استفاده از الگوریتم ژنتیک برای مدل تعادل فازی سیستم های متشکل از متلیتیوفن 3 و هیدروکربن
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|8074||2013||6 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Procedia Engineering, Volume 51, 2013, Pages 380–385
Removal of sulfur from gasoline is one of the major processes in refineries. Designing of removal process requires phase equilibrium data of the sulfur with various hydrocarbon systems. 3-Methylthiphene is an organic sulfur compound found in gasoline. In the present work VLE data of 3-Methylthiophene with various hydrocarbons were modeled using genetic algorithm. VLE data were modeled using Wilson, NRTL and Margules activity models. Margules models was found to be best amongst the studied models
The automobile industry is growing rapidly and the most undesirable outcome of the same is the air pollution due to the automobiles. There are various ways to reduce the air pollution and one of them is to have minimum impurities in the fuel used in the automobile. Sulfur is considered to be the major impurities in gasoline because .Worldwide the sulfur concentration in the gasoline is targeted to be zero in coming years.  The reduction of sulfur level in gasoline requires the refinery processes to be modified, specifically desulfurization process. Accurate Vapor Liquid Equilibrium (VLE) data of sulfur compounds with hydrocarbons is required for such process for both reaction and separation processes. VLE determination must be supported with good estimation models. 3-Methylthiophen is one of the organic sulfur compounds present with the gasoline.  Experimental determination of VLE of 3-Methylthiophene is available in the literature [1-4]. Present work is aimed to model these systems with different activity coefficient models and compare them. The binary interaction parameters for the binary systems were obtained using Genetic Algorithm. Genetic algorithms (GAs) were developed by Holland and co-workers in 1960’s . GA is a method for moving from one population of "chromosomes" (e.g., strings of ones and zeros, or "bits") to a new population by using a kind of "natural selection" together with the genetics have following elements in common. populations of chromosomes selection according to fitness, crossover to produce new offspring and random mutation of new offspring GA works on the principle of biological evolution and Charles Darwin‘s Theory of Survival of the Fittest. Figure 1 provides an idea about the steps to be performed for GA.
نتیجه گیری انگلیسی
Ten systems containing 3-Methylthiphene were modeled using four activity coefficients. The modeling was done using GA. GA was implemented using MATLAB® GA tool box. The performance of GA can be improved if the initial range of the binary interaction parameter is obtained by carrying out function value test. Out of four activity coefficient models studied it was found that Margules was able to represent the systems with highest accuracy. Binary interaction parameters generated can be utilized for predicting multi component phase equilibrium of the binary systems studied here. This will help in designing various separation and storage equipment handling these mixtures.