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

Nazari, Mohammad Alhuyi; Salem, Mohamed; Mahariq, İbrahim; Younes, Khaled; Maqableh, Bashar B.

doi:10.3389/fenrg.2021.742615

Cited by 32 publications

(15 citation statements)

References 52 publications

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“…A comprehensive study on the applications of different data-driven approaches in the performance modeling of solar units is introduced by Alhuyi Nazari et al [152]. They are also in other studies on solar energy [153,154], reviews on the applications of multi-criteria decision-making approaches for power plant site selection.…”

Section: Solar Power Plants In Iranmentioning

confidence: 99%

Performance Evaluation of Solar Power Plants: A Review and a Case Study

et al. 2021

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The world’s electricity generation has increased with renewable energy technologies such as solar (solar power plant), wind energy (wind turbines), heat energy, and even ocean waves. Iran is in the best condition to receive solar radiation due to its proximity to the equator (25.2969° N). In 2020, Iran was able to supply only 900 MW (about 480 solar power plants and 420 MW home solar power plants) of its electricity demand from solar energy, which is very low compared to the global average. Yazd, Fars, and Kerman provinces are in the top ranks of Iran, with the production of approximately 68, 58, and 47 MW using solar energy, respectively. Iran also has a large area of vacant land for the construction of solar power plants. In this article, the amount of electricity generation using solar energy in Iran is studied. In addition, the construction of a 10 MW power plant in the city of Sirjan is economically and technically analyzed. The results show that with US$16.14 million, a solar power plant can be built in the Sirjan region, and the initial capital will be returned in about four years. The results obtained using Homer software show that the highest maximum power generation is in July.

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Section: Solar Power Plants In Iranmentioning

confidence: 99%

Performance Evaluation of Solar Power Plants: A Review and a Case Study

et al. 2021

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“…Thus, the subsidence information must be predicted using historical and precise leveling data. Subsidence prediction methods currently include fuzzy theory (Choi et al, 2010; Malinowska, 2011), empirical methods (Li et al, 2019), and time functions (Gonzalez-Nicieza et al, 2007). Such theories and formulas usually have countless theoretical assumptions, which limit their accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Artificial intelligence (AI) methods have been widely used in different scientific and engineering fields (Alhuyi Nazar et al, 2021; Rashidi et al, 2021, 2022), and ML is one of the important research areas of intelligent methods. As an alternative, GSM point predictive models employing ML algorithms have been applied.…”

Section: Introductionmentioning

confidence: 99%

Improved mining subsidence prediction model for high water level area using machine learning and chaos theory

Yang

Chen

Fang

et al. 2022

Energy Exploration & Exploitation

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Ground surface monitoring (GSM) points collect information for mining surface subsidence monitoring and environmental governance. However, GSM points submerge in high groundwater mining areas, preventing the collection of monitoring data. The application of machine learning (ML) algorithms to subsidence prediction ignores the uncertainty and irregularity in subsidence changes. Thus, an innovative GSM point information prediction model, which improves the multikernel support vector machine (GA-MK-SVM) using chaos residual theory commonly used for capturing GSM point information, is proposed. The mean relative errors (MREs) between the predicted and observed results of GA-SVM and GA-MK-SVM were 8.2% and 6.1% during active periods, respectively. The GA-MK-SVM also performed better than the GA-SVM during stable periods. The residual error accumulates as the ML algorithms progress, resulting in imprecise predictions of the GSM points. Thus, the GA-MK-SVM model was improved using chaotic theory (Chaos-GA-MK-SVM), with MREs of 5.0% and 0.9% during the active and stable periods, respectively. The accuracy of the proposed model was improved by 1.1% and 3.2% compared with the unimproved GA-MK-SVM, respectively. The proposed approach provides practical GSM point information for mining subsidence studies and governance in high groundwater mines.

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“…Energy sources such as fossil fuels can be used to provide energy according to the demand of people, which means they are readily storable when not needed. However, renewable energy like solar and wind energy needs to be harvested when available and stored until required 1,2 . Thermal energy storage (TES) is an appropriate solution to solve the unbalance between energy demand and supply 3,4 .…”

Section: Introductionmentioning

confidence: 99%

Experimental study of metal‐oxide nanoparticles on the thermal properties of erythritol for thermal energy storage

Zhou

et al. 2022

Asia-Pacific J Chem Eng

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Erythritol has attracted wide attention due to high latent heat of 297.2 kJ/kg and excellent chemical stability as a medium-to low-temperature thermal energy storage (TES) material. However, low thermal conductivity leads to extended charging and discharging time and severely affects its large-scale application. In this article, three cheap nanoparticles (CuO, Al 2 O 3 , and Fe 2 O 3 ) and triethanolamine (TEA) were used as heat transfer enhancers and dispersant to improve the TES performance of erythritol. Sedimentation of nanoparticles in erythritol indicates that TEA is an effective dispersant in erythritol for three kinds of nanoparticles. The experimental results show that the thermal conductivity increased from 0.671 W/(mÁK) of erythritol to 0.722 W/(mÁK) and 0.761 W/(mÁK) of 1.5 wt% CuO and Fe 2 O 3 nano-enhanced phase change materials (NePCMs), respectively. Based on the DSC results, the melting temperature and heat of fusion of NePCMs stayed stable with nanoparticles but increased significantly after adding TEA. Furthermore, the charging/discharging cyclic behaviors of NePCMs were tested in the cycle test platform. During the cycle test, the melting time of 1.5 wt% CuO NePCM decreased from 657.9 s for pure erythritol to 615.8 s but increased to 757 s and 680.6 s for 1.5 wt% Al 2 O 3 and 1.5 wt% Fe 2 O 3 NePCMs. The solidification time decreased from 311.2 s for erythritol to 179.3 s, 198.5 s, and 132.4 s for 1.5 wt% CuO, 1.5 wt% Al 2 O 3 , and 1.5 wt% Fe 2 O 3 NePCMs. Interestingly, the supercooling degree of NePCMs we got from the test was much lower than that from the DSC curves, indicating that the supercooling is related to the sample size.These results testify that CuO and Fe 2 O 3 nanoparticles can improve the thermal conductivity of erythritol effectively.

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

Cited by 32 publications

References 52 publications

Performance Evaluation of Solar Power Plants: A Review and a Case Study

Performance Evaluation of Solar Power Plants: A Review and a Case Study

Improved mining subsidence prediction model for high water level area using machine learning and chaos theory

Experimental study of metal‐oxide nanoparticles on the thermal properties of erythritol for thermal energy storage

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