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In this paper, a concentrated photovoltaic-thermal solar dryer (CPV/TSD) using nano-enhanced PCM (Al2O3Paraffin wax) is experimentally studied. A comprehensive thermodynamic analysis of the system according to the first and second laws is discussed. Besides, the drying parameters (moisture content and moisture ratio) are predicted using the two machine learning algorithms (ANN and SVM) and compared the prediction success with four evaluation metrics (R2, rRMSE, MBE, and rMAE). The overall thermal energy efficiency and exergy efficiency of the CPV/TSD system are found to be 20% and 8%, respectively. Although solar radiation to the environment has decreased a lot, it has been found that the thermal energy transferred to the nano-enhanced PCM prevents the decrease in greenhouse temperature for the first 100 min. In the system, mushrooms are dried from the initial moisture content of 17.45 g water/g dry matter to the final moisture content of 0.0515 g water/g dry matter. Then the drying rate value for CPV/TSD system is calculated to be 0.436 g matter/g dry matter.min. On the other hand, even if both ANN and SVM algorithms have exhibited very satisfying results, ANN is coming to the fore in the prediction of the drying parameters considering all evaluation metrics together. WOS:000637191800006 2-s2.0-85102023951

Solar thermal systems are generally utilized for providing sustainable and environmentally friendly thermal energy that can be used in different applications. In the present study, a temperature-controlled fluidized bed solar drying system along with flat plate and plate with zigzag fins has been designed, manufactured, and experimentally tested. In the first step, the thermal behavior of designed solar air collectors has been numerically modeled. In the next step, the drying system's performance has been experimentally investigated. The overall efficiency of the system was found to be 64%. The maximum exergy efficiency of flat and zigzag plate solar air collectors was calculated as 7.2% and 11.6%, respectively. Then moisture content (MC) and moisture ratio (MR) values were modelled by response surface methodology (RSM), and the predicted results were compared with four metrics. It was found that the drying parameters were highly fitted with the mathematical models. MC metric was predicted with accurate values for performance criteria of R2, R-RMSE, and MBE as 0.9995, 1.94%, and ?0.0096, respectively. The general outcomes of numerical, experimental and modeling analyses of this research exhibited successfulness of the developed the fluidized bed solar drying system. © 2021 Elsevier Ltd 2-s2.0-85119097624

The globalising world, rapidly developing technology and growing population have brought many problems and led to disrupting the world ecological balance. Today, existing energy sources reached such a level that cannot meet the current needs of the world. Due to the fact that fossil fuels will run out in near future, it has made mankind tending to seek alternative energy sources. The main issues addressed in this paper are the history of greenhouse gas emissions (GHGe) and relations with nuclear energy policy, particularly in Turkey. Currently, nuclear has much less GHGe and high energy-efficiency, and also meets 10.6% of the world primary electiricty energy demand. Therefore, countries seriously began to evaluate nuclear energy instead of fossil-fuels. In line with this, Turkey started to build third nuclear plants and aims to meet at least 15% of its primary electiricity energy demand. Hereby, Turkey is not only reducing dependence on fossil-fuels but also planning to reach the undertaken GHGe level as a country of signed the Kyoto protocol. WOS:000684131100005 2-s2.0-85112330847

Agbulut, Umit/0000-0002-6635-6494 WOS: 000518657000001 The generation of electrical energy with photovoltaic modules is a highly useful and environmentally friendly method. For this reason, studies to increase the electrical energy production from photovoltaic (PV) modules have gained great importance. Concentrated PV systems constitute a significant part of these studies. The major problems with the concentrated PV systems are the risks of lowering the efficiency of the cells (i.e., concentration process increases PV cell temperature) and the thermal damage that can occur with sudden temperature increases. In order to avoid these risks, various applications are used to cool concentrated PV modules. In this study, an active system, which was developed for cleaning and cooling PV modules, was tested. The aim of the present study was to ensure that the surfaces were clean and free of external, contaminating factors such as dust and dirt, and that the PV cells were cooled. During the experiments, two different systems were compared: the system with the cleaning-cooling processes and the one without these processes. Prior to starting experiments, a hydrophobic liquid onto the surfaces of the PV modules was applied to facilitate the cleaning process. The results of the experiments revealed that the temperature of the PV module was 50 degrees C in the cleaning-cooling process and 67 degrees C in the system without the cleaning-cooling process. On the other hand, it was observed that the proposed design increased the power output of PV module up to 40%.

Gurel, Ali Etem/0000-0003-1430-8041 WOS: 000408180900008 In this study, we examined the effect of the use of paraffin wax on the panel temperature of concentrated solar panels. Some tests were performed on various days for three months (including spring, summer, and winter months). With the help of the experimental results, new approaches have been made as concentrator ratio, area ratio, concentrator efficiency and power coefficient. As a result of this approaches the concentrator ratio was found to be 3. When the concentrator ratio was 3, the panel temperature of the system with paraffin was found to be between 80 and 100 degrees C, while that of the other system was found to be above 100 degrees C. Panel efficiency might be reduced down to 10% at these temperatures. The study also included the development of a mathematical model of the changes in the panel temperature of the concentrated systems in which paraffin was not used. The temperature at the back of the panels could be calculated by this mathematical model, depending on the ambient temperature and the concentrated solar radiation. The correlation coefficient of the mathematical model was found to be 0.929. The test results of two days, which were not included in the model, were used to determine the reliability of the model, and the panel temperatures were calculated using the correlation coefficients of 0.933 and 0.966. (C) 2017 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

This study carried out in two stages. In the first stage, four different-sized layers were designed and manufactured for a concentrated photovoltaic system. These layers were used to change the concentration ratio and area ratio of the system. Furthermore, a new power coefficient equation with this paper is proposed to the literature for the determination of the system performance. In the second stage of the study, the power outputs measured in the study were predicted with four machine-learning algorithms, namely support vector machine, artificial neural network, kernel and nearest-neighbor, and deep learning. To evaluate the success of these machine learning algorithms, coefficient of determination (R-2), root mean squared error (RMSE), mean bias error (MBE), t-statistics (t-stat) and mean absolute bias error (MABE) have been discussed in the paper. The experimental results demonstrated that the double-layer application for the concentrator has ensured better results and enhanced the power by 16%. The average concentration ratio for the double-layer was calculated to be 1.8. Based on these data, the optimum area ratio was determined to be 9 for this V-trough concentrator. Furthermore, the power coefficient was calculated to be 1.35 for optimum area ratio value. R-2 of all algorithms is bigger than 0.96. Support vector machine algorithm has generally presented better prediction results particularly with very satisfying R-2, RMSE, MBE, and MABE of 0.9921, 0.7082 W, 0.3357 W, and 0.6238 W, respectively. Then it is closely followed by kernel-nearest neighbor, artificial neural network, and deep learning algorithms, respectively. In conclusion, this paper is reporting that the proposed new power coefficient approach is giving more reliable results than efficiency data and the power output data of concentrated photovoltaic systems can be highly predicted with the machine learning algorithms. (c) 2020 Elsevier Ltd. All rights reserved. Karabuk University Scientific Research Projects Coordination UnitKarabuk University [KBU-BAP-15/1-YL-019] This study is supported by Karabuk University Scientific Research Projects Coordination Unit. Project Number: KBU-BAP-15/1-YL-019. WOS:000561594800091 2-s2.0-85086895804

Solar radiation (SR) knowledge plays a vital role in the design, modelling, and operation of solar energy conversion systems and future energy investment policies of the governments. However, these data are not measured for all regions due to the non-availability of SR measurement equipment at the weather stations. Therefore, SR has to be accurately predicted using various prediction models. In this research, four models from different classes are being used to predict monthly average daily global SR data. The models used in this study are based on a machine-learning algorithm (feed-forward neural network), empirical models (3 Angstrom-type models), time series (Holt-Winters), and mathematical model (RSM). As the prediction locations, four provinces (Ankara, Karaman, Kilis, and Sirnak) in Turkey are selected. The dataset including pressure, relative humidity, wind speed, ambient temperature, and sunshine duration is supplied from the Turkish State Meteorological Service and it covers the years 2008-2018. In the study, monthly average daily global SR data for the year 2018 is being predicted, and the performance success of the models is discussed in terms of the following benchmarks R-2, MBE, RMSE, MAPE, and t-stat. In the results, R-2 value for all models is varying between 0.952 and 0.993 and MAPE and RMSE value for all models is smaller than 10% and 2 MJ/m(2)-day, respectively. Evaluation in terms of t-stat value, no models exceed the t-critic limit. Considering all the models together, ANN has presented the best results with an average R-2, MBE, RMSE, MAPE, and t-stat of 0.9911, 0.1323 MJ/m(2)-day, 0.78 MJ/m(2)-day, 4.9263%, and 0.582, respectively. Then Holt-Winters, RSM, and empirical models closely followed it, respectively. (C) 2020 Elsevier Ltd. All rights reserved. Duzce University Research FundDuzce University [2019.07.04.1049]; Duzce UniversityDuzce University This study is supported by Duzce University Research Fund Project Number: 2019.07.04.1049. The authors are indebted to Duzce University for its financial support. WOS:000586917600008 2-s2.0-85087967783

Agbulut, Umit/0000-0002-6635-6494 WOS: 000494884000050 An experimental study was conducted to determine the effects of adding copper oxide (CuO) with 50 nm diameter into neat methanol (CH3OH) on the energetic and exergetic analysis of a concentrated air collector with vacuum tube heat pipe at different air velocities (1, 2 and 3 m/s). The experimental results clearly indicated that the nanofluid application enhanced the thermal properties and provided better performance for heat pipe applications. Considering all air velocities together, the average efficiency for neat methanol and CuO-Methanol nanofluid was achieved by 65% and 64%, respectively. Additionally, the average specific exergy outlet values for neat methanol and CuO-Methanol nanofluid were calculated as 206 J/kg and 298 J/kg, respectively. In the experiments, it is seen that the efficiency values of the system using the CuO-Methanol nanofluid reached higher values, as the solar radiation values were higher. In conclusions, this paper distinctly suggests that the presence of copper oxide in the base fluid can be used in a concentrated air collector with vacuum tube heat pipe particularly at the high radiation conditions and positively affects the performance of the system.

Gurel, Ali Etem/0000-0003-1430-8041 WOS: 000358483200040 Photovoltaic (PV) module converts the incoming solar radiation into heat and electric energy. Due to this heating feature of photovoltaic modules, it is likely to produce heat energy from PV modules as well. Such systems are known as photovoltaic and thermal systems in the literature. In this study, a new design PV/T system was experimentally analyzed for cooling photovoltaic modules while heating water at the same time. PV/Tis a forced circulation system without pump. Therefore, these systems are different from those described in the literature. For all PV/T system, step-by-step exergy analysis has been performed according to obtained experimental results. PV module thermal exergy, solar collector thermal exergy, and PV module electrical exergy variations have been calculated for this exergy analysis. Overall, exergy efficiency was obtained as about 17% for 45 degrees C set temperature and 21% for 55 degrees C set temperature. (c) 2015 American Institute of Chemical Engineers Environ Prog, 34: 1249-1253, 2015

Gurel, Ali Etem/0000-0003-1430-8041 WOS: 000415930200035 This study investigates convective-infrared drying characteristics of timber. The biggest disadvantage of infrared heating systems is the uncontrollable rise of the surface temperature up to extremely high values, which causes damage to the product. In this study, the power of an infrared system was controlled and adjusted according to the surface temperature of the product. In doing so, the surface temperature of the infrared heater was prevented from reaching up to values that could damage the product. During the analysis, the surface temperature of the timbers was kept at an average of 65 degrees C, while the bottom temperature was kept at an average of 45 degrees C. The air collector used in the system was found to be 50% efficient. At the end of a 13-h drying period, the moisture content of pine timbers could be reduced from 1.42 to 0.0948 g water/g dry matter. During the system analysis, parameters such as moisture content, mass diffusion coefficient, and useful energy rate were taken into consideration. (c) 2017 American Institute of Chemical Engineers Environ Prog, 36: 1875-1881, 2017 Scientific Research Projects Coordination Unit of Karabuk UniversityKarabuk University [KBU-BAP-16/1-BM-007] This work was supported by Scientific Research Projects Coordination Unit of Karabuk University (Project Number: KBU-BAP-16/1-BM-007).

Gurel, Ali Etem/0000-0003-1430-8041 WOS: 000317851600027 In this study, optimum insulation thicknesses, energy savings, and payback periods have been investigated based on life-cycle cost analysis for an external wall in four various cities from four climate zones of Turkey. Also, the study have been analyzed the effect of insulation thickness on release of CO2 and SO2 emissions. The results show that insulation thicknesses vary between 0.045 and 0.195 m, life-cycle energy savings between 13.26 and 248.9 $/m2, payback periods between 1.19 and 1.97 years when used extruded polystyrene as insulation material. When the optimum insulation thickness was used, emissions were reduced by 6292% depending on the city, fuel type, and insulation material. (c) 2012 American Institute of Chemical Engineers Environ Prog, 32: 365-372, 2013

Gurel, Ali Etem/0000-0003-1430-8041 WOS: 000373866800020 The purpose of the present study is PV/T system, which designed and implemented in order to reuse of thermal energy while increasing of electric production. Solar radiation was increased with the concentrator about two folds and electrical power gains were increased. A paraffin wax was used to store latent heat as a thermal energy storage. Thermal energy was combined with a greenhouse like air drier and used to dry the product. Thermal and electrical energy gain in the concentrated and non-concentrated panels were compared and total system efficiency was analyzed. The maximum temperature obtained at the back of the panel was calculated as 37 degrees C and the panel efficiency was 11% at mean solar radiation was 2000 W/m(2). (C) 2016 Elsevier Ltd. All rights reserved.