Original papersMLP and MLR models for instantaneous thermal efficiency prediction of solar still under hyper-arid environment
Introduction
Desalination is an appropriate means for providing pure water. Moreover, desalination is one of the ways to deal with the water crisis, because it is considered a stable source of water production (Karimi Estahbanati et al., 2015). Many sources of energy can be utilized for water desalination such as fossil fuel and electricity (Feilizadeh et al., 2010). The desalination process consumes a significant amount of energy (Dev et al., 2011), which is often provided by fossil fuels, and this causes many adverse effects on the environment owing to the emission of significant amounts of greenhouse gasses: around 1-kg CO2-equivalents (CO2-e) per kW-h (or 3.8 kg CO2-e per) m3 of desalinated water (Ghaffour et al., 2014). However, renewable energy can be utilized for the desalination process to avoid these problems (Karimi Estahbanati et al., 2015, Rajaseenivasan and Murugavel, 2013). One of the available renewable energies for the desalination of low quality water is solar energy. A solar still is a simple way to desalinate water using solar energy and is one of the best solutions to confront the water crises in remote arid areas. With locally available materials, a solar still can be manufactured easily and the operation is simple, plus the maintenance is inexpensive and no skilled persons are required (Omara and Kabeel, 2014).
On the other hand, a solar still is not commonly used because of its low thermal efficiency and its low productivity (Kabeel et al., 2012, Buros, 2000, Tiwari, 1992), which depend on meteorological, design and operational parameters (Sivakumar and Ganapathy Sundaram, 2013). Accordingly, to optimize and enhance solar energy to be widely utilized, further modeling processes to forecast optimal performance and to identify vital parameters related to performance are required.
Solar stills should be designed and operated for optimum efficiency through modeling and prediction. Instantaneous thermal efficiency is one of the important parameters to be accurately determined and is therefore one of the most important parameters for judging the performance of solar stills since it details how effective the still is at absorbing solar energy and evaporating and collecting water. Classical modeling methods are too complex and the solutions require long calculations, and are occasionally completely unrealizable (Tripathy and Kumar, 2008, Khadir, 2005, West et al., 1997). In addition, the thermal performance analyses of solar systems are too complex, and analysis generally needs a large amount of computer power and requires a considerable amount of time for accurate forecasts. Moreover, the experiments and thermal analyses of solar thermal systems such as solar collectors and solar stills are complicated owing to numerous measurements and the heat transfer processes. On the other hand, it is very important for designers and engineers to be able to choose the best system speedily and accurately (Kalogirou, 2000, Kalogirou, 2001, Kalogirou, 2004). As an alternative to the classical methods of modeling, instantaneously thermal efficiency can be accurately modeled and predicted using artificial neural networks (ANNs). ANNs have been utilized in several engineering applications as they can model complex physical phenomena such as those in thermal engineering (Islamgolu, 2003). The ANN method, apart from decreasing the time required, is able to find solutions that make solar-energy applications more applicable and viable (Kaukal et al., 2011).
Several studies have demonstrated the application of ANNs in the modeling of many processes in solar thermal systems. For example, Lecoeuche and Lalot (2005) used an ANN to forecast the in-situ daily performance of solar air collectors. Farkas and Géczy-Víg (2003) developed ANN models for different types of solar thermal collectors to forecast their outlet temperature. An ANN was employed by Kalogirou (2006) for the calculation of the performance parameters of flat-plate solar collectors. The ANN method was also utilized to calculate the efficiency of flat plate solar thermal collectors by Sözen et al. (2008). Santos et al. (2012) used ANNs to determine the effectiveness of modeling solar still distillate production. Hamdan et al. (2013) developed ANN models to find the performance of a triple solar still operating under local atmospheric conditions in Jordan. Kalogirou et al. (2014) used ANNs for the performance forecasting of large solar systems. An ANN model was developed and used by Yaïci and Entchev (2014) to forecast the performance of a solar thermal energy system utilized for domestic hot water and space heating applications. Mashaly and Alazba (2015) conducted a comparative investigation of ANN learning algorithms for modeling solar still production. Moreover, the performance of an ANN and multiple linear regression (MLR) methods were evaluated comparatively for modeling processes in the field of solar energy in recent years. Citakoglu (2015) modeled monthly solar radiation values by ANN and MLR techniques. ANN and MLR were used for modeling the temperature of water in a solar cooker (Çakmak, 2014). Kassem et al. (2011) used MLR analysis and ANN for predicting drying efficiency during the solar drying process. The estimation capacities of MLR and ANN were investigated to estimate monthly-average daily solar radiation over Turkey (Şahin et al., 2013). Caner et al. (2011) used the ANN technique to estimate thermal performances of solar collectors. In order to analyze the ANN performance, they also developed a MLR model. The multi-Layer Perceptron network (MLP) is one of the ANN models that have attracted great efforts in solving a variety of problems (Piekniewski and Tybicki, 2004). In this study, the MLP neural network was used. The objectives are to develop an instantaneously thermal efficiency (ηith) prediction model for a solar still using the MLP model, to compare the MLP model to the MLR model, and to assess the potential of MLP for predicting ηith.
Section snippets
Experimental set-up
The experiments were conducted at the Agricultural Research and Experiment Station, at the Department of Agricultural Engineering, King Saud University, Riyadh, Saudi Arabia (24°44′10.90″N, 46°37′13.77″E) between February and April 2013. The weather data were obtained from a weather station (model: Vantage Pro2; manufacturer: Davis, USA) close by the experimental site (24°44′12.15″N, 46°37′14.97″E). The solar still system used in the experiments was constructed from a 6 m2 single stage C6000
Results and discussion
According to the results of the field experiments, the average ηith the solar still system was 52.47%. This is consistent with the findings of Omara et al., 2013, Abdullah, 2013. The ηith varies from 15.85% to 82.16% as shown in Table 1. The effect of the TDSF on solar distillation process was studied and it was found that ηith decreases with the increase of TDSF, which is in accordance with the findings of Mahdi et al. (2011). Moreover, it was revealed that the ηith was increased with the
Conclusions
The thermal efficiency of the solar still is generally the most important parameter to assess because it can reveal the best solar still design. Here, the thermal efficiency of the solar still was defined and expressed as the instantaneous thermal efficiency (ηith). In this study, the ηith values of the solar still were predicted by the Multi-Layer Perceptron (MLP) neural network and multiple linear regression (MLR) models using the experimental results. The application of the MLR and MLP
Acknowledgment
The project was financially supported by King Saud University, Vice Deanship of Research Chairs.
References (60)
Improving the performance of stepped solar still
Desalination
(2013)A neural network for the estimation of forces acting on radar targets
Neural Networks
(1991)Correlations for estimation of daily global solar radiation with hours of bright sunshine in Turkey
Energy
(2009)- et al.
Investigation on thermal performance calculation of two type solar air collectors using artificial neural network
Expert Syst. Appl.
(2011) Comparison of artificial intelligence techniques via empirical equations for prediction of solar radiation
Comput. Electron. Agric.
(2015)- et al.
A statistical procedure for determining the best performing air quality simulation model
Atmos. Environ.
(1990) - et al.
Performance study of the inverted absorber solar still with water depth and total dissolved solid
Appl. Energy
(2011) - et al.
Neural network modelling of flat-plate solar collectors
Comput. Electron. Agric.
(2003) - et al.
A new radiation model for a single-slope solar still
Desalination
(2010) - et al.
Renewable energy-driven innovative energy-efficient desalination technologies
Appl. Energy
(2014)
Generalized linear and generalized additive models in studies of species distributions: setting the scene
Ecol. Model.
Applications of artificial neural-networks for energy systems
Appl. Energy
Artificial neural networks in renewable energy systems applications: a review
Renew. Sust. Energy Rev.
Optimization of solar systems using artificial neural-networks and genetic algorithms
Appl. Energy
Prediction of flat-plate collector performance parameters using artificial neural network
Sol. Energy
Artificial neural networks for the performance prediction of large solar systems
Renew. Energy
Experimental investigation of a multi-effect active solar still: the effect of the number of stages
Appl. Energy
Prediction of the daily performance of solar collectors
Int. Commun. Heat Mass
Statistical and graphical methods for evaluating solute transport models: overview and application
J. Contam. Hydrol.
Statistical comparison of models for estimating solar radiation on inclined surfaces
Sol. Energy
An experimental wick-type solar still system: Design and construction
Desalination
Forecasting with cue information: a comparison of multiple regressions with alternative forecasting approaches
Eur. J. Oper. Res.
Enhancing the stepped solar still performance using internal reflectors
Desalination
A new approach for the prediction of ash fusion temperatures: a case study using Turkish lignites
Fuel
Prediction of the viscosity of clarified fruit juice using artificial neural network: a combined effect of concentration and temperature
J. Food Eng.
Theoretical and experimental investigation on double basin double slope solar still
Desalination
Comparison of ANN and MLR models for estimating solar radiation in Turkey using NOAA/AVHRR data
Adv. Space Res.
Modeling solar still production using local weather data and artificial neural networks
Renew. Energy
Improvement techniques of solar still efficiency: a review
Renew. Sust. Energy Rev.
Determination of efficiency of flat plate solar collector using neural network
Expert Syst. Appl.
Cited by (58)
Predicting implications of design changes in BIM-based construction projects through machine learning
2023, Automation in ConstructionPredicting daily solar radiation using a novel hybrid long short-term memory network across four climate regions of China
2023, Computers and Electronics in AgricultureAI based temperature reduction effect model of fog cooling for human thermal comfort: Climate adaptation technology
2023, Sustainable Cities and Society