First principles versus artificial neural network modelling of a solar desalination system with experimental validation

Bagheri, Ali; Esfandiari, Nadia; Honarvar, Bizhan; Azdarpour, Amin

doi:10.1080/13873954.2020.1788609

Cited by 13 publications

(2 citation statements)

References 21 publications

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“…Solar desalination can be integrated with other components to reach higher productivity. Data-driven methods are applicable for performance forecasting of these systems (Bagheri et al, 2020). As an example, Bagheri et al ( 2021) used ANN to model a solar desalination system composed of PVs, a heater, a battery, a cylindrical parabolic collector, etc.…”

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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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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show abstract

“…However, the energy payback time, or the time it took to recover the energy invested in constructing the system, was found to be relatively lengthy. In another investigation [10], a solar still was enhanced by employing both cylindrical parabolic collectors and solar panels. The solar panels, with a capacity of 300 W, were utilized to heat saline water through thermal elements positioned outside the solar still unit.…”

Section: Related Workmentioning

confidence: 99%

Integration of IoT Technologies for Enhanced Monitoring and Control in Hybrid-Powered Desalination Systems: A Sustainable Approach to Freshwater Production

Odeh,

Ishaq

2024

IoT

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In the face of our rapidly expanding global population, the necessity of meeting the fundamental needs of every individual is more pressing than ever. Human survival depends upon access to water, making it a vital resource that demands novel solutions to ensure universal availability. Although our planet is abundant in water, 97.5% of it is saltwater, compelling nations to investigate ways to make it suitable for consumption. Seawater desalination is becoming increasingly vital for water sustainability. While seawater desalination offers a solution, existing methods often grapple with high energy consumption and maintaining consistent water quality. This paper proposes a novel hybrid water desalination system that addresses these limitations. Our system leverages solar energy, a readily available renewable resource, to power the desalination process, significantly improving its environmental footprint and operational efficiency. Additionally, we integrated a network of sensors and the Internet of Things (IoT) to enable the real-time monitoring of system performance and water quality. This allows for the immediate detection and improvement in any potential issues, ensuring the consistent production of clean drinking water. By combining solar energy with robust quality control via IoT, our hybrid desalination system offers a sustainable and reliable approach to meet the growing demand for freshwater.

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Performance estimation of tubular solar still with a wicked rotating drum using DT, LR, and KNN techniques of machine learning

Saravanan

Parida

Murugan

et al. 2022

Neural Comput & Applic

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First principles versus artificial neural network modelling of a solar desalination system with experimental validation

Cited by 13 publications

References 21 publications

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

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

Integration of IoT Technologies for Enhanced Monitoring and Control in Hybrid-Powered Desalination Systems: A Sustainable Approach to Freshwater Production

Performance estimation of tubular solar still with a wicked rotating drum using DT, LR, and KNN techniques of machine learning

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