Predictive model for assessing and optimizing solar still performance using artificial neural network under hyper arid environment

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

doi:10.1016/j.solener.2015.05.013

Cited by 85 publications

(21 citation statements)

References 30 publications

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“…Sö zen et al [18] the ANN technique was applied to calculate the efficiency of solar collectors. ANNs have been used to model and forecast various solar still performances ranging from determining the effectiveness of modeling distillate yield using local weather data [19] and using different learning algorithms [20], to assess and optimize solar still performance under hyperarid environment [21], or instantaneous thermal efficiency prediction [22].…”

Section: Desalination and Water Treatmentmentioning

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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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: Desalination and Water Treatmentmentioning

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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“…Artificial intelligence (AI) techniques have been adopted to support the sustainability concept within technical issues. Among the several approaches to adopt AI techniques for system evaluation, artificial neural networks (ANN) and fuzzy logic (FL) systems have been prominently adopted in the area of sustainability [24,29]. For example, neural networks have been utilized by Abdeljawad et al [30] to forecast key water parameters such as salt concentration to evaluate reverse osmosis plant performance along the Gaza Strip, Palestine.…”

Section: Introductionmentioning

confidence: 99%

“…For example, neural networks have been utilized by Abdeljawad et al [30] to forecast key water parameters such as salt concentration to evaluate reverse osmosis plant performance along the Gaza Strip, Palestine. Additionally, an ANN has been used by Mashaly et al [29] for assessing and optimizing solar performance under hyper arid environments. Furthermore, Kant and Sangwan [31] developed models utilizing ANN and support vector regression (SVR) methods to evaluate power consumption.…”

Section: Introductionmentioning

confidence: 99%

Sustainability Ranking of Desalination Plants Using Mamdani Fuzzy Logic Inference Systems

et al. 2020

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As water desalination continues to expand globally, desalination plants are continually under pressure to meet the requirements of sustainable development. However, the majority of desalination sustainability research has focused on new desalination projects, with limited research on sustainability performance of existing desalination plants. This is particularly important while considering countries with limited resources for freshwater such as the United Arab Emirates (UAE) as it is heavily reliant on existing desalination infrastructure. In this regard, the current research deals with the sustainability analysis of desalination processes using a generic sustainability ranking framework based on Mamdani Fuzzy Logic Inference Systems. The fuzzy-based models were validated using data from two typical desalination plants in the UAE. The promising results obtained from the fuzzy ranking framework suggest this more in-depth sustainability analysis should be beneficial due to its flexibility and adaptability in meeting the requirements of desalination sustainability.

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“…In recent years, deep learning technology has been used to perform time series forecasting in the energy field. In solar energy applications, autoencoders and long short-term memory (LSTM) deep learning frameworks are used to estimate solar cell power conversion, and the forecasting results are more accurate compared with those of physical models [ 5 , 6 , 7 ]. Deep learning is also applied to photovoltaic power forecasting [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Based Power Generation Forecasting of Thermal Energy Conversion

Lai

2020

Entropy

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ORC is a heat to power solution to convert low-grade thermal energy into electricity with relative low cost and adequate efficiency. The working of ORC relies on the liquid–vapor phase changes of certain organic fluid under different temperature and pressure. ORC is a well-established technology utilized in industry to recover industrial waste heat to electricity. However, the frequently varied temperature, pressure, and flow may raise difficulty to maintain a steady power generation from ORC. It is important to develop an effective prediction methodology for power generation in a stable grid system. This study proposes a methodology based on deep learning neural network to the predict power generation from ORC by 12 h in advance. The deep learning neural network is derived from long short-term memory network (LSTM), a type of recurrent neural network (RNN). A case study was conducted through analysis of ORC data from steel company. Different time series methodology including ARIMA and MLP were compared with LSTM in this study and shows the error rate decreased by 24% from LSTM. The proposed methodology can be used to effectively optimize the system warning threshold configuration for the early detection of abnormalities in power generators and a novel approach for early diagnosis in conventional industries.

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Predictive model for assessing and optimizing solar still performance using artificial neural network under hyper arid environment

Cited by 85 publications

References 30 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

Sustainability Ranking of Desalination Plants Using Mamdani Fuzzy Logic Inference Systems

Deep-Learning-Based Power Generation Forecasting of Thermal Energy Conversion

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