Forecasting future oil demand in Iran using GSA (Gravitational Search Algorithm)

Growing energy demand of the world, made the major oil and gas exporting countries to have critical role in the energy supply. The geostrategic situation of Iran and its access to the huge hydrocarbon resources placed the country among important areas and resulted in the investment development of oil and gas industry.In this study, a novel approach for oil consumption modeling is presented. Three demand estimation models are developed to forecast oil consumption based on socio-economic indicators using GSA (Gravitational Search Algorithm). In first model (PGIE) oil consumption is estimated based on population, GDP, import and export. In second model (PGML) population, GDP, export minus import, and number of LDVs (light-duty vehicles) are used to forecast oil consumption and in third one (PGMH) population, GDP, export minus import, and number of HDVs (heavy-duty vehicles) are used to estimate oil consumption. Linear and non-linear forms of equations are developed for each model.In order to show the accuracy of the algorithm, a comparison is made with the GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) estimation models which are developed for the same problem. Oil demand in Iran is forecasted up to year 2030.     

Experimental and theoretical investigation of dual purpose solar collector

Experimental data indicate that high temperature and high performance can be obtained using dual purpose solar collector (DPSC) compared to single water or air collector. A mathematical model based on effectiveness method has been developed for the investigation of thermal performance of DPSC. In the collector two fluids (water and air) flow simultaneously. Three different kinds of channels are used to enhance the performance of collector, such as: rectangular fin, triangular fin and without fin. Simulation results show that channels with rectangular fin have better performance compared with others. The effect of water inlet temperature and air flow rate on heat delivery by air and water has been investigated.     

Application of thin plate splines for solving a class of boundary integral equations arisen from Laplace's equations with nonlinear boundary conditions

This article describes a technique for numerically solving a class of nonlinear boundary integral equations of the second kind with logarithmic singular kernels. These types of integral equations occur as a reformulation of boundary value problems of Laplace's equations with nonlinear Robin boundary conditions. The method uses thin plate splines (TPSs) constructed on scattered points as a basis in the discrete collocation method. The TPSs can be seen as a type of the free shape parameter radial basis functions which establish effective and stable methods to estimate an unknown function. The proposed scheme utilizes a special accurate quadrature formula based on the non-uniform Gauss–Legendre integration rule for approximating logarithm-like singular integrals appeared in the approach. The numerical method developed in the current paper does not require any mesh generations, so it is meshless and independent of the geometry of the domain. The algorithm of the presented scheme is accurate and easy to implement on computers. The error analysis of the method is provided. The convergence validity of the new technique is examined over several boundary integral equations and obtained results confirm the theoretical error estimates.     

Combined Use of Geostatistics and Multi-Criteria Decision Analysis to Determine New Pumping Well Locations in the Gol-Gohar Open Pit Mine,Iran

Groundwater seepage into open pit mines must be controlled carefully. Slope instability, dewatering of blast holes, and mining operations below the groundwater table are important issues caused by groundwater seepage into the Gol-Gohar open pit mine, Iran. There are several methods to overcome these problems, such as construction of cut-off walls, ditches and sumps, horizontal drains, and pumping wells. Drilling of a new pumping well has several difficulties in which the determination of its location is a major issue. In this study, a stochastic simulation approach called simulated annealing was used to determine the best possible locations for new pumping wells. Three major groundwater variables, including the groundwater level, electrical conductivity, and transmissivity were selected for the geostatistical study. The results of simulations showed reliable correlation (Pearson) between the variables. Comparison of the variograms at different depths of the Gol-Gohar pit mine revealed that the effect of faults intensified with increasing depth. The best potential locations for drilling of new pumping wells were identified by the use of multi-criteria decision analysis performed on the simulation results. This method can be used in other regions with similar hydrogeological settings.     

ASSESSMENT OF ELECTRICITY DEMAND IN IRAN'S INDUSTRIAL SECTOR USING DIFFERENT INTELLIGENT OPTIMIZATION TECHNIQUES

This study presents application of particle swarm optimization (PSO) and genetic algorithm (GA) methods to estimate electricity demand in Iran's industrial sectors, based on economic indicators. The economic indicators used in this study are number of customers, gross domestic product (GDP), electricity production and price. The models developed in two forms (exponential and linear). Electricity consumption in Iran's industrial sector from 1981 to 2005 is considered as the case of this study. The available data is partly used for finding the optimal, or near optimal, values of the weighting parameters (1981–1999) and partly for testing the models (2000–2005). For the best results of GA, the relative error averages were 1.13% and 1.29% for GA ? DE M _exponential and GA ? DE M _linear and for PSO were 1.03% and 1.69% for PSO ? DE M _exponential and PSO ? DEM_linear. Electricity consumption is forecasted up to year 2030.     

Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran

This paper presents application of PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) techniques to estimate oil demand in Iran, based on socio-economic indicators. The models are developed in two forms (exponential and linear) and applied to forecast oil demand in Iran. PSO–DEM and GA–DEM (PSO and GA demand estimation models) are developed to estimate the future oil demand values based on population, GDP (gross domestic product), import and export data. Oil consumption in Iran from 1981 to 2005 is considered as the case of this study. The available data is partly used for finding the optimal, or near optimal values of the weighting parameters (1981–1999) and partly for testing the models (2000–2005). For the best results of GA, the average relative errors on testing data were 2.83% and 1.72% for GA–DEM_exponential and GA–DEM_linear, respectively. The corresponding values for PSO were 1.40% and 1.36% for PSO–DEM_exponential and PSO–DEM_linear, respectively. Oil demand in Iran is forecasted up to year 2030.     

Groundwater Circulation and Origin of Salinity in a Multi-aquifer System: The Gohar-Zamin Mining Area,Iran

Mine Water and the Environment - Determining the origin of groundwater in active and unstable mining environments has proven quite challenging. We evaluated the origin and salinity of the...