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Wyszukujesz frazę "Hamdan, Mohammad A." wg kryterium: Autor


Wyświetlanie 1-3 z 3
Tytuł:
Performance Enhancement of a Photovoltaic Module by Passive Cooling Using Water-Based Aluminum Oxide Nano-Fluid
Autorzy:
Hamdan, Mohammad A.
Powiązania:
https://bibliotekanauki.pl/articles/2086398.pdf
Data publikacji:
2022
Wydawca:
Polskie Towarzystwo Inżynierii Ekologicznej
Tematy:
PV cooling
nanotechnology
PV performance
fined PV
Opis:
The performance of a PV (photovoltaic) module relies heavily on the operating temperature. The aim of the current study was to improve PV performance by passive cooling with nano-coated aluminum fins attached to the backside of the photovoltaic panels. Four identical PV panels were installed side by side for simultaneous measurements. The first one (B) is a basic PV that was used for comparison purposes, the second one (N) PV, which is coated with water-based Al2O3 nano-fluid, the third is finned PV (F), with fins being attached to its backside and the Al2O3 nano-fluid coated fins are attached to the backside of the fourth PV (FN). The hourly electrical generated power by each PV, I-V, and I_V curves for each PV were recorded and stored using I-V Checker. In addition, the backside temperature of each PV and the ambient temperature were measured on an hourly basis using K-type thermocouples; the measured temperature values were stored in a data logger. It was found that the (FN) PV gave the best performance compared to the base unit, with an increase in the generated power by 5.77%, followed by the nanocoated (N) PV with an increase of 2.14% and finally the finned (F) PV with an increase of 0.74%. Furthermore, the PV with the nano-coated fins exhibits the lower temperature 31°C, followed by the nano-coated PV, and finally the fined PV, with the backside average temperature of the basic unit being 39°C.
Źródło:
Journal of Ecological Engineering; 2022, 23, 4; 276--283
2299-8993
Pojawia się w:
Journal of Ecological Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Air Quality Assessment and Forecasting Using Neural Network Model
Autorzy:
Hamdan, Mohammad A.
Ata, Mohammad F. Bani
Sakhrieh, Ahmad H.
Powiązania:
https://bibliotekanauki.pl/articles/1838288.pdf
Data publikacji:
2021
Wydawca:
Polskie Towarzystwo Inżynierii Ekologicznej
Tematy:
air pollutant
ANN
MATLAB
forecasting
Opis:
Air pollution is a major obstacle faced by all countries which impacts the environment, public health, socioeconomics, and agriculture. In this study, the air pollutants in the city of Amman were presented and analyzed. Nonlinear Autoregressive Exogenous (NARX) model was used to forecast the daily average levels of pollutants in Amman, Jordan. The model was built using the MATLAB software. The model utilized a Marquardt-Levenberg learning algorithm. Its performance was presented using different indices, R2 (Coefficient of Determination), R (Coefficient of Correlation), NMSE (Normalized Mean Square Error), and Plots representing network predictions vs original data. Historical measurements of air pollutants were obtained from 4 of the Ministry of Environment (MoEnv) air quality monitoring stations in Amman. The meteorological data representing three years (2015, 2016, and 2017) were used as predictors to train the Artificial Neural Network (ANN) while the data of the year 2018 were used to test it. The results showed good performance when forecasting SO2, O3, CO, and NO2, and acceptable performance when forecasting Particulate Matter (PM10) at the given 4 locations.
Źródło:
Journal of Ecological Engineering; 2021, 22, 6; 1-11
2299-8993
Pojawia się w:
Journal of Ecological Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Air Quality Assessment and Forecasting Using Neural Network Model
Autorzy:
Hamdan, Mohammad A.
Ata, Mohammad F. Bani
Sakhrieh, Ahmad H.
Powiązania:
https://bibliotekanauki.pl/articles/1838392.pdf
Data publikacji:
2021
Wydawca:
Polskie Towarzystwo Inżynierii Ekologicznej
Tematy:
air pollutant
ANN
MATLAB
forecasting
Opis:
Air pollution is a major obstacle faced by all countries which impacts the environment, public health, socioeconomics, and agriculture. In this study, the air pollutants in the city of Amman were presented and analyzed. Nonlinear Autoregressive Exogenous (NARX) model was used to forecast the daily average levels of pollutants in Amman, Jordan. The model was built using the MATLAB software. The model utilized a Marquardt-Levenberg learning algorithm. Its performance was presented using different indices, R2 (Coefficient of Determination), R (Coefficient of Correlation), NMSE (Normalized Mean Square Error), and Plots representing network predictions vs original data. Historical measurements of air pollutants were obtained from 4 of the Ministry of Environment (MoEnv) air quality monitoring stations in Amman. The meteorological data representing three years (2015, 2016, and 2017) were used as predictors to train the Artificial Neural Network (ANN) while the data of the year 2018 were used to test it. The results showed good performance when forecasting SO2, O3, CO, and NO2, and acceptable performance when forecasting Particulate Matter (PM10) at the given 4 locations.
Źródło:
Journal of Ecological Engineering; 2021, 22, 6; 1-11
2299-8993
Pojawia się w:
Journal of Ecological Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

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