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Enhancing Photovoltaic Panel Performance through Hybrid Nanoparticle Cooling: A Study on Zinc Oxide and Aluminum Oxide Nanofluids

Alkhalil Shahnaz, Altork Yousef, Sukkariyh Mustafa, Abdelhafez Eman

Advances in Science and Technology. Research Journal

2024

Vol. 18, no 4

329--335

High operating temperatures, particularly under conditions of high solar irradiation have adverse effects on the performance of the photovoltaic (PV) panels. The efficiency of electricity generation decreases with an increase in operating temperature, and therefore, minimizing the operating temperature is essential. Thus, efficient cooling systems are of significant importance, particularly in areas with scorching heat during the day. Hybrid nanoparticles have been identified as one of the most effective methods in utilizing the concept of PV cooling because of their special characteristics that can help improve the efficiency of solar panels in the long run. These nanoparticles offer the best heat dissipation and convective heat transfer alongside better light trapping and stability and are relatively cheaper to produce, thus playing a central role in enhancing the cooling effectiveness in photovoltaic systems. In our view, depending on these combined forces, hybrid nanoparticles can enhance the general effectiveness, dependability, and efficacy of solar panels as a high-potential instrument for solar power extraction. This study sought to determine the most effective ZnO and Al₂O₃ Nanofluids concentrations in improving the performance of PV modules. Five PV modules were placed side by side. One of them was a reference sample; the other four were coated on the backside with a range of hybrid nanofluid concentrations. K-type thermocouples were used to monitor the hourly backside thermal profile of each module to ensure thermal integrity. Moreover, a data logger monitored the current and the voltage of each PV during the experiment. In general, the coated modules had significantly better results compared to the control. The best improvement in the generated output power was obtained when 0. 4% Al₂O₃ and 0.2% ZnO reached 28.4% and increased efficiency to 29.6%.

Estimation of Water Disinfection by Using Data Mining

Bashayreh Esra'a, Manasrah Ahmad, Alkhalil Shahnaz, Abdelhafez Eman

Ecological Engineering & Environmental Technology

2021

Vol. 22, iss. 1

109--116

In this study, the Artificial Neural Network (ANN) models and multiple linear regression techniques were used to estimate the relation between the concentration of total coliform, E. coli and Pseudomonas in the wastewater and the input variables. Two techniques were used to achieve this objective. The first is a classical technique with multiple linear regression models, while the second one is data mining with two types of ANN (Multilayer Perceptron (MLP) and Radial Basis Function (RBF). The work was conducted using (SPSS) software. The obtained estimated results were verified against the measured data and it was found that data mining by using the RBF model has good ability to recognize the relation between the input and output variables, while the statistical error analysis showed the accuracy of data mining by using the RBF model is acceptable. On the other hand, the obtained results indicate that MLP and multiple linear regression have the least ability for estimating the concentration of total coliform, E. coli and pseudomonas in wastewater.