Experimental investigation on Peltier based hybrid PV/T active solar still for enhancing the overall performance

Pounraj, P.; Winston, D. Prince; Kabeel, A.E.; Kumar, B. Praveen; Manokar, A. Muthu; Sathyamurthy, Ravishankar; Christabel, S. Cynthia

doi:10.1016/j.enconman.2018.05.011

Cited by 145 publications

(29 citation statements)

References 53 publications

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“…Besides, the heated fluid can be actually used in various applications depending on the form of heat energy (water or air). Many studies throughout the world have been done so far to study the performance of photovoltaic thermal (PVT) collectors by all means of analytical solutions, experimental measurements, and numerical simulations . The aim of analytical solutions is to simplify the process of performance estimation, while experimental studies provide a more realistic approach in view of the environmental and operating conditions.…”

Section: Introductionmentioning

confidence: 99%

Machine learning predictive models for optimal design of building‐integrated photovoltaic‐thermal collectors

et al. 2020

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This research article aims to examine the feasibility of several machine learning techniques to forecast the exergetic performance of a building-integrated photovoltaic-thermal (BIPVT) collector. In this regard, it uses multiple linear regression, multilayer perceptron, radial basis function regressor, sequential minimal optimization improved support vector machine, lazy.IBK, random forest (RF), and random tree approaches. Moreover, it implements the performance evaluation criteria (PEC) to evaluate the system's performance from the perspective of exergy. The use of these approaches serves the identification process to realize the relationship between the input-output parameters of the BIPVT system. The novelty of this work is that it utilizes and compares multiple learning algorithms to predict the PEC of BIPVT through design parameters. Hence, the research considers the parameter (PEC) as the essential output of the BIPVT collector, while the input parameters are the length, width, and depth of the duct, located under the PV modules, as well as the air mass flow rate. The results of the research for the statistical indexes of mean absolute error, root mean square error, relative absolute error (%), and root relative squared error (%) show values of (0.2967, 0.3885, 1.8754, and 1.5237) and (0.4957, 0.8153, 2.9586, and 2.8289), respectively, for the training and testing datasets. While R 2 ranges (0.9997-0.9999) for those datasets. Therefore, to estimate the exergy performance of the BIPVT collector, the RF model is superior to other proposed models. K E Y W O R D SBIPVT, exergetic performance, machine learning, performance evaluation criteria, predictive model

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Section: Introductionmentioning

confidence: 99%

Machine learning predictive models for optimal design of building‐integrated photovoltaic‐thermal collectors

et al. 2020

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“…Different review articles have been studied . Various research works have reported the use of nanofluid in solar still, the conventional solar still (CSS) with energy storage materials, inclined solar still, and hybrid solar still …”

Section: Introductionmentioning

confidence: 99%

Experimental investigation on the effect of photovoltaic panel partially and fully submerged in water

Madhu

Esakki

Kabeel

et al. 2019

Heat Trans. Asian Res.

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This study presents an experimental analysis of improving the thermal, electrical efficiency, and yield of a conventional solar still (CSS). The photovoltaic (PV) efficiency decreases with increase in water depth inside the basin while the still efficiency is higher in the case of fully submerged condition. The maximum water production was about 8 kg/m 2 /day with PV under fully submerged condition; and during off-shine hours the still efficiency was higher when compared with the partially submerged condition. Similarly, with a decrease in water temperature the panel efficiency is increases. The maximum hourly water production with and without the PV was found to be 1.3 and 0.45 kg/m 2 , respectively. The main outcome of this study is that this mechanism can be used in isolated locations where there is a scarcity of current and distilled water. K E Y W O R D S efficiency, electrical, photovoltaic panel, solar still, water depth, yield Heat Transfer-Asian Res. 2019;48:1709-1721.wileyonlinelibrary.com/journal/htj

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“…Solar thermal technology is the traditional method to conserve the continuous use of fossil fuel to evaporate water. Multistage flash, membrane desalination, and reverse osmosis technique were developed with the constant operation for mass production of drinking water, whereas it consumes electrical energy as the primary source and requires higher land capacity with initial investment . Even the quality of produced fresh water in direct incorporation of particles in the fluid is questionable.…”

Section: Introductionmentioning

confidence: 99%

Experimental investigation on the effect of MgO and TiO ₂ nanoparticles in stepped solar still

et al. 2019

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Summary This work aims at augmenting the amount of potable water using MgO and TiO2 in stepped solar still. Experiments were carried out for the climatic conditions of Chennai, India, with two different concentrations of nanofluids inside a stepped basin under three different cases. Results show that there is an improvement in yield of fresh water from stepped solar still by 33.18% and 41.05% using 0.1% and 0.2% volume concentration of TiO2 nanofluid, respectively, whereas the freshwater yield from stepped still with MgO nanofluids improved by 51.7% and 61.89%. Furthermore, the economic analysis revealed that the cost of potable water from the modified solar still reduced from 0.029 to 0.016 $/kg. Similarly, the useful annual energy, yearly cost per kilogram, and annual cost per kilowatt hour are significantly profitable with the use of MgO nanofluid in the stepped basin and found as 512.46 kWh, 0.025 $/kg, and 0.026 $/kWh, respectively. It is also found that the cost of potable water from the modified still significantly increases as the amount of fresh water produced is decreased with increased fabrication cost of the solar still.

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Experimental investigation on Peltier based hybrid PV/T active solar still for enhancing the overall performance

Cited by 145 publications

References 53 publications

Machine learning predictive models for optimal design of building‐integrated photovoltaic‐thermal collectors

Machine learning predictive models for optimal design of building‐integrated photovoltaic‐thermal collectors

Experimental investigation on the effect of photovoltaic panel partially and fully submerged in water

Experimental investigation on the effect of MgO and TiO ₂ nanoparticles in stepped solar still

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Experimental investigation on Peltier based hybrid PV/T active solar still for enhancing the overall performance

Cited by 145 publications

References 53 publications

Machine learning predictive models for optimal design of building‐integrated photovoltaic‐thermal collectors

Machine learning predictive models for optimal design of building‐integrated photovoltaic‐thermal collectors

Experimental investigation on the effect of photovoltaic panel partially and fully submerged in water

Experimental investigation on the effect of MgO and TiO 2 nanoparticles in stepped solar still

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Product

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Experimental investigation on the effect of MgO and TiO ₂ nanoparticles in stepped solar still