Optimization of a novel spray flash desalination system integrated with concentrated solar power utilizing response surface methodology

Ahmed, Faizan; Aziz, Mohd Yusmaidie; Shaik, Feroz; Khor, C.Y.

doi:10.1016/j.desal.2023.116640

Cited by 13 publications

(2 citation statements)

References 40 publications

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“…Generally, one applies a low-degree polynomial to a particular collection of independent variables. When a linear function of the independent variables accurately describes the response, the first-order model is used as the approximation function (Ahmed et al 2023).…”

Section: Response Surface Methodology (Rsm)mentioning

confidence: 99%

Application of response surface methodology and neural networks in pyramid solar still for seawater desalination: An optimization and prediction strategy

2024

Global NEST Journal

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<p>Potable water is essential in various aspects of daily life. Converting brackish water to drinkable water using traditional methods is costly and has environmental impacts. Solar energy is preferred over fossil fuels and other energy sources due to its cost and environmental benefits. Solar stills are increasingly popular due to the growing need for drinkable water, but their performance requires improvement. The study examines the pyramid solar still (PSS) productivity using different operating parameters, such as solar intensity (350-950 W/m2), water inlet temperature (30-50°C), water depth (4-8 cm), and insulation thickness (0.05-0.15m). The response surface technique (RSM) was used to examine the performance of the still under different operating conditions. The RSM approach optimized the process for maximum output and reduced testing time and effort. To verify the optimum response derived from RSM, the artificial neural networks (ANN) model was implemented. The comparative studies of the ANN and RSM models demonstrates a strong correlation with the outcomes achieved in maximizing the productivity of PSS. It found that the ideal values for solar intensity, water intake temperature, water depth, and insulation thickness are 950 W/m2, 50°C, 4 cm, and 0.15 m, respectively. These parameters contribute to the production of 2.585 kg/m2 of distillate.</p>

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Section: Response Surface Methodology (Rsm)mentioning

confidence: 99%

Application of response surface methodology and neural networks in pyramid solar still for seawater desalination: An optimization and prediction strategy

2024

Global NEST Journal

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“…199 Moreover, the difference between adjusted R 2 and predicted R 2 should be less than 0.2, indicating reasonable agreement. 200 Adequate precision measures the signal-to-noise ratio. 201 The value of adequate precision should be greater than 4 to confirm adequate signal for design space navigation.…”

Section: Response Surface Methodology (Rsm)mentioning

confidence: 99%

Graphene in rubber formulations: a comprehensive review and performance optimization insights

Leong,

Lim,

Ibrahim

2023

Mol. Syst. Des. Eng.

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Theoretical and experimental investigations of inclined solar still using secondary reflectors on east-west for enhanced distillate yield

Venkata Sai,

Reddy

2024

Applied Thermal Engineering

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Optimization of a novel spray flash desalination system integrated with concentrated solar power utilizing response surface methodology

Cited by 13 publications

References 40 publications

Application of response surface methodology and neural networks in pyramid solar still for seawater desalination: An optimization and prediction strategy

Application of response surface methodology and neural networks in pyramid solar still for seawater desalination: An optimization and prediction strategy

Graphene in rubber formulations: a comprehensive review and performance optimization insights

Theoretical and experimental investigations of inclined solar still using secondary reflectors on east-west for enhanced distillate yield

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