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Multi-performance optimization of nanofluid cooled hybrid photovoltaic thermal system using fuzzy integrated methodology
Journal Pre-proof Multi-performance optimization of nanofluid cooled hybrid photovoltaic thermal system using fuzzy integrated methodology
Ibrahim Ahmed Qeays, Syed Mohd. Yahya, Mohammad Asjad, Zahid A. Khan PII:
S0959-6526(20)30498-4
DOI:
https://doi.org/10.1016/j.jclepro.2020.120451
Reference:
JCLP 120451
To appear in:
Journal of Cleaner Production
Received Date:
12 October 2019
Accepted Date:
05 February 2020
Please cite this article as: Ibrahim Ahmed Qeays, Syed Mohd. Yahya, Mohammad Asjad, Zahid A. Khan, Multi-performance optimization of nanofluid cooled hybrid photovoltaic thermal system using fuzzy integrated methodology, Journal of Cleaner Production (2020), https://doi.org/10.1016/j. jclepro.2020.120451
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Journal Pre-proof Multi-performance optimization of nanofluid cooled hybrid photovoltaic thermal system using fuzzy integrated methodology Ibrahim Ahmed Qeays1 , Syed Mohd. Yahya1,*, Mohammad Asjad2 , Zahid A. Khan2 1Sustainable
Energy & Acoustics Research Lab, Mechanical Engineering, Z.H.C.E.T, Aligarh Muslim University,Aligarh-202002, India.
2Department
of Mechanical Engineering, Jamia Millia Islamia University,New Delhi-110025, India.
Abstract In the event of rapidly depleting conventional sources of energy such as fossil fuels and an urge for protecting environment from pollution, there is a thrust, across the globe, to produce cleaner and sustainable energy. Solar energy is one such form which is seen as a future source of energy. Solar energy is converted into electrical and thermal energy with the help of a hybrid photovoltaic thermal system (HPVTS).Performance of the PV panel may be affected when it gets heated due to high ambient temperature and other reasons. Proper cooling of PV panel may protect it from heating which may prevent deterioration in its performance. This paper proposes a mechanism for cooling the PV panel by circulating nanofluid around it. It also experimentally investigates the effect of critical input parameters such as irradiance, ambient temperature, flow rate, and concentration of the nanofluid on the nanofluid cooled HPVTS attributes (output responses) like overall efficiency, exergy loss, surface temperature, entropy generation, and electrical efficiency using the Taguchi’s L16 orthogonal array (OA). Weighting factors are calculated using Triangular fuzzy numbers (TFN) for output responses and optimal setting of the input parameters is obtained using TOPSIS. Keywords: Solar energy; Electrical energy; Thermal energy; Nanofluid cooled HPVTS; Multi-attribute decision making; TOPSIS
*corresponding author email: [email protected]
Journal Pre-proof 1. Introduction Two events of world in the last three decades are responsible for the major economic crash: (i) Middle East war in October 1973 and (ii) huge surge in global oil prices during 2008 to 2014. These events generated researchers’ interest in solar energy worldwide [1, 2]. Further, these economic crises resulted in serious concern globally in the consumption and production of oil, which had already achieved its zenith [3]. Pareto et al. [4] demonstrated the interplay between oil prices and inflation that affected investments and also shifted the usage of coal and gas at a large scale. It is emphasized that inflation with continuous depleting oil fields, will create a hike in energy prices, and this will go on until we replaces the fossil fuels by sustainable sources of energy. One such clean source of energy i.e. solar energy can be harnessed in two possible ways: (i) thermal energy coming from the sun can be exploited by constructing solar collectors with heat absorbers like water or any other fluid and (ii) light energy can be harnessed by semiconductor photovoltaic (PV) panels which convert light energy directly to DC current. Solar radiation is an energy source with tremendous potential for developing countries, particularly those which are located in temperate and tropical zones [5]. PV cells have a wide range of application and are clean and eco-friendly source for power production [6]. Photovoltaic systems have various advantages such as independent operation, reliable long life, no fuel and fumes, easy installation, low maintenance, and low recurring costs etc. as compared to other sources [7]. However, the PV systems tend to get heated due to absorption of unwanted heat from sun during operation which obstructs their efficiency. The absorbed heat causes serious problems including increase in the working temperature of the PV cell [8, 9].Increase in cell temperature depletes its electrical power conversion rate. Increase of one degree Celsius in the working temperature of a c-Si cells based PV system resulted in a 0.45% decrease in its efficiency [10]. To get rid of this situation, PV panel in conjunction
Journal Pre-proof with thermal system is used which is termed as hybrid PV thermal system (HPVTS). In this system unwanted heat from PV panel is removed by additional thermal unit which acts as a heat exchanger. Therefore, HPVTS produces both electricity and heat simultaneously. HPVTSs have been studied considerably using different methodology such as analytical methods, experimental measurements, and numerical simulations etc. Al-Shamani et al. [11] used various nanofluids made up of metallic oxide and carbide (SiO2, TiO2 and SiC) in photovoltaic thermal (PV/T) collector. Their experimental results showed that the PV/T collector with SiC nanofluid had maximum electrical efficiency of 13.52% and overall efficiency of 81.73% at a constant flow rate of 0.170 kg/s and insolation level of 1000 W/m2, followed by PV/T-TiO2nanofluids, PV/T-SiO2 nanofluids, and PV/T-water respectively. Yun and Qunzhi [12] applied MgO-water nanofluid over the silicon PV panel in order to reduce the heat of solar cells. The particle size of the nanofluid used was approximately 10 nm and the concentrations were 0.02%wt., 0.06%wt. and 0.1%wt. They found that there was no gain in electrical conversion efficiency as compared to the traditional PV system but overall efficiency was enhanced. Rejeb et al.[13] performed a parametric investigation of a photovoltaic thermal (PV/T) system. They tested two types of nanoparticles (Al2O3 and Cu) mixed in different base fluids (pure water and ethylene glycol) with varying concentration (0.1, 0.2 and 0.4%wt.).Results of their study revealed that the best thermal and electrical efficiency was attained by metallic water based nanofluid in comparison to ethylene glycol (EG) based metallic oxide nanofluid. Sardarabadi et al. [14] employed silica/water nanofluid to investigate the performance of a PVT system. Silica nanoparticles of 11-14 nm diameter were mixed with water in two concentrations i.e. 1% and 3% by weight. They reported an increase of 7.6% and 12.8% in thermal efficiency for nanofluid with 1% wt. and 3% wt. respectively. Further, they also observed that the overall efficiency was also upgraded to 3.6% for 1% wt. Whereas, it went down to 7.9% for 3% wt. Al-Waeli et al. [15] observed
Journal Pre-proof that by adding 3% wt. of SiC nanoparticles to water increased the fluid density and viscosity by 0.0082% and 1.8% respectively which increased the electrical efficiency by 24.1%. Radwan et al. [17] studied a novel cooling method for low concentrated photovoltaic thermal system by introducing a micro channel heat sink with different nanofluids (Al2O3 and SiC) at different concentrations. They found SiC-water more effective than Al2O3–water as higher reduction in the temperature was achieved in case of the SiC-water nanofluid. Their results showed a drop in the temperature of the PV cell to 38°C and improvement of 19% in electrical efficiency with the use nanofluids. Karami and Rahimi et al. [18] studied the behaviour of the water based nanofluid with small amount of Boehmite for the PV/T system. They made two different configurations, one was straight channel and other was helical channel. They found that temperature drop was higher in case of nanofluid as compared to water. Further, they reported that the electrical efficiencies of PV/T system using straight and helical channels were 20.57% and 37.67% respectively. Thus, they recommended that the helical configuration with Boehmite nanofluid was a better option to increase the performance of the PV/T system. Al-Waeli et al. [19] studied the effect of nanofluids on the efficiency of PV/T system. They used water based nanofluids with different concentrations of Al2O3, CuO and SiC nanoparticles in their study which revealed that the SiC-water nanofluid provided the best thermal efficiency among all nanofluids. They also found that the thermal efficiency was enhanced by 1.96%, 3.42% and 4.8% for Al2O3, CuO and SiC respectively when 4%vol was used. Bellos and Tzivanidis [20] examined the effects of nanofluid, stored in the storage tank of different capacities, on thermal and electrical efficiencies of a hybrid PV/T system. The result of their experiment showed that for Cu/water nanofluid stored in 150litre tank, the electrical efficiency was enhanced by 1.49% and the thermal efficiency was increased by 4.35%. They also found that the mean yearly thermal efficiency of the PV/T system for Cu based nanofluid was 43.8% which was 42% that of water and the mean yearly
Journal Pre-proof electrical efficiency increased from 12.4% to 12.5% when the results were compared with water. Lari et al. [40] investigated the performance of PV system experimentally using Ag/water nanofluids. The results of their study showed that there was an increase in electrical output by 8.5% and an increase in thermal output by 13% when the performance was compared with the water cooled system. Hasan et al.[21] performed an experiment using nanofluid as a coolant to investigate the efficiencies associated with PV/T system. They used a setup which consisted of 36 nozzles and 4 parallel tubes. The nozzles injected the nanofluid on the back of the PV system. Three water based nanofluids made of three nanoparticles i.e. SiC, TiO2 and SiO₂ were used in their experiment. They reported that the use of SiC/water nanofluid resulted in the best performance of the PV/T system and the electrical, thermal and overall efficiency of the system in case of SiC nanoparticle was 12.75%, 85% and 97.75% respectively. From the studies conducted by several researchers presented above, it is evident that performance of the nanofluid cooled HPVTS is greatly affected by certain critical input parameters of the system. Consequently, optimization of the input parameters is necessarily required to derive optimum performance of the system. The decision to select optimum input parameters to achieve best performance of HPVTS involves conflicting criteria which calls for application of Multi-Attribute Decision Making (MADM) methods. Among several MADM methods, techniques such as Analytic Hierarchy Process (AHP) [22], Technique for Order Preference by Simulation of Ideal Solution (TOPSIS) [23] VIKOR [24] and gray relational analysis [25] are generally used for tackling problems related to engineering application. These techniques were employed by many researchers to solve different types of decision problems [26-30]. Tong & Su [31] suggested that TOPSIS is the best suited MADM method to solve decision problems as it has the ability to tackle both continuous and discrete data for multiple response
Journal Pre-proof problems. It is based on the basic idea of selecting the alternative which is closest in distance to the positive ideal solution and farthest in distance from the negative ideal solution. Researchers have emphasized for a long time that the uncertainty, subjectivity and vagueness involved in real life multi criteria decision making (MCDM) problems further complicates and increases the complexity of the problems [32]. Most commonly, linguistic variables such as very low, low, high, very high etc. are used to reduce the vagueness of the data. This is achieved by employing the fuzzy set theory [33, 34] which reduces the uncertainty in the data with the help of linguistic variables. Linguistic variables can be represented by different sets of numbers like triangular, trapezoidal, pentagonal, etc. fuzzy number sets. Researchers have used various fuzzy based MCDM methods to solve decision problems. Rani et al. [35] applied VIKOR approach in conjunction with fuzzy set to explore and investigate the renewable energy technologies in India. Lin et al. [36] used fuzzy technique along with weighted probability method for the optimal design of thermal energy storage system based on phase change material. Their study revealed that the design obtained using this technique was consistent with physical scenario. James et al. [37] applied fuzzy- MADM approach for the assessment of green supplier. Their results confirmed the validity of the proposed model and ensured the efficacy to solve the selection problem of green suppliers and further improvement strategies. Wang et al.[38] performed analysis to choose the best available technology for solid waste management by incorporating the fuzzy-MCDM approach. They used linguistic variable to rate the alternatives and found the weight of evaluation criteria. The model developed by them was helpful for stakeholders to determine the priority sequence of attribute. MADM based studies in solar applications such as solar collector design, HPVTS system optimisation are rare in the literature. Especially, the application employing fuzzy-TOPSIS for multi-performance optimization of HPVTS is missing in archival literature. Therefore,
Journal Pre-proof multi-performance optimisation of HPVTS using fuzzy-TOPSIS method makes the work worthy and novel. This work aims at selecting the optimum input parameters of the nanofluid cooled HPVTS which yields optimum performance of the system. Taguchi L16 orthogonal array is used as design of experiments in this study. This study considers four input parameters of the HPVTS i.e. irradiance, ambient temperature, flow rate and concentration of the nanofluid each at four levels. Triangular fuzzy numbers (TFN) are used to determine the relative importance of output responses. Finally TOPSIS is used for each experimental trial for the calculation of overall performance index values and the optimum combination of factor levels.
2. Materials and methodology 2.1 Experimental setup The detailed diagram of the experimental setup consisting of different components is shown in Fig. 1. It consists of one 300W poly-crystalline silicon photovoltaic module (Vikram Solar Co., India). Collectors used in this system are made up of copper tubes which are placed beneath the PV cells in serpentine structure to cool down the PV surface temperature. The nanofluid is circulated via pump through the cooling circuit installed at the rear side of panel with a steady constant mass flow rate. A counter flow design shell-and-tube heat exchanger is used to cool the nanofluid after it gets heated in the cooling circuit of HPVTS. Temperature of the nanofluid at the inlet and outlet of the cooling circuit and the heat exchanger are recorded by k-type thermocouple mounted over there. Surface temperatures of PV module cells are taken by temperature gun. The electrical circuit comprises of charge controllers, storage batteries, and consumers DC load (here a 100W bulb is used). To ensure a continuous electricity production by HPVTS, charge controllers act as a connecting switch between batteries, consumers load and solar panels. For measuring short-circuit and open-circuit
Journal Pre-proof currents and voltages, digital multimeters are used while hand held solar power meter is employed for measuring radiation.
Flow meter
S
TS
TS: Panel surface temperature S: Solar power meter
T2 PV Panel
TA: Ambient temperature sensor TA Tank
T1 Pump
Voltmeter Ammeter
T3
Charge Controller
Heat Exchanger
Battery
Load
Fig. 1: Schematic of the experimental setup where T1, T2 andT3 are thermocouple attached to the system for temperature measurement of inlet and outlet. The red colour path represents thermal circuit while blue colour path represent electric circuit.
2.2 Preparation of the nanofluid Zn-(EG+water) nanofluid with four concentration (in % volume) of nanoparticle i.e. 0.1%, 0.3%, 0.5% and 0.7% was used in this study. Zinc nanoparticles of APS 40nmwas purchased from SRL lab. Different sample (Fig.2) as per requirement of concentration was made by dispersing Zn particles in a solution of 50% deionised water and 50% EG. After that sonication process was performed through probe type sonicator (Make: WENSAR, Model:
Journal Pre-proof Pro-500) for 180 minutes. During the sonication process, surfactant CTAB is added to make the colloidal solution stable. After completion of the sonication process, the final nanofluid was prepared and subsequently used in the experimental study. Zeta potential test was performed for all samples to measure the stability of the nanofluids using Zeta Sizer (make: Malvern Panalytical, UK; NanoZS90), the zeta potential value was found to be 60mV at a nanoparticle concentration of 0.5%vol., which shown good stability. However for 0.1%, 0.3% and 0.7% vol. concentration the zeta potential values were 25mV, 38mV and 45mV respectively, showing moderate stability.
Fig. 2: Prepared nanofluid of Zn-water/EG with 0.5% concentaration.
2.3 Input parameters and their levels Based on the available literature, four input parameters of the HPVTS i.e. irradiance, ambient temperature, flow rate, and concentration of the nanofluid were considered in this study [1419]. The feasible space for the input parameters was defined by varying the irradiance in the range from 400 to 1000 W/m2, the ambient temperature in the range from 25 to 40oC, the flow rate in the range from 0.5 to 2 l/min, and the concentration of the nanofluid in the range
Journal Pre-proof from 0.1 to 0.7 %vol. Four levels of each input parameter were selected. The input factors and their levels are shown in Table 1. Table 1: Input parameters and their levels Input parameters Symbol Irradiance A Ambient temperature B Flow rate C Concentration of the nanofluid D
Unit W/m2 oC l/min %vol.
Level 1 400 25 0.5 0.1
Level 2 600 30 1.0 0.3
Level 3 Level 4 800 1,000 35 40 1.5 2.0 0.5 0.7
2.4 Taguchi design of experiments In the Taguchi design of experiment, specially designed orthogonal arrays (OAs) are used to study the entire input parameter range with relatively fewer numbers of experiments compared to full factorial design. An OA consists of a predefined number of rows and columns. Rows of the OA represents number of experimental trials and columns represent allocation of the input parameters and their interactions. Selection of a suitable OA depends on the number of input parameters and their levels involved in the study. An OA whose degrees of freedom is either equal to or more than the degrees of freedom of the complete range of input factors and their interactions and which can accommodate the selected levels of input parameters is suitable for use in the study. The degrees of freedom of an OA are defined as total number of experiments minus one. Similarly, the degrees of freedom of an input parameter are specified as number of levels of that parameter minus one. In the present study four input parameters each at four levels are considered. Degrees of freedom of each input parameter are 3 (4 – 1). Thus, the total degrees of freedom of all four input parameters are 12. An orthogonal array whose degrees of freedom are at least 12 and also it can accommodate four input parameters each at four levels can be suitably used in this study. Thus, L16 OA having 15 degrees of freedom (16 – 1) was used to experimentally ascertain the effect of input factors on the output responses as its degrees of freedom is higher than the overall degrees of freedom of all four input factors and it can also accommodate four levels
Journal Pre-proof parameters. It may be noted that in this study only main effect of the input parameters was investigated and combined/interaction effect of the parameters was not explored. Further, five attributes (output responses) of the nanofluid cooled HPVTS were selected in this study the details of which are provided in section 2.5. The L16 OA along with the values of five attributes of each experiment is shown in Table 2.
Table 2: L16 OA and values of output responses Expt. No. Input parameters Output responses Overall Exergy Surface Entropy efficiency loss temperature generation (%) (W/m2) (oC) A B C D (W/K.m2) 1 400 25 0.5 0.1 41.83 512 32 2.11 2 400 30 1 0.3 30.85 501 34.6 2.17 3 400 35 1.5 0.5 35.23 506 38 2.21 4 400 40 2 0.7 32.41 535 42 2.75 5 600 25 1 0.5 42.14 510 35 2.25 6 600 30 0.5 0.7 50.87 542 41 2.31 7 600 35 2 0.1 48.60 520 38 2.40 8 600 40 1.5 0.3 44.63 502 45 2.79 9 800 25 1.5 0.7 53.62 580 37 2.42 10 800 30 2 0.5 55.30 550 40.5 2.47 11 800 35 0.5 0.3 58.27 592 44 2.75 12 800 40 1 0.1 48.75 610 48 2.96 13 1,000 25 2 0.3 48.65 565 42 2.62 14 1,000 30 1.5 0.1 52.34 602 46 2.73 15 1,000 35 1 0.7 56.20 595 50 2.85 16 1,000 40 0.5 0.5 62.35 715 56 2.91
Electrical efficiency (%) 13.38 13.20 13.11 12.98 13.45 13.41 13.12 13.14 13.71 13.85 13.60 13.35 13.89 13.10 13.15 13.57
2.5 Output responses and their measurement In this study the output responses were evaluated using instruments and empirical correlation depending upon the type of response. As discussed earlier, PV surface temperature was measured using temperature gun in the vicinity of the panel at different locations, and then an average value was used as a measure of surface temperature. In HPVTS framework, the overall efficiency (ηPVT) is basically the ratio of output energy and the input energy for a chosen time interval and it is given by Eq.(1). ηPVT is indeed sum of the electrical and thermal efficiencies (ηel and ηth) respectively.
Journal Pre-proof
𝜂𝑃𝑉𝑇≅
𝑡2
𝐸𝑡ℎ + 𝐸𝑒𝑙
∫𝑡1(𝐴𝑐𝐸′′𝑡ℎ + 𝐴𝑃𝐸′′𝑒𝑙)𝑑𝑡
𝐸𝑖𝑛
𝐴𝑐∫𝑡1(𝐺′′𝑒𝑓𝑓)𝑑𝑡
⟹ 𝜂𝑃𝑉𝑇 =
𝑡2
= 𝜂𝑡ℎ +𝑟.𝜂𝑒𝑙
(1)
where, 𝐸𝑡h and 𝐸𝑒𝑙 are thermal and electrical power output (in Watt) produced by HPVTS respectively and 𝐸𝑖𝑛 is the input power form sun, Ap and Ac are the solar panel area (m2) and the collector tubes area respectively, with Ap/Ac as a packing factor denoted by ‘r’, 𝐸′′𝑡ℎ represents the rate of thermal energy yield per unit area of collector tubes, 𝐸′′𝑒𝑙 denotes the rate of electrical energy yield per unit area of PV panel, 𝐺′′𝑒𝑓𝑓denotes the effective irradiance, t1 and t2 are lower and upper limits of the time interval chosen for collecting data. Based on simple energy analysis, 𝐸𝑡ℎcan be determined using Eq. (2). 𝐸𝑡ℎ = 𝑚𝑓.𝐶𝑝,𝑓.(𝑇𝑓,𝑜 ― 𝑇𝑓,𝑖)
(2)
where, 𝑚𝑓 denotes the mass flow rate of fluid through the tubes, 𝑇𝑓,𝑖and 𝑇𝑓,𝑜represent the fluid inlet and outlet temperatures, respectively and 𝐶𝑝,𝑓is the fluid specific heat. The volume fraction of nanoparticles in the base fluid (𝜙), can be estimated from Eq.(3): 𝑚𝑛/𝜌𝑛
𝜙 = 𝑚𝑛/𝜌𝑛 + 𝑚𝑓/𝜌𝑓
(3)
where,𝑚𝑛 and 𝑚𝑓 denotes nanoparticles and fluid mass (kg/s) respectively, 𝜌𝑛 is the density of nanoparticles while𝜌𝑓 is the density of fluid, specific heat and density of nanofluid is calculated using empirical relations [39]. Electrical conversion efficiency is given by Eq.(4). 𝐸𝑒𝑙
𝜂𝑒𝑙 ≡ 𝐸𝑖𝑛 = where,𝐼𝑠𝑐stands for short circuit current
𝑉𝑜𝑐 × 𝐼𝑠𝑐 × 𝐹𝐹 𝐺𝑒𝑓𝑓
(4)
and 𝑉𝑜𝑐 stands for open circuit voltage, 𝐺𝑒𝑓𝑓
represents effective irradiance given by solar power meter. Filled factor (FF) is a characteristic associated with PV panel and it depends on its material properties. FF can be calculated from Eq. (5). 𝑃𝑚
𝐹𝐹 = 𝑉𝑜𝑐 × 𝐼𝑠𝑐
(5)
Journal Pre-proof where, 𝑃𝑚 represents the maximum electrical power generated i.e., the ideal electrical yield, 𝑉𝑜𝑐and 𝐼𝑠𝑐are the open circuit voltage and short circuit current respectively. 𝑃𝑚 can be calculated from Eq. (6). 𝑃𝑚 = 𝑉𝑚 × 𝐼𝑚
(6)
where, 𝑉𝑚and 𝐼𝑚represent maximum current and voltage of PV module. Exergy loss can be estimated by subtracting the output exergy from input exergy [40]. Input exergy is the exergy associated with radiation coming from the sun while output exergy is the sum of electrical and thermal exergies. Since electrical energy from PV panel is the available useful work, therefore electrical exergy is equal to the electrical energy. All these exergies are calculated using Eq.(7) to Eq. (9).
(
𝐸𝑥𝑠𝑢𝑛 = 𝐺𝑒𝑓𝑓 1 ―
)
𝑇𝑎𝑚𝑏 𝑇𝑠𝑢𝑛
(7)
where, 𝐸𝑥𝑠𝑢𝑛represents exergy from the sun in W/m2, 𝐺𝑒𝑓𝑓 is irradiance level, 𝑇𝑎𝑚𝑏is the ambient temperature, and 𝑇𝑠𝑢𝑛is the sun temperature in oC. (8)
𝐸𝑥𝑒𝑙 = 𝐸𝑒𝑙 where, 𝐸𝑥𝑒𝑙 represents electrical exergy and 𝐸𝑒𝑙 signifies electrical energy.
(
𝐸𝑥𝑡ℎ = 𝐸𝑡ℎ 1 ―
𝑇𝑎𝑚𝑏 + 273 𝑇𝑓𝑜 ― 273
)
(9)
where, 𝐸𝑥𝑡ℎrepresents thermal exergy, 𝐸𝑡ℎis the thermal power. Finally, the entropy generation in W/K.m2 is calculated using Eq.(10a): 𝑆𝑔𝑒𝑛 =
𝐸𝑥𝑙𝑜𝑠𝑠 𝑇𝑎𝑚𝑏
(10a)
where, 𝑆𝑔𝑒𝑛represents entropy generation and 𝐸𝑥𝑙𝑜𝑠𝑠is the exergy loss. In order to get accurate and reliable results of experiments uncertainty analysis was performed for various parameters and electrical efficiency involved in the experimental study [14]. The uncertainties associated with the measuring instruments of the experimental setup
Journal Pre-proof are listed in the Table 3. If ‘S’ is a function ‘n’ independent linear parameters as S=S(w1, w2,…wn) the uncertainty of function ‘S’ is given by Eq.(10b): 𝛿𝑆 =
(
2 ∂𝑆 𝛿𝑤 1 ∂𝑤1
) +(
2 ∂𝑆 𝛿𝑤 2 ∂𝑤2
)
+…+
(
2 ∂𝑆 𝛿𝑤 𝑛 ∂𝑤𝑛
)
where 𝛿𝑆 is the uncertainty of function ‘S’, 𝛿𝑤𝑖 is the uncertainty of parameter wi, and
(10b) ∂𝑆 ∂𝑤𝑖
is
the partial derivative of ‘S’ with respect to parameter wi. The uncertainties associated with the the experiments are found to be less than 4% for all the cases considered in this study. Table 3: Equipment’s and their uncertainties involved in the Experimentation. Equipment Parameter Accuracy Maximum Uncertainty (In Experiment) Multimeter (Digital) Voltage ±(0.5%) 0.05V Multimeter (Digital) Current ±(0.6%) 0.02A o K-type thermocouple Fluid and ambient ±0.3 C 0.15 oC Temperature Solar power meter Irradiance ±10 W/m2 5.3 W/m2 Flowmeter Mass flow rate ±2.5 kg/h 1.18kg/h o Temperature Gun PV surface temperature ±0.5 C 0.25 oC
2.6 Optimization using fuzzy TOPSIS Fuzzy TOPSIS method has been used in this study for multi-performance optimization of the nanofluid cooled HPVTS which comprises of the following steps: Step 1: Collect information about importance weights of the output responses in linguistic terms such as extremely low, low, high, extremely high etc. from the decision makers/experts. Step 2: Convert the linguistic terms into equivalent numeral values using particular fuzzy numbers such as triangular, trapezoidal, pentagonal etc. fuzzy numbers. Step 3: Determine the aggregated fuzzy weight of the output responses. Step 4: Normalize the performance matrix.
Journal Pre-proof Step 5: Determine the weighted normalized matrix. Step 6: Determine the positive and negative ideal solutions. Step 7: Determine the distance of the actual results from the positive and negative ideal solutions. Step 8: Determine the closeness coefficient (CC) values and rank the experiments/alternatives based on these values in such a way that the experiment with highest CC value gets rank 1 and the rank decreases as the CC value decreases. Table 3 shows the linguistic equivalents allotted to the importance weights of the output responses. Triangular fuzzy numbers were used to describe the linguistic equivalents. A decision making panel comprising of four experts individually assessed the importance weight of each output response in linguistic equivalents. Table 4 shows the experts response and Table 5 shows the aggregated fuzzy weights of the output responses. Normalized performance index was used to achieve the optimized condition. Eq. (11) was used to obtain the normalized performance matrix. 𝑡𝑚𝑛 =
𝑧𝑚𝑛 16 ∑𝑚 = 1𝑧2𝑚𝑛
(11)
where, zmn is the original value of mth attributeof nth experiment and 𝑡𝑚𝑛 is the corresponding normalized value. The performance matrix P was obtained by the multiplication of normalized performance matrix and the aggregated fuzzy weights of output responses. The positive and negative ideal value sets i.e. 𝑃 + and 𝑃 ― were obtained using Eq.(12) and Eq. (13) respectively. 𝑃+
= {[max (𝑝𝑚𝑛)|𝑛 𝜖 𝑁] 𝑜𝑟 [min (𝑝𝑚𝑛)|𝑛 𝜖 𝑁′], 𝑛 = 1,2,3,…..,16} = {𝑝1+ , 𝑝2+ ,…., 𝑝5+ } (12)
Journal Pre-proof 𝑃―
= {[min (𝑝𝑚𝑛)|𝑛 𝜖 𝑁] 𝑜𝑟 [max (𝑝𝑚𝑛)|𝑛 𝜖 𝑁′], 𝑛 = 1,2,3,…..,16} = {𝑝1― , 𝑝2― ,…., 𝑝5― } (13)
where, N = {n= 1,2,…,5 |n} : Associated with output responses (higher the better) N’ = {n= 1,2,…,5 |n} : Associated with output responses (lower the better) In this study, two output responses i.e. overall efficiency and electrical efficiency were of higher the better type whereas exergy loss, surface temperature, and entropy generation were preferably lower the better type. In the weighted normalized fuzzy decision matrix, the values are normalized positive triangular fuzzy numbers belonging to the interval [0,1]. Eq. (14) and Eq. (15) were used to compute the distance between the actual results and the positive and negative ideal solutions respectively. 5
∑ 𝛿(𝑝
+ = 𝛿𝑚
𝑚𝑛,
𝑝𝑛+ ), 𝑚 = 1,2,…,16
(14)
𝑝𝑛― ), 𝑚 = 1,2,…,16
(15)
𝑚=1
5
― 𝛿𝑚
∑ 𝛿(𝑝
=
𝑚𝑛,
𝑚=1
where,𝛿(𝑢,𝑣) represents the distance between two fuzzy numbers i.e. (u1, u2, u3) and (v1, v2, v3) which was computed using Eq. (16).
𝛿(𝑢,𝑣) =
[(𝑢1 ― 𝑣1)2 + (𝑢2 ― 𝑣2)2 + (𝑢3 ― 𝑣3)2] 3
(16)
The closeness of an experimental trial which leads to ideal solution is articulated by a coefficient termed as the closeness coefficient (CCm), determined using Eq.(17).
𝐶𝐶𝑚 =
𝛿𝑚― 𝛿𝑚+ + 𝛿𝑚―
(17)
Journal Pre-proof Table 3: Weight assigned to linguistic variables for each output response Sivapirakasam et al., [28] Importance Extremely low Very low Low Medium High Very high Extremely high
Abbreviation EL VL L M H VH EH
Fuzzy weight (0,0,0.1) (0,0.1,0.3) (0.1,0.3,0.5) (0.3,0.5,0.7) (0.5,0.7,0.9) (0.7,0.9.1.0) (0.9,1.0,1.0)
Table 4: Feedback on output responses given by experts Output response Experts E1 E2 E3 E4 Overall efficiency VH H VH EH Exergy loss EL L L VL Surface temperature L M L L Entropy generation EL VL VL L Electrical efficiency EH VH VH EH
Table 5: Fuzzy weights Output responses Fuzzy weight Overall efficiency (0.70, 0.875, 0.975) Exergy loss (0.05, 0.15, 0.30) Surface temperature (0.15, 0.35, 0.55) Entropy generation (0.025, 0.125, 0.30) Electrical efficiency (0.80, 0.95, 1.00) 3. Results and Discussion Using Eq. (11) to Eq. (17), CCm value for each experimental trial of the L16 OA was computed and these are shown in Table 6. It is evident from Table 6, input parameters combination of experimental trial No. 10 gives the maximum value of the closeness coefficient (CCm= 0.712136). Thus, experiment No. 10, consequently becomes the best combination of input parameters that is responsible for desired output responses (i.e. the best multi-performance characteristics) among the sixteen experiments simultaneously.
Journal Pre-proof Table 6: Closeness coefficients Expt. No. Irradiance Ambient (W/m2) temperature (oC) 1 400 25 2 400 30 3 400 35 4 400 40 5 600 25 6 600 30 7 600 35 8 600 40 9 800 25 10 800 30 11 800 35 12 800 40 13 1,000 25 14 1,000 30 15 1,000 35 16 1,000 40
Flow rate (l/min)
Concentration of the nanofluid (%vol.)
0.5 1 1.5 2 1 0.5 2 1.5 1.5 2 0.5 1 2 1.5 1 0.5
0.1 0.3 0.5 0.7 0.5 0.7 0.1 0.3 0.7 0.5 0.3 0.1 0.3 0.1 0.7 0.5
Closeness coefficient (CCm) 0.616786 0.347937 0.381579 0.240092 0.542833 0.618227 0.589635 0.442365 0.696126 0.712136 0.678149 0.442744 0.572063 0.524265 0.552478 0.577194
Taguchi suggested the use of the response table to obtain optimum combination of the input parameters. Therefore, it was employed and for each level of independent parameters the closeness coefficient was calculated. The average value of the closeness coefficient for each level of the input parameters was calculated and these values are listed in the response table shown in Table 7. Table 7: Response table for closeness coefficient Input parameter Average closeness coefficient Level 1 Level 2 Level 3 Level 4 Irradiance 0.3966 0.5483 0.6323 0.5565 Ambient temperature 0.6070 0.5506 0.5505 0.4256 Flow rate 0.6226 0.4715 0.5111 0.5285 Concentration of the nanofluid 0.5434 0.5101 0.5534 0.5267
Max-min 0.2357 0.1814 0.1511 0.0433
Irrespective of the performance characteristic of the output responses, whether they are of higher the better or lower the better type, a higher value of the closeness coefficient is desired which corresponds to better performance. Thus, based on the CCm values shown in Table 7, it was found that the optimum multi-performance of the nanofluid cooled HPVTS was obtained for 800 W/m2 irradiance (level 3), 25oC ambient temperature (level 1), 0.5 l/min flow rate
Journal Pre-proof (level 1), and 0.5 %vol. concentration of the nanofluid (level 3). As evident from Table 7, the difference in the maximum and the minimum values of the closeness coefficient for HPVTS input parameters were as follow: 0.2357 for irradiance, 0.1814 for ambient temperature, 0.1511 for flow rate and 0.0433 for concentration of the nanofluid. The most influential input variable affecting performance parameters was ascertained by comparing these values. The most influential input parameter was the one having maximum of these values. The maximum of these values was 0.2357 which indicated that among the four input parameters, the irradiance had the strongest effect on the multi-performance characteristics of the nanofluid cooled HPVTS. The level of importance of the input parameters to the multiperformance characteristics of the nanofluid cooled HPVTS in decreasing order is as follows: irradiance, ambient temperature, flow rate and concentration of the nanofluid. As observed from the closeness coefficient, the best suitable parameters for optimum performance in the given range of input variables were 800 W/m2 irradiance (level 3), 25oC ambient temperature (level 1), 0.5 l/min flow rate (level 1), and 0.5 %vol. concentration of the nanofluid (level 3). Various studies [13-15] showed that the best range of irradiance for better electrical power conversion is from 800 to 900w/m2. It is obvious that when the flow rate is low, the nanofluid gets enough time to be in contact with the walls of the tube and absorbs more heat. Low level of the flow rate suggested by the optimisation technique used in the present study (0.5 l/min) gives the best performance for the thermal efficiency of the HPVTS because at low flow rate, heat transfer rate of the fluid circulating in tubes increases which causes reduction in the temperature of the photovoltaic/thermal leading to improvement in its current and voltage [41, 42]. Most of the researchers [20, 21, 43-46] concluded that low level of particle concentration is suitable because at higher level problem of agglomeration starts due to which the nanofluid loses its stability and also its inherent heat transfer capability. The optimisation result of this study also suggests 0.5% vol. concentration to be the best level within the range
Journal Pre-proof of parameters considered. Similarly, low ambient temperature is required for better performance of HPVTS because at higher temperature cell temperature rises, sometime it becomes twice the ambient temperature, which may deteriorate the electrical performance. Prolonged high temperature further results in structural damage due to development of thermal stresses. Therefore, the response from the fuzzy-TOPSIS method is in concurrence with the feasible technical outcomes given by the various researchers [19-21]. 4. Conclusion, limitations and scope for future research 4.1 Conclusions Production of cleaner and sustainable energy is the need of hour to fulfil an ever increasing demand for energy and to protect environment from pollution. Researchers have been putting concerted efforts to devise ways and means to produce clean energy. Abundance of the cleanest energy in the form of solar energy is available as a nature’s gift. Channelizing and converting solar energy into some other useful forms such as thermal and electrical energy is essentially required to enhance energy generation economically. It can be achieved through the use of HPVTS. Heating of the PV panel due to solar energy affects its performance and therefore, there is a need for cooling it. This paper proposed a cooling mechanism in which specially developed nanofluid was used to cool down the PV panel. Further, it also proposed a combined Taguchi and fuzzy TOPSIS methods to solve multi-response optimization problem as the performance of the HPVTS is affected by several critical input parameters. Taguchi’s L16 OA was used to explore effect of input parameters on the output responses. Triangular fuzzy numbers were used to determine fuzzy weighting factors of the output responses. Fuzzy TOPSIS was used to rank the sixteen experimental runs. Based on the closeness coefficient, optimum combination of the input parameters and their levels was determined using Taguchi’s response table method. The optimal performance of the HPVTS was obtained for 800 W/m2 irradiance (level 3), 25oC ambient temperature (level 1), 0.5 l/min
Journal Pre-proof flow rate (level 1), and 0.5 %vol. concentration of the nanofluid (level 3). The result depicted by optimisation technique is in consonance with experimental results available in the archival literature and give a physically realistic combination of input parameters levels. It was observed that highest value of closeness coefficient (0.2357) was achieved for irradiance, therefore it was the most significant input parameter following ambient temperature, flow rate and concentration of nanofluid that affect the multi-performance characteristic of the HPVTS. This paper demonstrated that Taguchi based fuzzy TOPSIS method was quite efficient and effective in solving the multi-response optimization problem considered in this study.
4.2 Limitations of the study Every study is associated with certain limitations and therefore, it is essential to highlight them. Following are the limitations of the present study:
Serpentine design of cooling tubes was used in this study.
Only one type of nanofluid i.e. Zn-(water+EG) was considered, for performance investigation.
It considered only four input parameters each at four levels and five output responses pertaining to the HPVTS.
It only emphasized the main effect of the input parameters and did not consider the combined or interaction effect of the input parameters on the multi-performance of the HPVTS.
It collected responses from only a few decision makers/experts to compute weighting factors of the output responses.
4.3 Scope for future research
Journal Pre-proof As such it is indeed difficult to suggest scope for future research because the scope does not have any limit. However, following suggestions are made to be incorporated in future studies:
Different design of heat exchanger can be employed in the HPVTS.
Different metallic, non-metallic and carbon based nanoparticle can be used to prepare nanofluid.
More input parameters with more levels may be considered.
Multi-performance optimization considering interaction or combined effects of the input parameters may be carried out.
More number of input parameters and output responses may be considered.
Opinion from more number of decision makers or experts may be obtained to determine weighting factors of the output responses.
Other fuzzy based multi-attribute decision making methods such as fuzzy-VIKOR, fuzzy-PROMETHEE, fuzzy-ELECTRE etc. may be used.
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Journal Pre-proof Syed Mohd. Yahya: Conceptualization, Methodology, supervision, Reviewing and Editing Ibrahim Ahmed Qeays: Data curation, writing. Zahid A. Khan : Writing- Original draft preparation , Software, Investigation. Mohammad Asjad: Software, Validation.
Journal Pre-proof Highlights
Zn/(ethylene glycol+water) nanofluid is prepared for cooling PV panel. Performance evaluation of hybrid photovoltaic thermal system (HPVTS). Four input parameters each at four levels and five output responses of the HPVTS are considered. Sixteen experiments as per Taguchi’s L16 orthogonal array are conducted. Multi-performance optimization is carried out using integrated fuzzy approach.
Ibrahim Ahmed Qeays, Syed Mohd. Yahya, Mohammad Asjad, Zahid A. Khan PII:
S0959-6526(20)30498-4
DOI:
https://doi.org/10.1016/j.jclepro.2020.120451
Reference:
JCLP 120451
To appear in:
Journal of Cleaner Production
Received Date:
12 October 2019
Accepted Date:
05 February 2020
Please cite this article as: Ibrahim Ahmed Qeays, Syed Mohd. Yahya, Mohammad Asjad, Zahid A. Khan, Multi-performance optimization of nanofluid cooled hybrid photovoltaic thermal system using fuzzy integrated methodology, Journal of Cleaner Production (2020), https://doi.org/10.1016/j. jclepro.2020.120451
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Journal Pre-proof Multi-performance optimization of nanofluid cooled hybrid photovoltaic thermal system using fuzzy integrated methodology Ibrahim Ahmed Qeays1 , Syed Mohd. Yahya1,*, Mohammad Asjad2 , Zahid A. Khan2 1Sustainable
Energy & Acoustics Research Lab, Mechanical Engineering, Z.H.C.E.T, Aligarh Muslim University,Aligarh-202002, India.
2Department
of Mechanical Engineering, Jamia Millia Islamia University,New Delhi-110025, India.
Abstract In the event of rapidly depleting conventional sources of energy such as fossil fuels and an urge for protecting environment from pollution, there is a thrust, across the globe, to produce cleaner and sustainable energy. Solar energy is one such form which is seen as a future source of energy. Solar energy is converted into electrical and thermal energy with the help of a hybrid photovoltaic thermal system (HPVTS).Performance of the PV panel may be affected when it gets heated due to high ambient temperature and other reasons. Proper cooling of PV panel may protect it from heating which may prevent deterioration in its performance. This paper proposes a mechanism for cooling the PV panel by circulating nanofluid around it. It also experimentally investigates the effect of critical input parameters such as irradiance, ambient temperature, flow rate, and concentration of the nanofluid on the nanofluid cooled HPVTS attributes (output responses) like overall efficiency, exergy loss, surface temperature, entropy generation, and electrical efficiency using the Taguchi’s L16 orthogonal array (OA). Weighting factors are calculated using Triangular fuzzy numbers (TFN) for output responses and optimal setting of the input parameters is obtained using TOPSIS. Keywords: Solar energy; Electrical energy; Thermal energy; Nanofluid cooled HPVTS; Multi-attribute decision making; TOPSIS
*corresponding author email: [email protected]
Journal Pre-proof 1. Introduction Two events of world in the last three decades are responsible for the major economic crash: (i) Middle East war in October 1973 and (ii) huge surge in global oil prices during 2008 to 2014. These events generated researchers’ interest in solar energy worldwide [1, 2]. Further, these economic crises resulted in serious concern globally in the consumption and production of oil, which had already achieved its zenith [3]. Pareto et al. [4] demonstrated the interplay between oil prices and inflation that affected investments and also shifted the usage of coal and gas at a large scale. It is emphasized that inflation with continuous depleting oil fields, will create a hike in energy prices, and this will go on until we replaces the fossil fuels by sustainable sources of energy. One such clean source of energy i.e. solar energy can be harnessed in two possible ways: (i) thermal energy coming from the sun can be exploited by constructing solar collectors with heat absorbers like water or any other fluid and (ii) light energy can be harnessed by semiconductor photovoltaic (PV) panels which convert light energy directly to DC current. Solar radiation is an energy source with tremendous potential for developing countries, particularly those which are located in temperate and tropical zones [5]. PV cells have a wide range of application and are clean and eco-friendly source for power production [6]. Photovoltaic systems have various advantages such as independent operation, reliable long life, no fuel and fumes, easy installation, low maintenance, and low recurring costs etc. as compared to other sources [7]. However, the PV systems tend to get heated due to absorption of unwanted heat from sun during operation which obstructs their efficiency. The absorbed heat causes serious problems including increase in the working temperature of the PV cell [8, 9].Increase in cell temperature depletes its electrical power conversion rate. Increase of one degree Celsius in the working temperature of a c-Si cells based PV system resulted in a 0.45% decrease in its efficiency [10]. To get rid of this situation, PV panel in conjunction
Journal Pre-proof with thermal system is used which is termed as hybrid PV thermal system (HPVTS). In this system unwanted heat from PV panel is removed by additional thermal unit which acts as a heat exchanger. Therefore, HPVTS produces both electricity and heat simultaneously. HPVTSs have been studied considerably using different methodology such as analytical methods, experimental measurements, and numerical simulations etc. Al-Shamani et al. [11] used various nanofluids made up of metallic oxide and carbide (SiO2, TiO2 and SiC) in photovoltaic thermal (PV/T) collector. Their experimental results showed that the PV/T collector with SiC nanofluid had maximum electrical efficiency of 13.52% and overall efficiency of 81.73% at a constant flow rate of 0.170 kg/s and insolation level of 1000 W/m2, followed by PV/T-TiO2nanofluids, PV/T-SiO2 nanofluids, and PV/T-water respectively. Yun and Qunzhi [12] applied MgO-water nanofluid over the silicon PV panel in order to reduce the heat of solar cells. The particle size of the nanofluid used was approximately 10 nm and the concentrations were 0.02%wt., 0.06%wt. and 0.1%wt. They found that there was no gain in electrical conversion efficiency as compared to the traditional PV system but overall efficiency was enhanced. Rejeb et al.[13] performed a parametric investigation of a photovoltaic thermal (PV/T) system. They tested two types of nanoparticles (Al2O3 and Cu) mixed in different base fluids (pure water and ethylene glycol) with varying concentration (0.1, 0.2 and 0.4%wt.).Results of their study revealed that the best thermal and electrical efficiency was attained by metallic water based nanofluid in comparison to ethylene glycol (EG) based metallic oxide nanofluid. Sardarabadi et al. [14] employed silica/water nanofluid to investigate the performance of a PVT system. Silica nanoparticles of 11-14 nm diameter were mixed with water in two concentrations i.e. 1% and 3% by weight. They reported an increase of 7.6% and 12.8% in thermal efficiency for nanofluid with 1% wt. and 3% wt. respectively. Further, they also observed that the overall efficiency was also upgraded to 3.6% for 1% wt. Whereas, it went down to 7.9% for 3% wt. Al-Waeli et al. [15] observed
Journal Pre-proof that by adding 3% wt. of SiC nanoparticles to water increased the fluid density and viscosity by 0.0082% and 1.8% respectively which increased the electrical efficiency by 24.1%. Radwan et al. [17] studied a novel cooling method for low concentrated photovoltaic thermal system by introducing a micro channel heat sink with different nanofluids (Al2O3 and SiC) at different concentrations. They found SiC-water more effective than Al2O3–water as higher reduction in the temperature was achieved in case of the SiC-water nanofluid. Their results showed a drop in the temperature of the PV cell to 38°C and improvement of 19% in electrical efficiency with the use nanofluids. Karami and Rahimi et al. [18] studied the behaviour of the water based nanofluid with small amount of Boehmite for the PV/T system. They made two different configurations, one was straight channel and other was helical channel. They found that temperature drop was higher in case of nanofluid as compared to water. Further, they reported that the electrical efficiencies of PV/T system using straight and helical channels were 20.57% and 37.67% respectively. Thus, they recommended that the helical configuration with Boehmite nanofluid was a better option to increase the performance of the PV/T system. Al-Waeli et al. [19] studied the effect of nanofluids on the efficiency of PV/T system. They used water based nanofluids with different concentrations of Al2O3, CuO and SiC nanoparticles in their study which revealed that the SiC-water nanofluid provided the best thermal efficiency among all nanofluids. They also found that the thermal efficiency was enhanced by 1.96%, 3.42% and 4.8% for Al2O3, CuO and SiC respectively when 4%vol was used. Bellos and Tzivanidis [20] examined the effects of nanofluid, stored in the storage tank of different capacities, on thermal and electrical efficiencies of a hybrid PV/T system. The result of their experiment showed that for Cu/water nanofluid stored in 150litre tank, the electrical efficiency was enhanced by 1.49% and the thermal efficiency was increased by 4.35%. They also found that the mean yearly thermal efficiency of the PV/T system for Cu based nanofluid was 43.8% which was 42% that of water and the mean yearly
Journal Pre-proof electrical efficiency increased from 12.4% to 12.5% when the results were compared with water. Lari et al. [40] investigated the performance of PV system experimentally using Ag/water nanofluids. The results of their study showed that there was an increase in electrical output by 8.5% and an increase in thermal output by 13% when the performance was compared with the water cooled system. Hasan et al.[21] performed an experiment using nanofluid as a coolant to investigate the efficiencies associated with PV/T system. They used a setup which consisted of 36 nozzles and 4 parallel tubes. The nozzles injected the nanofluid on the back of the PV system. Three water based nanofluids made of three nanoparticles i.e. SiC, TiO2 and SiO₂ were used in their experiment. They reported that the use of SiC/water nanofluid resulted in the best performance of the PV/T system and the electrical, thermal and overall efficiency of the system in case of SiC nanoparticle was 12.75%, 85% and 97.75% respectively. From the studies conducted by several researchers presented above, it is evident that performance of the nanofluid cooled HPVTS is greatly affected by certain critical input parameters of the system. Consequently, optimization of the input parameters is necessarily required to derive optimum performance of the system. The decision to select optimum input parameters to achieve best performance of HPVTS involves conflicting criteria which calls for application of Multi-Attribute Decision Making (MADM) methods. Among several MADM methods, techniques such as Analytic Hierarchy Process (AHP) [22], Technique for Order Preference by Simulation of Ideal Solution (TOPSIS) [23] VIKOR [24] and gray relational analysis [25] are generally used for tackling problems related to engineering application. These techniques were employed by many researchers to solve different types of decision problems [26-30]. Tong & Su [31] suggested that TOPSIS is the best suited MADM method to solve decision problems as it has the ability to tackle both continuous and discrete data for multiple response
Journal Pre-proof problems. It is based on the basic idea of selecting the alternative which is closest in distance to the positive ideal solution and farthest in distance from the negative ideal solution. Researchers have emphasized for a long time that the uncertainty, subjectivity and vagueness involved in real life multi criteria decision making (MCDM) problems further complicates and increases the complexity of the problems [32]. Most commonly, linguistic variables such as very low, low, high, very high etc. are used to reduce the vagueness of the data. This is achieved by employing the fuzzy set theory [33, 34] which reduces the uncertainty in the data with the help of linguistic variables. Linguistic variables can be represented by different sets of numbers like triangular, trapezoidal, pentagonal, etc. fuzzy number sets. Researchers have used various fuzzy based MCDM methods to solve decision problems. Rani et al. [35] applied VIKOR approach in conjunction with fuzzy set to explore and investigate the renewable energy technologies in India. Lin et al. [36] used fuzzy technique along with weighted probability method for the optimal design of thermal energy storage system based on phase change material. Their study revealed that the design obtained using this technique was consistent with physical scenario. James et al. [37] applied fuzzy- MADM approach for the assessment of green supplier. Their results confirmed the validity of the proposed model and ensured the efficacy to solve the selection problem of green suppliers and further improvement strategies. Wang et al.[38] performed analysis to choose the best available technology for solid waste management by incorporating the fuzzy-MCDM approach. They used linguistic variable to rate the alternatives and found the weight of evaluation criteria. The model developed by them was helpful for stakeholders to determine the priority sequence of attribute. MADM based studies in solar applications such as solar collector design, HPVTS system optimisation are rare in the literature. Especially, the application employing fuzzy-TOPSIS for multi-performance optimization of HPVTS is missing in archival literature. Therefore,
Journal Pre-proof multi-performance optimisation of HPVTS using fuzzy-TOPSIS method makes the work worthy and novel. This work aims at selecting the optimum input parameters of the nanofluid cooled HPVTS which yields optimum performance of the system. Taguchi L16 orthogonal array is used as design of experiments in this study. This study considers four input parameters of the HPVTS i.e. irradiance, ambient temperature, flow rate and concentration of the nanofluid each at four levels. Triangular fuzzy numbers (TFN) are used to determine the relative importance of output responses. Finally TOPSIS is used for each experimental trial for the calculation of overall performance index values and the optimum combination of factor levels.
2. Materials and methodology 2.1 Experimental setup The detailed diagram of the experimental setup consisting of different components is shown in Fig. 1. It consists of one 300W poly-crystalline silicon photovoltaic module (Vikram Solar Co., India). Collectors used in this system are made up of copper tubes which are placed beneath the PV cells in serpentine structure to cool down the PV surface temperature. The nanofluid is circulated via pump through the cooling circuit installed at the rear side of panel with a steady constant mass flow rate. A counter flow design shell-and-tube heat exchanger is used to cool the nanofluid after it gets heated in the cooling circuit of HPVTS. Temperature of the nanofluid at the inlet and outlet of the cooling circuit and the heat exchanger are recorded by k-type thermocouple mounted over there. Surface temperatures of PV module cells are taken by temperature gun. The electrical circuit comprises of charge controllers, storage batteries, and consumers DC load (here a 100W bulb is used). To ensure a continuous electricity production by HPVTS, charge controllers act as a connecting switch between batteries, consumers load and solar panels. For measuring short-circuit and open-circuit
Journal Pre-proof currents and voltages, digital multimeters are used while hand held solar power meter is employed for measuring radiation.
Flow meter
S
TS
TS: Panel surface temperature S: Solar power meter
T2 PV Panel
TA: Ambient temperature sensor TA Tank
T1 Pump
Voltmeter Ammeter
T3
Charge Controller
Heat Exchanger
Battery
Load
Fig. 1: Schematic of the experimental setup where T1, T2 andT3 are thermocouple attached to the system for temperature measurement of inlet and outlet. The red colour path represents thermal circuit while blue colour path represent electric circuit.
2.2 Preparation of the nanofluid Zn-(EG+water) nanofluid with four concentration (in % volume) of nanoparticle i.e. 0.1%, 0.3%, 0.5% and 0.7% was used in this study. Zinc nanoparticles of APS 40nmwas purchased from SRL lab. Different sample (Fig.2) as per requirement of concentration was made by dispersing Zn particles in a solution of 50% deionised water and 50% EG. After that sonication process was performed through probe type sonicator (Make: WENSAR, Model:
Journal Pre-proof Pro-500) for 180 minutes. During the sonication process, surfactant CTAB is added to make the colloidal solution stable. After completion of the sonication process, the final nanofluid was prepared and subsequently used in the experimental study. Zeta potential test was performed for all samples to measure the stability of the nanofluids using Zeta Sizer (make: Malvern Panalytical, UK; NanoZS90), the zeta potential value was found to be 60mV at a nanoparticle concentration of 0.5%vol., which shown good stability. However for 0.1%, 0.3% and 0.7% vol. concentration the zeta potential values were 25mV, 38mV and 45mV respectively, showing moderate stability.
Fig. 2: Prepared nanofluid of Zn-water/EG with 0.5% concentaration.
2.3 Input parameters and their levels Based on the available literature, four input parameters of the HPVTS i.e. irradiance, ambient temperature, flow rate, and concentration of the nanofluid were considered in this study [1419]. The feasible space for the input parameters was defined by varying the irradiance in the range from 400 to 1000 W/m2, the ambient temperature in the range from 25 to 40oC, the flow rate in the range from 0.5 to 2 l/min, and the concentration of the nanofluid in the range
Journal Pre-proof from 0.1 to 0.7 %vol. Four levels of each input parameter were selected. The input factors and their levels are shown in Table 1. Table 1: Input parameters and their levels Input parameters Symbol Irradiance A Ambient temperature B Flow rate C Concentration of the nanofluid D
Unit W/m2 oC l/min %vol.
Level 1 400 25 0.5 0.1
Level 2 600 30 1.0 0.3
Level 3 Level 4 800 1,000 35 40 1.5 2.0 0.5 0.7
2.4 Taguchi design of experiments In the Taguchi design of experiment, specially designed orthogonal arrays (OAs) are used to study the entire input parameter range with relatively fewer numbers of experiments compared to full factorial design. An OA consists of a predefined number of rows and columns. Rows of the OA represents number of experimental trials and columns represent allocation of the input parameters and their interactions. Selection of a suitable OA depends on the number of input parameters and their levels involved in the study. An OA whose degrees of freedom is either equal to or more than the degrees of freedom of the complete range of input factors and their interactions and which can accommodate the selected levels of input parameters is suitable for use in the study. The degrees of freedom of an OA are defined as total number of experiments minus one. Similarly, the degrees of freedom of an input parameter are specified as number of levels of that parameter minus one. In the present study four input parameters each at four levels are considered. Degrees of freedom of each input parameter are 3 (4 – 1). Thus, the total degrees of freedom of all four input parameters are 12. An orthogonal array whose degrees of freedom are at least 12 and also it can accommodate four input parameters each at four levels can be suitably used in this study. Thus, L16 OA having 15 degrees of freedom (16 – 1) was used to experimentally ascertain the effect of input factors on the output responses as its degrees of freedom is higher than the overall degrees of freedom of all four input factors and it can also accommodate four levels
Journal Pre-proof parameters. It may be noted that in this study only main effect of the input parameters was investigated and combined/interaction effect of the parameters was not explored. Further, five attributes (output responses) of the nanofluid cooled HPVTS were selected in this study the details of which are provided in section 2.5. The L16 OA along with the values of five attributes of each experiment is shown in Table 2.
Table 2: L16 OA and values of output responses Expt. No. Input parameters Output responses Overall Exergy Surface Entropy efficiency loss temperature generation (%) (W/m2) (oC) A B C D (W/K.m2) 1 400 25 0.5 0.1 41.83 512 32 2.11 2 400 30 1 0.3 30.85 501 34.6 2.17 3 400 35 1.5 0.5 35.23 506 38 2.21 4 400 40 2 0.7 32.41 535 42 2.75 5 600 25 1 0.5 42.14 510 35 2.25 6 600 30 0.5 0.7 50.87 542 41 2.31 7 600 35 2 0.1 48.60 520 38 2.40 8 600 40 1.5 0.3 44.63 502 45 2.79 9 800 25 1.5 0.7 53.62 580 37 2.42 10 800 30 2 0.5 55.30 550 40.5 2.47 11 800 35 0.5 0.3 58.27 592 44 2.75 12 800 40 1 0.1 48.75 610 48 2.96 13 1,000 25 2 0.3 48.65 565 42 2.62 14 1,000 30 1.5 0.1 52.34 602 46 2.73 15 1,000 35 1 0.7 56.20 595 50 2.85 16 1,000 40 0.5 0.5 62.35 715 56 2.91
Electrical efficiency (%) 13.38 13.20 13.11 12.98 13.45 13.41 13.12 13.14 13.71 13.85 13.60 13.35 13.89 13.10 13.15 13.57
2.5 Output responses and their measurement In this study the output responses were evaluated using instruments and empirical correlation depending upon the type of response. As discussed earlier, PV surface temperature was measured using temperature gun in the vicinity of the panel at different locations, and then an average value was used as a measure of surface temperature. In HPVTS framework, the overall efficiency (ηPVT) is basically the ratio of output energy and the input energy for a chosen time interval and it is given by Eq.(1). ηPVT is indeed sum of the electrical and thermal efficiencies (ηel and ηth) respectively.
Journal Pre-proof
𝜂𝑃𝑉𝑇≅
𝑡2
𝐸𝑡ℎ + 𝐸𝑒𝑙
∫𝑡1(𝐴𝑐𝐸′′𝑡ℎ + 𝐴𝑃𝐸′′𝑒𝑙)𝑑𝑡
𝐸𝑖𝑛
𝐴𝑐∫𝑡1(𝐺′′𝑒𝑓𝑓)𝑑𝑡
⟹ 𝜂𝑃𝑉𝑇 =
𝑡2
= 𝜂𝑡ℎ +𝑟.𝜂𝑒𝑙
(1)
where, 𝐸𝑡h and 𝐸𝑒𝑙 are thermal and electrical power output (in Watt) produced by HPVTS respectively and 𝐸𝑖𝑛 is the input power form sun, Ap and Ac are the solar panel area (m2) and the collector tubes area respectively, with Ap/Ac as a packing factor denoted by ‘r’, 𝐸′′𝑡ℎ represents the rate of thermal energy yield per unit area of collector tubes, 𝐸′′𝑒𝑙 denotes the rate of electrical energy yield per unit area of PV panel, 𝐺′′𝑒𝑓𝑓denotes the effective irradiance, t1 and t2 are lower and upper limits of the time interval chosen for collecting data. Based on simple energy analysis, 𝐸𝑡ℎcan be determined using Eq. (2). 𝐸𝑡ℎ = 𝑚𝑓.𝐶𝑝,𝑓.(𝑇𝑓,𝑜 ― 𝑇𝑓,𝑖)
(2)
where, 𝑚𝑓 denotes the mass flow rate of fluid through the tubes, 𝑇𝑓,𝑖and 𝑇𝑓,𝑜represent the fluid inlet and outlet temperatures, respectively and 𝐶𝑝,𝑓is the fluid specific heat. The volume fraction of nanoparticles in the base fluid (𝜙), can be estimated from Eq.(3): 𝑚𝑛/𝜌𝑛
𝜙 = 𝑚𝑛/𝜌𝑛 + 𝑚𝑓/𝜌𝑓
(3)
where,𝑚𝑛 and 𝑚𝑓 denotes nanoparticles and fluid mass (kg/s) respectively, 𝜌𝑛 is the density of nanoparticles while𝜌𝑓 is the density of fluid, specific heat and density of nanofluid is calculated using empirical relations [39]. Electrical conversion efficiency is given by Eq.(4). 𝐸𝑒𝑙
𝜂𝑒𝑙 ≡ 𝐸𝑖𝑛 = where,𝐼𝑠𝑐stands for short circuit current
𝑉𝑜𝑐 × 𝐼𝑠𝑐 × 𝐹𝐹 𝐺𝑒𝑓𝑓
(4)
and 𝑉𝑜𝑐 stands for open circuit voltage, 𝐺𝑒𝑓𝑓
represents effective irradiance given by solar power meter. Filled factor (FF) is a characteristic associated with PV panel and it depends on its material properties. FF can be calculated from Eq. (5). 𝑃𝑚
𝐹𝐹 = 𝑉𝑜𝑐 × 𝐼𝑠𝑐
(5)
Journal Pre-proof where, 𝑃𝑚 represents the maximum electrical power generated i.e., the ideal electrical yield, 𝑉𝑜𝑐and 𝐼𝑠𝑐are the open circuit voltage and short circuit current respectively. 𝑃𝑚 can be calculated from Eq. (6). 𝑃𝑚 = 𝑉𝑚 × 𝐼𝑚
(6)
where, 𝑉𝑚and 𝐼𝑚represent maximum current and voltage of PV module. Exergy loss can be estimated by subtracting the output exergy from input exergy [40]. Input exergy is the exergy associated with radiation coming from the sun while output exergy is the sum of electrical and thermal exergies. Since electrical energy from PV panel is the available useful work, therefore electrical exergy is equal to the electrical energy. All these exergies are calculated using Eq.(7) to Eq. (9).
(
𝐸𝑥𝑠𝑢𝑛 = 𝐺𝑒𝑓𝑓 1 ―
)
𝑇𝑎𝑚𝑏 𝑇𝑠𝑢𝑛
(7)
where, 𝐸𝑥𝑠𝑢𝑛represents exergy from the sun in W/m2, 𝐺𝑒𝑓𝑓 is irradiance level, 𝑇𝑎𝑚𝑏is the ambient temperature, and 𝑇𝑠𝑢𝑛is the sun temperature in oC. (8)
𝐸𝑥𝑒𝑙 = 𝐸𝑒𝑙 where, 𝐸𝑥𝑒𝑙 represents electrical exergy and 𝐸𝑒𝑙 signifies electrical energy.
(
𝐸𝑥𝑡ℎ = 𝐸𝑡ℎ 1 ―
𝑇𝑎𝑚𝑏 + 273 𝑇𝑓𝑜 ― 273
)
(9)
where, 𝐸𝑥𝑡ℎrepresents thermal exergy, 𝐸𝑡ℎis the thermal power. Finally, the entropy generation in W/K.m2 is calculated using Eq.(10a): 𝑆𝑔𝑒𝑛 =
𝐸𝑥𝑙𝑜𝑠𝑠 𝑇𝑎𝑚𝑏
(10a)
where, 𝑆𝑔𝑒𝑛represents entropy generation and 𝐸𝑥𝑙𝑜𝑠𝑠is the exergy loss. In order to get accurate and reliable results of experiments uncertainty analysis was performed for various parameters and electrical efficiency involved in the experimental study [14]. The uncertainties associated with the measuring instruments of the experimental setup
Journal Pre-proof are listed in the Table 3. If ‘S’ is a function ‘n’ independent linear parameters as S=S(w1, w2,…wn) the uncertainty of function ‘S’ is given by Eq.(10b): 𝛿𝑆 =
(
2 ∂𝑆 𝛿𝑤 1 ∂𝑤1
) +(
2 ∂𝑆 𝛿𝑤 2 ∂𝑤2
)
+…+
(
2 ∂𝑆 𝛿𝑤 𝑛 ∂𝑤𝑛
)
where 𝛿𝑆 is the uncertainty of function ‘S’, 𝛿𝑤𝑖 is the uncertainty of parameter wi, and
(10b) ∂𝑆 ∂𝑤𝑖
is
the partial derivative of ‘S’ with respect to parameter wi. The uncertainties associated with the the experiments are found to be less than 4% for all the cases considered in this study. Table 3: Equipment’s and their uncertainties involved in the Experimentation. Equipment Parameter Accuracy Maximum Uncertainty (In Experiment) Multimeter (Digital) Voltage ±(0.5%) 0.05V Multimeter (Digital) Current ±(0.6%) 0.02A o K-type thermocouple Fluid and ambient ±0.3 C 0.15 oC Temperature Solar power meter Irradiance ±10 W/m2 5.3 W/m2 Flowmeter Mass flow rate ±2.5 kg/h 1.18kg/h o Temperature Gun PV surface temperature ±0.5 C 0.25 oC
2.6 Optimization using fuzzy TOPSIS Fuzzy TOPSIS method has been used in this study for multi-performance optimization of the nanofluid cooled HPVTS which comprises of the following steps: Step 1: Collect information about importance weights of the output responses in linguistic terms such as extremely low, low, high, extremely high etc. from the decision makers/experts. Step 2: Convert the linguistic terms into equivalent numeral values using particular fuzzy numbers such as triangular, trapezoidal, pentagonal etc. fuzzy numbers. Step 3: Determine the aggregated fuzzy weight of the output responses. Step 4: Normalize the performance matrix.
Journal Pre-proof Step 5: Determine the weighted normalized matrix. Step 6: Determine the positive and negative ideal solutions. Step 7: Determine the distance of the actual results from the positive and negative ideal solutions. Step 8: Determine the closeness coefficient (CC) values and rank the experiments/alternatives based on these values in such a way that the experiment with highest CC value gets rank 1 and the rank decreases as the CC value decreases. Table 3 shows the linguistic equivalents allotted to the importance weights of the output responses. Triangular fuzzy numbers were used to describe the linguistic equivalents. A decision making panel comprising of four experts individually assessed the importance weight of each output response in linguistic equivalents. Table 4 shows the experts response and Table 5 shows the aggregated fuzzy weights of the output responses. Normalized performance index was used to achieve the optimized condition. Eq. (11) was used to obtain the normalized performance matrix. 𝑡𝑚𝑛 =
𝑧𝑚𝑛 16 ∑𝑚 = 1𝑧2𝑚𝑛
(11)
where, zmn is the original value of mth attributeof nth experiment and 𝑡𝑚𝑛 is the corresponding normalized value. The performance matrix P was obtained by the multiplication of normalized performance matrix and the aggregated fuzzy weights of output responses. The positive and negative ideal value sets i.e. 𝑃 + and 𝑃 ― were obtained using Eq.(12) and Eq. (13) respectively. 𝑃+
= {[max (𝑝𝑚𝑛)|𝑛 𝜖 𝑁] 𝑜𝑟 [min (𝑝𝑚𝑛)|𝑛 𝜖 𝑁′], 𝑛 = 1,2,3,…..,16} = {𝑝1+ , 𝑝2+ ,…., 𝑝5+ } (12)
Journal Pre-proof 𝑃―
= {[min (𝑝𝑚𝑛)|𝑛 𝜖 𝑁] 𝑜𝑟 [max (𝑝𝑚𝑛)|𝑛 𝜖 𝑁′], 𝑛 = 1,2,3,…..,16} = {𝑝1― , 𝑝2― ,…., 𝑝5― } (13)
where, N = {n= 1,2,…,5 |n} : Associated with output responses (higher the better) N’ = {n= 1,2,…,5 |n} : Associated with output responses (lower the better) In this study, two output responses i.e. overall efficiency and electrical efficiency were of higher the better type whereas exergy loss, surface temperature, and entropy generation were preferably lower the better type. In the weighted normalized fuzzy decision matrix, the values are normalized positive triangular fuzzy numbers belonging to the interval [0,1]. Eq. (14) and Eq. (15) were used to compute the distance between the actual results and the positive and negative ideal solutions respectively. 5
∑ 𝛿(𝑝
+ = 𝛿𝑚
𝑚𝑛,
𝑝𝑛+ ), 𝑚 = 1,2,…,16
(14)
𝑝𝑛― ), 𝑚 = 1,2,…,16
(15)
𝑚=1
5
― 𝛿𝑚
∑ 𝛿(𝑝
=
𝑚𝑛,
𝑚=1
where,𝛿(𝑢,𝑣) represents the distance between two fuzzy numbers i.e. (u1, u2, u3) and (v1, v2, v3) which was computed using Eq. (16).
𝛿(𝑢,𝑣) =
[(𝑢1 ― 𝑣1)2 + (𝑢2 ― 𝑣2)2 + (𝑢3 ― 𝑣3)2] 3
(16)
The closeness of an experimental trial which leads to ideal solution is articulated by a coefficient termed as the closeness coefficient (CCm), determined using Eq.(17).
𝐶𝐶𝑚 =
𝛿𝑚― 𝛿𝑚+ + 𝛿𝑚―
(17)
Journal Pre-proof Table 3: Weight assigned to linguistic variables for each output response Sivapirakasam et al., [28] Importance Extremely low Very low Low Medium High Very high Extremely high
Abbreviation EL VL L M H VH EH
Fuzzy weight (0,0,0.1) (0,0.1,0.3) (0.1,0.3,0.5) (0.3,0.5,0.7) (0.5,0.7,0.9) (0.7,0.9.1.0) (0.9,1.0,1.0)
Table 4: Feedback on output responses given by experts Output response Experts E1 E2 E3 E4 Overall efficiency VH H VH EH Exergy loss EL L L VL Surface temperature L M L L Entropy generation EL VL VL L Electrical efficiency EH VH VH EH
Table 5: Fuzzy weights Output responses Fuzzy weight Overall efficiency (0.70, 0.875, 0.975) Exergy loss (0.05, 0.15, 0.30) Surface temperature (0.15, 0.35, 0.55) Entropy generation (0.025, 0.125, 0.30) Electrical efficiency (0.80, 0.95, 1.00) 3. Results and Discussion Using Eq. (11) to Eq. (17), CCm value for each experimental trial of the L16 OA was computed and these are shown in Table 6. It is evident from Table 6, input parameters combination of experimental trial No. 10 gives the maximum value of the closeness coefficient (CCm= 0.712136). Thus, experiment No. 10, consequently becomes the best combination of input parameters that is responsible for desired output responses (i.e. the best multi-performance characteristics) among the sixteen experiments simultaneously.
Journal Pre-proof Table 6: Closeness coefficients Expt. No. Irradiance Ambient (W/m2) temperature (oC) 1 400 25 2 400 30 3 400 35 4 400 40 5 600 25 6 600 30 7 600 35 8 600 40 9 800 25 10 800 30 11 800 35 12 800 40 13 1,000 25 14 1,000 30 15 1,000 35 16 1,000 40
Flow rate (l/min)
Concentration of the nanofluid (%vol.)
0.5 1 1.5 2 1 0.5 2 1.5 1.5 2 0.5 1 2 1.5 1 0.5
0.1 0.3 0.5 0.7 0.5 0.7 0.1 0.3 0.7 0.5 0.3 0.1 0.3 0.1 0.7 0.5
Closeness coefficient (CCm) 0.616786 0.347937 0.381579 0.240092 0.542833 0.618227 0.589635 0.442365 0.696126 0.712136 0.678149 0.442744 0.572063 0.524265 0.552478 0.577194
Taguchi suggested the use of the response table to obtain optimum combination of the input parameters. Therefore, it was employed and for each level of independent parameters the closeness coefficient was calculated. The average value of the closeness coefficient for each level of the input parameters was calculated and these values are listed in the response table shown in Table 7. Table 7: Response table for closeness coefficient Input parameter Average closeness coefficient Level 1 Level 2 Level 3 Level 4 Irradiance 0.3966 0.5483 0.6323 0.5565 Ambient temperature 0.6070 0.5506 0.5505 0.4256 Flow rate 0.6226 0.4715 0.5111 0.5285 Concentration of the nanofluid 0.5434 0.5101 0.5534 0.5267
Max-min 0.2357 0.1814 0.1511 0.0433
Irrespective of the performance characteristic of the output responses, whether they are of higher the better or lower the better type, a higher value of the closeness coefficient is desired which corresponds to better performance. Thus, based on the CCm values shown in Table 7, it was found that the optimum multi-performance of the nanofluid cooled HPVTS was obtained for 800 W/m2 irradiance (level 3), 25oC ambient temperature (level 1), 0.5 l/min flow rate
Journal Pre-proof (level 1), and 0.5 %vol. concentration of the nanofluid (level 3). As evident from Table 7, the difference in the maximum and the minimum values of the closeness coefficient for HPVTS input parameters were as follow: 0.2357 for irradiance, 0.1814 for ambient temperature, 0.1511 for flow rate and 0.0433 for concentration of the nanofluid. The most influential input variable affecting performance parameters was ascertained by comparing these values. The most influential input parameter was the one having maximum of these values. The maximum of these values was 0.2357 which indicated that among the four input parameters, the irradiance had the strongest effect on the multi-performance characteristics of the nanofluid cooled HPVTS. The level of importance of the input parameters to the multiperformance characteristics of the nanofluid cooled HPVTS in decreasing order is as follows: irradiance, ambient temperature, flow rate and concentration of the nanofluid. As observed from the closeness coefficient, the best suitable parameters for optimum performance in the given range of input variables were 800 W/m2 irradiance (level 3), 25oC ambient temperature (level 1), 0.5 l/min flow rate (level 1), and 0.5 %vol. concentration of the nanofluid (level 3). Various studies [13-15] showed that the best range of irradiance for better electrical power conversion is from 800 to 900w/m2. It is obvious that when the flow rate is low, the nanofluid gets enough time to be in contact with the walls of the tube and absorbs more heat. Low level of the flow rate suggested by the optimisation technique used in the present study (0.5 l/min) gives the best performance for the thermal efficiency of the HPVTS because at low flow rate, heat transfer rate of the fluid circulating in tubes increases which causes reduction in the temperature of the photovoltaic/thermal leading to improvement in its current and voltage [41, 42]. Most of the researchers [20, 21, 43-46] concluded that low level of particle concentration is suitable because at higher level problem of agglomeration starts due to which the nanofluid loses its stability and also its inherent heat transfer capability. The optimisation result of this study also suggests 0.5% vol. concentration to be the best level within the range
Journal Pre-proof of parameters considered. Similarly, low ambient temperature is required for better performance of HPVTS because at higher temperature cell temperature rises, sometime it becomes twice the ambient temperature, which may deteriorate the electrical performance. Prolonged high temperature further results in structural damage due to development of thermal stresses. Therefore, the response from the fuzzy-TOPSIS method is in concurrence with the feasible technical outcomes given by the various researchers [19-21]. 4. Conclusion, limitations and scope for future research 4.1 Conclusions Production of cleaner and sustainable energy is the need of hour to fulfil an ever increasing demand for energy and to protect environment from pollution. Researchers have been putting concerted efforts to devise ways and means to produce clean energy. Abundance of the cleanest energy in the form of solar energy is available as a nature’s gift. Channelizing and converting solar energy into some other useful forms such as thermal and electrical energy is essentially required to enhance energy generation economically. It can be achieved through the use of HPVTS. Heating of the PV panel due to solar energy affects its performance and therefore, there is a need for cooling it. This paper proposed a cooling mechanism in which specially developed nanofluid was used to cool down the PV panel. Further, it also proposed a combined Taguchi and fuzzy TOPSIS methods to solve multi-response optimization problem as the performance of the HPVTS is affected by several critical input parameters. Taguchi’s L16 OA was used to explore effect of input parameters on the output responses. Triangular fuzzy numbers were used to determine fuzzy weighting factors of the output responses. Fuzzy TOPSIS was used to rank the sixteen experimental runs. Based on the closeness coefficient, optimum combination of the input parameters and their levels was determined using Taguchi’s response table method. The optimal performance of the HPVTS was obtained for 800 W/m2 irradiance (level 3), 25oC ambient temperature (level 1), 0.5 l/min
Journal Pre-proof flow rate (level 1), and 0.5 %vol. concentration of the nanofluid (level 3). The result depicted by optimisation technique is in consonance with experimental results available in the archival literature and give a physically realistic combination of input parameters levels. It was observed that highest value of closeness coefficient (0.2357) was achieved for irradiance, therefore it was the most significant input parameter following ambient temperature, flow rate and concentration of nanofluid that affect the multi-performance characteristic of the HPVTS. This paper demonstrated that Taguchi based fuzzy TOPSIS method was quite efficient and effective in solving the multi-response optimization problem considered in this study.
4.2 Limitations of the study Every study is associated with certain limitations and therefore, it is essential to highlight them. Following are the limitations of the present study:
Serpentine design of cooling tubes was used in this study.
Only one type of nanofluid i.e. Zn-(water+EG) was considered, for performance investigation.
It considered only four input parameters each at four levels and five output responses pertaining to the HPVTS.
It only emphasized the main effect of the input parameters and did not consider the combined or interaction effect of the input parameters on the multi-performance of the HPVTS.
It collected responses from only a few decision makers/experts to compute weighting factors of the output responses.
4.3 Scope for future research
Journal Pre-proof As such it is indeed difficult to suggest scope for future research because the scope does not have any limit. However, following suggestions are made to be incorporated in future studies:
Different design of heat exchanger can be employed in the HPVTS.
Different metallic, non-metallic and carbon based nanoparticle can be used to prepare nanofluid.
More input parameters with more levels may be considered.
Multi-performance optimization considering interaction or combined effects of the input parameters may be carried out.
More number of input parameters and output responses may be considered.
Opinion from more number of decision makers or experts may be obtained to determine weighting factors of the output responses.
Other fuzzy based multi-attribute decision making methods such as fuzzy-VIKOR, fuzzy-PROMETHEE, fuzzy-ELECTRE etc. may be used.
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Journal Pre-proof Syed Mohd. Yahya: Conceptualization, Methodology, supervision, Reviewing and Editing Ibrahim Ahmed Qeays: Data curation, writing. Zahid A. Khan : Writing- Original draft preparation , Software, Investigation. Mohammad Asjad: Software, Validation.
Journal Pre-proof Highlights
Zn/(ethylene glycol+water) nanofluid is prepared for cooling PV panel. Performance evaluation of hybrid photovoltaic thermal system (HPVTS). Four input parameters each at four levels and five output responses of the HPVTS are considered. Sixteen experiments as per Taguchi’s L16 orthogonal array are conducted. Multi-performance optimization is carried out using integrated fuzzy approach.