Environmental Engineering
Z. Farajzadeh; M.A. Nematollahi
Abstract
BACKGROUND AND OBJECTIVES: The rank of Iran in terms of pollutant emissions, which mainly originate from the consumption of energy products, is much higher than the rank of gross domestic product, placing Iran the fourth in the production and consumption of gas and oil, among the cases with the highest ...
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BACKGROUND AND OBJECTIVES: The rank of Iran in terms of pollutant emissions, which mainly originate from the consumption of energy products, is much higher than the rank of gross domestic product, placing Iran the fourth in the production and consumption of gas and oil, among the cases with the highest emission intensity in the world. Different driving forces account for the high emission intensity. This study decomposes the changes in the aggregate emission intensity of the selected pollutants into a broader scope of driving forces including energy, urbanization, output, labor, and trade-related variables. The examined pollutants were far beyond carbon dioxide, including nitrogen oxides, sulphur dioxide, and carbon monoxide, emitted from energy product consumption. The aim of this study was to investigate the emission intensity of the selected pollutants and their components. METHODS: Decomposition analysis was done to decompose the emission intensity into a broader scope of the driving forces far beyond what examined in the literature. For this purpose, two well-known artificial neural networks, multilayer perceptron, and wavelet-based neural network were applied to forecast the emission intensity of the selected pollutants and their components.FINDINGS: The emission intensity of nitrogen oxides and sulphur dioxide illustrated a decreasing trend. In contrast, a general increasing trend with significant fluctuation was observed for carbon monoxide and carbon dioxide emission intensity. Among the components, energy structure, population-labor ratio, and trade openness showed an intensity decreasing effect, while urban per capita output, urbanization, energy intensity, and industrial output-trade ratio contributed to higher emission intensity of the pollutants. Moreover, the multilayer perceptron and wavelet-based neural networks were recommended to examine the predictability of the emission intensity and its components.CONCLUSION: It was found that intensive and extensive growth and energy structure were the most significant driving forces of the emission intensity. The forecast results indicated that the emission intensity of nitrogen oxides, sulphur dioxide, and carbon monoxide might be predicted by the applied networks with a prediction error of less than 0.2 percent. However, the prediction error for carbon dioxide emission intensity was much higher.
Environmental Engineering
D. Fadhiliani; M. Ikhwan; M. Ramli; S. Rizal; M. Syafwan
Abstract
BACKGROUND AND OBJECTIVES: The hydrodynamic uncertainty of the ocean is the reason for testing marine structures as an initial consideration. This uncertainty has an impact on the natural structure of the topography as well as marine habitats. In the hydrodynamics laboratory, ships and offshore structures ...
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BACKGROUND AND OBJECTIVES: The hydrodynamic uncertainty of the ocean is the reason for testing marine structures as an initial consideration. This uncertainty has an impact on the natural structure of the topography as well as marine habitats. In the hydrodynamics laboratory, ships and offshore structures are tested using mathematical models as input to the wave marker. For large wavenumbers, Benjamin Bona Mahony's equation has a stable direction and position in the wave tank. During their propagation, the generated waves exhibit modulation instability and phase singularity phenomena. These two factors refer to Benjamin Bona Mahony as a promising candidate for generating extreme waves in the laboratory. The aim of this research is to investigate the distribution of energy in each modulation frequency change. The Hamiltonian formula that describes the phenomenon of phase singularity is used to observe energy. This data is critical in determining the parameters used to generate extreme waves.METHODS: The envelope of the Benjamin Bona Mahony wave group can be used to study the Benjamin Bona Mahony wave. The Benjamin Bona Mahony wave group is known to evolve according to the Nonlinear Schrodinger equation. The Hamiltonian governs the dynamics of the phase amplitude and proves the Nonlinear Schrodinger equation's singularity for finite time. The Hamiltonian is derived from the appropriate Lagrangian for Nonlinear Schrodinger and then transformed into the Hamiltonian with the displaced phase-amplitude variable.FINDINGS: Potential energy is related to wave amplitude and kinetic energy is related to wave steepness in the study of surface water waves. When , the maximum wave amplitude and steepness are obtained. When , extreme waves cannot be formed due to steepness. This is due to the possibility of breaking waves into smaller waves on the shore. In terms of position, the energy curve is symmetrical.CONCLUSION: According to Hamiltonian's description of the energy distribution, the smaller the modulation frequency, the greater the potential and kinetic energy involved in wave propagation, and vice versa. While the wave's amplitude and steepness will be greatest for a low modulation frequency, and vice versa. The modulation frequency considered as an extreme wave generator is , because the resulting amplitude is quite high and the energy in the envelope is also quite large.
