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Now showing items 1 - 16 of 190

  • Contribution of gravity and Bristow methods for Karez (aqueduct) detection

    Ebrahimi, A.   Dehghan, Mohammed Javad   Ashtari, Amir  

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  • A robust model for computing pressure drop in vertical multiphase flow

    Ebrahimi, A.   Khamehchi, E.  

    •A total number of 1740 data collected and from this data, 1650 data were selected as the valid data.•A neural network model was developed using the different methods of LM, GA, and PSO.•The trend analysis test carried out to prove the model stability and its ability to simulate the physical process of the problem.•The accuracy of the model was compared with other methods and it was proved that developed model can outperform all of them.
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  • The Use of Optimization Procedures to Estimate Minimum Miscibility Pressure

    Ebrahimi, A.   Khamehchi, E.  

    An important factor in the design of gas injection projects is the minimum miscibility pressure (MMP). A new genetic algorithm (GA)-based correlation and two neural network models (one of them is trained by back propagation [BP] algorithm and another is trained by particle swarm optimization algorithm) have been developed to estimate the CO2-oil MMP. The correlation and models use the following key input parameters: reservoir temperature, molecular weight of C+ (5), and mole percentage of the volatiles and intermediate components (for the first time, the mole percentages are used as independent variables). Then results are validated against experimental data and finally compared with commonly used correlations reported in the literature. The results show that the neural network model trained by BP algorithm and the correlation that has been developed by GA can be applied effectively and afford high accuracy and dependability for MMP forecasting.
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  • Cadmium and lead removal by new bacterial isolates from coal and aluminum mines

    Dabir, A.   Heidari, P.   Ghorbani, H.   Ebrahimi, A.  

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  • B-spline Curve Fitting by Diagonal Approximation BFGS Methods

    Ebrahimi, A.   Loghmani, G. B.  

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  • Website error analysis of colleges and universities on Long Island in New York

    Ebrahimi, A.   Schweikert, C.   Sayeed, S.   Parham, S.   Akibu, H.   Saeed, A.   Parris, W.  

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  • Empirical study of novice programming with plans and objects

    Ebrahimi, A.   Schweikert, C.  

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  • Aspects of U-Th fractionation in Tertiary limestones and calcretes of Dhofar, southern Oman

    Pracejus, Bernhard   Sundararajan, Narasimman   Al-Khirbash, Salah   Al-Hosni, Talal   Ebrahimi, A.   Al-Bulushi, Shakil   Al-Lazki, Ali   Al-Maashani, Musadem  

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  • Optimal vibration control of rotors with an open edge crack using an electromagnetic actuator

    Ebrahimi, A.   Heydari, M.   Behzad, M.  

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  • A comparative study for the source depth estimation of very low electromagnetic (VLF-EM) signal

    Ebrahimi, A.   Sundararajan, N.  

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  • Sperm Whale Algorithm: an Effective Metaheuristic Algorithm for Production Optimization Problems

    Ebrahimi, A.   Khamehchi, E.  

    Highlights • A new algorithm called SWA was introduced. The whale’s life style was used as a model in creating this algorithm. • One of the advantages of SWA over others is that it uses the worst and the best answers to reach the optimum point. • SWA was used to solve 26 benchmark functions, 3 benchmark functions in several dimensions and one production optimization SWA performance was compared with GA (Genetic Algorithm) and PSO (Particle Swarm Optimization). • Results shows that SWA needed fewer NFEs to solve most of the problems compared to GA and PSO. • It was found that the SWA could reduce the time required for finding the optimum answer by half. Abstract A new optimization algorithm called sperm whale algorithm (SWA) is proposed to solve production optimization problems. This algorithm is based on the sperm whale's lifestyle. Like other population-based algorithms, SWA uses a population of solutions to find the optimum answer. One of the advantages of this method over others is that it uses two contradictory types of answers: it uses the worst and the best answers to reach the optimum point. The SWA algorithm was tested on 26 benchmarks and three benchmarks in several dimensions and one production optimization problem. The results and comparison of its performance with other algorithms show that SWA's performance is superior to other algorithms and it could be confidently used in optimization tasks.
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  • Shape modeling based on specifying the initial B-spline curve and scaled BFGS optimization method

    Ebrahimi, A.   Barid Loghmani, G.  

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  • Developing a novel workflow for natural gas lift optimization using advanced support vector machine

    Ebrahimi, A.   Khamehchi, E.  

    Highlights • Long-term optimization of the NGL in a real field has been performed using PSO and GA. • Eclipse software was replaced by the trained SVM to decrease the optimization time. • The optimum SVM parameters were determined by the PSO algorithm (Advanced SVM). • Taguchi design was used to determine optimum GA and PSO parameters. Abstract Natural Gas Lift (NGL) is one of the most attractive methods to enhance oil recovery. In this method, oil is produced using gas from the gas region either adjacent or far from the oil layer. The reservoir simulation software should be run many times to optimize the NGL process. This is practically impossible due to time-consuming simulation of an actual reservoir. In this study, support vector machine (SVM) was used to overcome the problem. The reservoir simulation software was replaced by the trained SVM. Through this, each run only takes a few seconds. The process was optimized for a real field using particle swarm optimization (PSO) and genetic algorithm (GA). The optimum SVM parameters were determined by the PSO algorithm. Taguchi experiment design was used to determine optimum GA and PSO parameters.
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  • Monitoring and health risk assessment of phthalate esters in household’s drinking water of Isfahan, Iran

    Abdolahnejad, A.   Gheisari, L.   Karimi, M.   Norastehfar, N.   Ebrahimpour, K.   Mohammadi, A.   Ghanbari, R.   Ebrahimi, A.   Jafari, N.  

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  • Isotherm and kinetic studies for the Biosorption of Cadmium from Aqueous Solution by Alhaji Maurorum Seed

    Ebrahimi, A.   Ehteshami, M.   Dahrazma, B.  

    Cadmium is an extremely toxic metal commonly found in industrial regions. Anthropogenic activity is the most important factor causing its interference to water, soil and air resources. The aim of many researches is to present remediation strategy or to remove cadmium from contaminated resources through an economical and efficient method. Cadmium adsorption from aqueous solution using Alhaji maurorum seed adsorbent has been investigated and optimized in this study. Moreover, isotherm and kinetics of adsorption process was studied. The seeds are washed by distilled water after separation from the plant, and then dried in room temperature for 48 h. They are powdered by grinder and passed through sieve no.18 as well. Adsorption process was optimized in 4 steps regarding pH, contact time, adsorption dose and initial concentration of cadmium effects. The cadmium concentration in solution was measured using ICP-OES method. The results of optimization tests showed that the optimum condition of cadmium adsorption (85.5% removal) occurs at pH of 6.5 with 20 g/L of adsorption dose for 45 min. In addition, the efficiency of adsorption process increases as the cadmium concentration reduces in the initial solution. Adsorption process follows the pseudo second-order kinetics and Freundlich isotherm with correlation coefficients of 0.999 and 0.99, respectively. According to the findings of this analysis, it was concluded that A. maurorum seed is a good biological adsorbent for adsorbing cadmium from aqueous solution.
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  • Carbon dioxide photocatalytic reduction to methane by stabilized nano ZnS on acid-activated Montmorillonite

    Ebrahimi, A.   Sarrafi, A.   Talebizadeh, A.   Tahmooresi, M.   Hashemipour, H.  

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