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

• Autorzy: Hmadan Mohammad A. , Abdelhafez Eman , Shawabkeh Reyad

Identyfikatory

DOI

10.12912/27197050/139337

• Języki publikacji: EN

Abstrakty

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.

Słowa kluczowe

EN

desulfurized diesel; activated carbon; sulfur dioxide; air pollution

• Wydawca: Lubelski Oddział Polskiego Towarzystwo Inżynierii Ekologicznej ; Lublin University of Technology ; WNGB Wydawnictwo Naukowe
• Czasopismo: Ecological Engineering & Environmental Technology
• Rocznik: 2021
• Tom: Vol. 22, iss. 5
• Strony: 97--102
• Opis fizyczny: Bibliogr. 13 poz., rys., tab.

Twórcy

autor

Hmadan Mohammad A.

• Department of Mechanical Engineering, School of Engineering, The University of Jordan, Amman, Jordan

• https://orcid.org/0000-0002-8482-5178

autor

Abdelhafez Eman

• Al-Zaytoonah University of Jordan, Faculty of Engineering and Technology, Department of Alternative Energy Technology, Amman 11733, Jordan

• https://orcid.org/0000-0001-9480-7582

autor

Shawabkeh Reyad

• Department of Chemical Engineering, School of Engineering, The University of Jordan, Amman, Jordan

• https://orcid.org/0000-0001-7463-3415

Bibliografia

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