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1

Estimation of Photovoltaic Module Performance with L-Shaped Aluminum Fins Using Weather Data

Hamdan Mohammad, Abdelhafez Eman, Musa Akram, Ajib Salman

Journal of Ecological Engineering

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2024

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Vol. 25, nr 1

336--344

EN

Photovoltaic (PV) power prediction is vital for efficient and effective solar energy utilization within the energy ecosystem. It enables grid stability, cost savings, and the seamless integration of solar power into the broader energy infrastructure. In this work, previously obtained data on the estimation of the power produced by a PV, which is cooled by L-shaped aluminum fins attached to the backside of the PV at different spacings, is used to predict the power produced by the PV. This is achieved by employing both neural network models and multiple linear regression (MLR) techniques to assess the correlation between power generated by PV with L-shaped aluminum fins and its input variables. Two distinct approaches were employed for this purpose. The first approach involved the conventional MLR model, while the second utilized a neural network, specifically the multilayer perceptron (MLP) model. The estimated outcomes were subsequently compared against the previously measured data. The MLP model showed a great ability to identify the relationship between input and output variables, it was noted. The statistical error study provided evidence of data mining’s acceptable accuracy when using the MLP model. Conversely, the results indicated that the MLR technique exhibited the least ability to estimate the power generated by PV with L-shaped aluminum fins.

2

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

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2024

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Vol. 18, no 4

329--335

EN

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%.

3

Utilization of Desulfurized Heavy Liquid Fuel Blends in Domestic Boiler

Al Aboushi Ahmad, Abdelhafez Eman, Hamdan Mohammad, Ajib Salman, Alsaqoor Sameh

Journal of Ecological Engineering

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2023

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Vol. 24, nr 7

225--233

EN

One way to cut down the consumption of diesel fuel in domestic heating in Jordan is to blend it with shale oil, which may be extracted from oil shale. This leads to a cut down in the national fuel bill in Jordan. Unfortunately, shale oil contains significant amounts of sulfur as impurities and upon burning sulfur oxides are emitted causing a negative environmental impact, and hence desulfurization of such fuel blends is essential. This may be achieved by adding activated carbon to the fluids. The process of removing sulfur from shale oil is crucial for safeguarding the environment, human well-being, and equipment, as well as meeting regulatory requirements and creating superior-quality goods. In this study, a domestic boiler was utilized to evaluate the degree of desulfurization process of blends of diesel and shale oil fuels upon their burning in a domestic boiler, to achieve this, blends of both fuels were prepared with varying amounts of shale oil (10%, 20%, 30%, and 40%) and various amounts of activated carbon were added to the prepared mixtures of diesel fuel and shale oil. The assessment of performance included examining the environmental impact, specifically by analyzing exhaust gases to measure the concentration of Sulfur Oxide (SO2). It was found that an increase in the concentration of shale oil in the mixture led to an increase in the concentration of SO2. However, adding more activated carbon to the mixture from the fuels resulted in a decrease in the SO2 concentration. The lowest SO2 concentration was observed when 1g of activated carbon was added per liter of the fuel mixture at a 20% concentration of shale oil, and 0.6g of activated carbon per liter of the fuel mixture at a 40% concentration of oil shale.

4

The Effect of Nanomaterial Type on Water Disinfection Using Data Mining

Hamdan Mohammad, Khalil Rana Haj, Abdelhafez Eman, Ajib Salman

Journal of Ecological Engineering

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2023

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Vol. 24, nr 4

244--251

EN

Multiple linear regression and artificial neural network (ANN) models were utilized in this study to assess the type influence of nanomaterials on polluted water disinfection. This was accomplished by estimating E. coli (E.C) and the total coliform (TC) concentrations in contaminated water while nanoparticles were added at various concentrations as input variables, together with water temperature, PH, and turbidity. To achieve this objective, two approaches were implemented: data mining with two types of artificial neural networks (MLP and RBF), and multiple linear regression models (MLR). The simulation was conducted using SPSS software. Data mining was revealed after the estimated findings were checked against the measured data. It was found that MLP was the most promising model in the prediction of the TC and E.C concentration, s followed by the RBF and MLR models, respectively.

5

Enhancement of Solar Water Disinfection Using Nanocatalysts

Hamdan Mohammad, Al Louzi Razan, Al Aboushi Ahmad, Abdelhafez Eman

Journal of Ecological Engineering

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2022

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Vol. 23, nr 12

14--20

EN

Solar water disinfection (SODIS) is a simple and low-cost method of increasing water quality. However, it takes about 6 hours of exposure to solar radiation. The elimination of harmful pathogenic germs from drinking water can be accelerated using a combination of sun disinfection and nanotechnology. In this study, a hybrid water purification technique using solar water disinfection, Titanium Oxide (TiO2), and natural mineral clays was investigated. TiO2, natural kaolin clay nanoparticles, and a mixture of TiO2 and natural clay were added to contaminated wastewater containers at different concentrations. After that, the containers were exposed to sun light for different time intervals. Samples were then collected from all tests to measure the total counts of Total Coliform and Escherichia coli (E. coli) using the IDEXX system. The results showed that the addition of TiO2 and natural kaolin clay to wastewater with solar water disinfection reduced the total count of the pathogenic microorganisms and decreased the time needed time for the disinfection process compared to using solar energy alone. The results also showed that the optimum concentration of the TiO2, which yielded the shortest purification time and lowest levels of pathogenic microorganisms, was 0.006 g/ml. In co ntrast, the most effective concentratio n of natural clay was 0.0015 g/ml. Moreover, the results showed that the optimum concentration of the mixture of TiO2 and natural clay, which speeds up the purification time an d lowest the level of pathogen ic microorganisms was 0.006 g/ml for TiO2 and 1.2 g/ml for the natural clay.

6

Forecasting Air Pollution with Sulfur Dioxide Emitted from Burning Desulfurized Diesel Using Artificial Neural Network

Hmadan Mohammad A., Abdelhafez Eman, Shawabkeh Reyad

Ecological Engineering & Environmental Technology

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2021

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Vol. 22, iss. 5

97--102

EN

Concentrations of emitted pollutants in the atmosphere are influenced by the emission sources and metrological data. In Jordan, Diesel fuel is considered to be a main source of SO2, which has negative impact on air quality. In this work, the emitted SO2 during the burning of desulfurized diesel fuel using activated carbon is conducted using three types of Artificial Neural Network (Elman, NARX and Feedforward models). To accomplish this, previously experimental work on desulfurization of diesel fuel using two types of activated carbon was adopted. Metrological data involving the average daily temperature (T), relative humidity (RH), wind speed (WS), pressure (P), concentration of Particulate Matter (PM10) and average daily solar radiation (SR) over the period from 2/1/2020 to 30/12/2020. It was found that NARX model is the most accurate model in the furcating process of SO2, flowed by Elman and feedforward was found to be the least capable model in predicting the SO2 emitted concentration.

7

Estimation of Water Disinfection by Using Data Mining

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

Ecological Engineering & Environmental Technology

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2021

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Vol. 22, iss. 1

109--116

EN

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.