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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

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.

3

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.