Productivity Modeling Enhancement of a Solar Desalination Unit with Nanofluids Using Machine Learning Algorithms Integrated with Bayesian Optimization

Kandeal, A.W.; An, Meng; Chen, Xiangquan; Algazzar, Almoataz M.; Thakur, Amrit Kumar; Guan, Xiaoyu; Wang, Jianyong; Elkadeem, M.R.; Ma, Weigang; Sharshir, Swellam W.

doi:10.1002/ente.202100189

Cited by 20 publications

(4 citation statements)

References 71 publications

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“…The model was then measured for accuracy based on the selected statistical indicators: standard error of the regression (S), Durbin-Watson statistic (d), variance inflation factor (VIF), Mallows Cp, probability value (p-value), coefficient of determination (R 2 ), adjusted-R squared (R 2 a ), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). These parameters were calculated using the following equations [17,39,40]:…”

Section: Discussionmentioning

confidence: 99%

“…TMY data is considered to be a reliable representation of the weather pattern of Dongji Islet since it is based on the collection of weather data over a long period of time. The multivariable linear regression model, Equation (17), was developed with R-squared (R 2 ), adjusted R-squared (R 2 a ), MAE, RMSE, and MAPE values of 99.5%, 99.4%, 0.144, 0.167, and 9.71%, respectively. This study also showed that the meteorological parameters of daily total global solar radiation, ambient temperature, and extent of cloud cover were the most appropriate variables for predicting the productivity of the prototype solar still.…”

Section: Discussionmentioning

confidence: 99%

“…Such models vary in accuracy, complexity, costs, time for development, technology, and technical expertise. Common approaches taken by researchers to model the productive performance of a solar still include thermal [9][10][11][12][13][14], intelligent [15][16][17][18], and statistical modelling [19][20][21][22][23][24][25][26][27][28][29]. For the purpose of this study, the statistical modelling approach was adopted.…”

Section: Introductionmentioning

confidence: 99%

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Statistical Model for the Sizing of a Prototype Solar Still Applicable to Remote Islands

Samuel

Chang

2022

Water

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The topography and location of many remote islands limit the available freshwater resources present for use by the inhabitants. However, the abundance of solar and seawater resources and small population size makes them ideal candidates for solar still application. A prototype solar still was designed and fabricated for use on such applications; however, for its implementation, a statistical model was developed to assess its productive performance at a pilot location, Dongji Islet. Experiments were conducted to collect data and construct a multivariable regression model by means of the jack-knife procedure and best subsets technique. The model was then used to size the solar still for implementation by applying the TMY data of Dongji Islet. The daily total global solar radiation, average ambient temperature, and extent of cloud cover were found to be the most suitable predictor variables for the model based on their correlation to the productivity of the protype solar still and their p-value. The model predicted a maximum daily yield of 5.88 L/day in July and a minimum of 1.97 L/day in December. In relation to the annual predicted yield, the length of the solar still can be increased by 88.6% in order to satisfy the daily minimum requirement of 7.5 L per day per person.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Statistical Model for the Sizing of a Prototype Solar Still Applicable to Remote Islands

Samuel

Chang

2022

Water

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“…Intelligent methods can be applied for accurate evaluation of these solar stills. Kandeal et al (2021) tested various data-driven methods including ANN, Support Vector Regression (SVR), linear SVR, and RF to model the performance of a doubleslope solar still utilizing the carbon black nanofluid in 1.5% wt concentration. The inputs of the proposed model were air ambient temperature, solar radiation, wind speed, vapor temperature, basin temperature, and temperatures at the glass inlet and outlet.…”

Section: Applications Of Data-driven Methods In Solar Desalinationsmentioning

confidence: 99%

Utilization of Data-Driven Methods in Solar Desalination Systems: A Comprehensive Review

Nazari¹,

Salem²,

Mahariq³

et al. 2021

Front. Energy Res.

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Renewable energy sources have been used for desalination by employing different technologies and mediums due to the limitations of fossil fuels and the environmental issues related to their consumption. Solar energy is one of the most applicable types of renewable sources for desalination in both direct and indirect ways. The performance of solar desalination is under effects of different factors which makes their performance prediction difficult in some cases. In this regard, data-driven methods such as artificial neural networks (ANNs) would be proper tools for their modeling and output forecasting. In the present article, a comprehensive review is provided on the applications of different data-driven approaches in performance modeling of solar-based desalination units. It can be concluded that by employing these methods with proper inputs and structures, the outputs of the solar desalination units can be reliably and accurately forecasted. In addition, several recommendations are produced for the upcoming work in the relevant areas of the study.

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Experimental Investigation for Enhancement of Solar Still Performance for Wastewater Treatment with the Influence of Encapsulated Phase Change Material

Sharma,

Tyagi,

Chopra

et al. 2024

Energy Tech

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This research aims to investigate the single‐slope solar still for treating industrial wastewater using stearic acid as a phase change material. The phase change material is macroencapsulated in aluminum bottles. The experiment is run at 50% wastewater depth with 9 kg (scenario 1), 12 kg (scenario 2), and 15 kg (scenario 3) mass of stearic acid and without stearic acid (scenario 4). The total output productivity of 2247, 1600, and 1359 mL m−2 d−1 is achieved in scenarios 1, 2, and 3, respectively, out of which evening time productivity is 24.47%, 22.18%, and 28.39% increasing the operational time by 5 h. The system is well efficient in reducing total dissolved solids and total kjeldahl nitrogen and chemical oxygen demand with an efficiency of 90.45%, 98.57%, and 97.89%, respectively. A notable increase of 78.92%, 36.63%, and 32.67% in thermal efficiency is attained using 9, 12, and 15 kg mass of stearic acid respectively compared to traditional solar still. Comparing scenarios based on thermal efficiency, it can be concluded that thermal efficiency and mass of phase change material are inversely related and lower masses show better performance efficiency.

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Productivity Modeling Enhancement of a Solar Desalination Unit with Nanofluids Using Machine Learning Algorithms Integrated with Bayesian Optimization

Cited by 20 publications

References 71 publications

Statistical Model for the Sizing of a Prototype Solar Still Applicable to Remote Islands

Statistical Model for the Sizing of a Prototype Solar Still Applicable to Remote Islands

Utilization of Data-Driven Methods in Solar Desalination Systems: A Comprehensive Review

Experimental Investigation for Enhancement of Solar Still Performance for Wastewater Treatment with the Influence of Encapsulated Phase Change Material

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