Area determination of solar desalination system for irrigating crops in greenhouses using different quality feed water

Mashaly, Ahmed F.; Alazba, A. A.; Al-Awaadh, Alhussein M.; Mattar, Mohamed A.

doi:10.1016/j.agwat.2015.02.009

Cited by 16 publications

(18 citation statements)

References 25 publications

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“…The minimum (MIN), maximum (MAX), mean (AVG), standard deviation (SD), Skewness (SK), Kurtosis (KU), and coefficient of variation (CV) are shown in Table 2. As Table 2 43 PPT, respectively. With a mean of 28.14 PPT, the value of available TDS F is not valid for either drinking or any other use (e.g.…”

Section: Data Processing and General Performancementioning

confidence: 98%

“…A more complete demonstration of these experimental data is given by [36]. Also, a detailed discussion and analysis of comparative investigation between MD and crop water requirement to determine the required area of the solar still system can be found in [43]. Table 4 shows the results of the statistical performance of the ANN model with different node numbers in the hidden layer and different transfer/activation functions.…”

Section: Data Processing and General Performancementioning

confidence: 99%

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Neural network approach for predicting solar still production using agricultural drainage as a feedwater source

Mashaly

Alazba

2016

Desalination and Water Treatment

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A B S T R A C TThis study investigates the application of artificial neural network (ANN) for predicting solar still production (MD). Agricultural drainage water (ADW) was desalinated using a solar still. Important meteorological variables: ambient air temperature, relative humidity, wind speed, and solar radiation, together with the operational variables of flow rate, temperature, and total dissolved solids of feedwater, were considered as input parameters for ANN modeling. The output parameter was MD. The results revealed that the ANN model with five neurons and hyperbolic tangent transfer function was the most appropriate for MD prediction based on the minimum measures of error. The optimal ANN model had a 7-5-1 architecture. The ANN model was also compared to multiple linear regression (MLR). The results indicated that, compared to the MLR model, the ANN model provided better prediction results in all modeling stages. The average of the coefficient of determination between the ANN results and the experimental data was more than 0.96. Consequently, the ANN model was shown to have acceptable generalization capability and accuracy. The relative errors of forecasted MD values for the ANN model were mostly in the vicinity of ±10%. These results indicate that ANN can be successfully used in the MD prediction of a solar still desalinating ADW. One major output/contribution of this research involves assessment of the ANN modeling technique during ADW solar desalination, which adds a new perspective to the system analysis, design, and modeling for the potential productivity of a solar still to produce water during the ADW desalination process.

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Section: Data Processing and General Performancementioning

confidence: 98%

Section: Data Processing and General Performancementioning

confidence: 99%

Neural network approach for predicting solar still production using agricultural drainage as a feedwater source

Mashaly

Alazba

2016

Desalination and Water Treatment

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“…As seen from this table, the solar still desalinations are a good candidate for supplying water in dry climates. They are simple and inexpensive with the least capital and operational costs and can produce freshwater with high quality while being eco‐friendly. However, they need larger areas for their solar collectors while their production capacity and efficiency are low (ie, their GOR is less than 0.5), because of the heat being wasted through the glass …”

Section: Solar‐driven Desalinationsmentioning

confidence: 99%

“…The rough estimate of crop water requirement (CWR) for a typical greenhouse is 0.012 to 0.016 m 3 /d/m 2 . To calculate it exactly, three computational methods of Penman‐Monteith, Fernandez, and Hargreaves are normally employed to calculate the reference evapotranspiration (ET 0 ). Table summarizes these methods and highlights the key parameters considered by each method. Among these, the Penman‐Monteith method calculates the reference evapotranspiration as a function of climatic factors and geographical location parameters, including longitude, latitude, altitude, solar radiation on the grass surface, soil heat flux, air temperature, mean daily wind speed, and actual vapor pressure.…”

Section: Greenhouse Technologymentioning

confidence: 99%

“…On the other hand, employing solar‐driven desalinations for greenhouses is another promising approach to address the water‐energy‐food nexus. Such systems have been introduced in several countries around the world and discussed in multiple reports . These technologies can even help a greenhouse to be operated in rural areas, without access to the electricity grid, which is often referred to as off‐grid greenhouses.…”

Section: Introductionmentioning

confidence: 99%

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A comprehensive review of solar‐driven desalination technologies for off‐grid greenhouses

Shekarchi

Shahnia

2018

Int J Energy Res

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Summary This paper is motivated by the crisis of freshwater in remote areas around the world and responds to the growing need for sustainable food production in arid lands. It focuses on utilizing solar energy to yield freshwater from the sea or brackish water with less environmental impacts, for greenhouses, which can produce sustainable food all over the year. The integration of various solar‐driven desalinations such as solar still, humidification‐dehumidification, reverse osmosis, electrodialysis, and multieffect and multistage flash with greenhouses are evaluated, for better sustainability towards greenization. The paper first discusses the specifications of solar‐driven desalinations and compares their advantages and limitations. Then, different types of greenhouses are introduced, and their total water requirement is discussed based on their locations, crop type, greenhouse technology, irrigation type, and environmental conditions, as well as their cooling and heating strategies. Later, the existing integration of solar‐driven desalinations with greenhouses are reviewed, and their advantages and limitations are deliberated. Finally, the paper discusses the criteria to be considered when selecting solar‐driven desalinations for greenhouses and presents a detailed comparison between the water production rate and cost as well as the energy consumption of these systems. In the end, the most appropriate combinations of solar‐driven desalinations with greenhouses are recommended based on their water requirement and production cost.

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Membership function comparative investigation on productivity forecasting of solar still using adaptive neuro‐fuzzy inference system approach

Mashaly

Alazba

2017

Env Prog and Sustain Energy

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Modeling solar still productivity (SSP) is one of the most studied topics in solar desalination due to it having essential applications in the design of solar still systems. This study applied an adaptive neuro-fuzzy inference system (ANFIS) and different membership functions (MFs) to predict the SSP required by designers, operators, and beneficiaries of solar stills. The output of this research can be used as a reference for designing and managing solar stills that could lead to optimizing the performance. The modeling process was based on real-field experimental data. The model considers the solar radiation, relative humidity, total dissolved solids of the feed, total dissolved solids of the brine, and feed flow rate as the input variables. The results show that ANFIS forecasting with generalized bell MF (GBELLMF) produced the highest correlation coefficient (CC) and the smallest root mean square error (RMSE) when compared with other MF types. Thus, the ANFIS model with GBELLMF (CC 5 0.99; RMSE 5 0.03 L/m 2 /h) provides the best SSP prediction accuracy, which is better than other models with MFs. In addition, the statistical indicators demonstrate that the ANFIS model is better for predicting the SSP than multiple linear regressions. These findings demonstrate that ANFIS can be applied to forecast the SSP using weather and operational data as inputs with the best membership function (which is GBELLMF).

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Area determination of solar desalination system for irrigating crops in greenhouses using different quality feed water

Cited by 16 publications

References 25 publications

Neural network approach for predicting solar still production using agricultural drainage as a feedwater source

Neural network approach for predicting solar still production using agricultural drainage as a feedwater source

A comprehensive review of solar‐driven desalination technologies for off‐grid greenhouses

Membership function comparative investigation on productivity forecasting of solar still using adaptive neuro‐fuzzy inference system approach

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