome these issues, randomization based neural networks such as random vector functional link (RVFL) network have been proposed. RVFL model has several characteristics such as fast training speed, simple architecture, and universal approximation capability, that make it a viable randomized neural network. This article presents the comprehensive review of the development of RVFL model, which can serve as the extensive summary for the beginners as well as practitioners. We discuss the shallow RVFL, ensemble RVFL, deep RVFL and ensemble deep RVFL models. The variations, improvements and applications of RVFL models are discussed in detail. Moreover, we discuss the different hyperparameter optimization techniques followed in the literature to improve the generalization performance of the RVFL model. Finally, we give potential future research directions/opportunities that can inspire the researchers to improve the RVFL architecture further.

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“…Prediction of the joint characteristics produced by LTW has significant importance in optimizing the joint quality. Artificial intelligence tools have been reported as efficient tools to model and predict the response of different engineering processes such as metal cutting [ 30 ], desalination systems [ 31 ], power generation plants [ 32 ], material processing [ 33 ], wastewater treatment plant [ 34 ], solar systems [ 35 ], and heat exchangers [ 36 ]. They also performed well in the modeling manufacturing process of polymeric materials such as friction stir welding [ 37 ], ultrasonic welding [ 38 ], and laser cutting of polymers [ 39 ].…”

Section: Introductionmentioning

confidence: 99%

Predicting Characteristics of Dissimilar Laser Welded Polymeric Joints Using a Multi-Layer Perceptrons Model Coupled with Archimedes Optimizer

Moustafa

Elsheikh

2023

Polymers

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This study investigates the application of a coupled multi-layer perceptrons (MLP) model with Archimedes optimizer (AO) to predict characteristics of dissimilar lap joints made of polymethyl methacrylate (PMMA) and polycarbonate (PC). The joints were welded using the laser transmission welding (LTW) technique equipped with a beam wobbling feature. The inputs of the models were laser power, welding speed, pulse frequency, wobble frequency, and wobble width; whereas, the outputs were seam width and shear strength of the joint. The Archimedes optimizer was employed to obtain the optimal internal parameters of the multi-layer perceptrons. In addition to the Archimedes optimizer, the conventional gradient descent technique, as well as the particle swarm optimizer (PSO), was employed as internal optimizers of the multi-layer perceptrons model. The prediction accuracy of the three models was compared using different error measures. The AO-MLP outperformed the other two models. The computed root mean square errors of the MLP, PSO-MLP, and AO-MLP models are (39.798, 19.909, and 2.283) and (0.153, 0.084, and 0.0321) for shear strength and seam width, respectively.

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Predicting the performance of solar dish Stirling power plant using a hybrid random vector functional link/chimp optimization model

Cited by 67 publications

References 57 publications

Modeling and Energy Analysis of the Hybrid Performance of Solar Dish Concentrator Integrated with Multi-Effect Desalination Unit

Modeling and Energy Analysis of the Hybrid Performance of Solar Dish Concentrator Integrated with Multi-Effect Desalination Unit

Random vector functional link network: recent developments, applications, and future directions

Predicting Characteristics of Dissimilar Laser Welded Polymeric Joints Using a Multi-Layer Perceptrons Model Coupled with Archimedes Optimizer

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