توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقاله‌های نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده می‌شوند.
۱Characterization and modeling of a crude oil desalting\dehydration plant by experimental design approach
نویسنده(ها): ، ، ،
اطلاعات انتشار: یازدهمین کنگره ملی مهندسی شیمی ایران، سال
تعداد صفحات: ۱۱
Oil produced in most of oil fields is accompanied by water and dissolved salts, mainly NaCl, which can cause considerable operational problem. Therefore desalting and dehydration plants are often installed in crude oil production units to remove water soluble salts from an oil stream. This paper investigates experimentally effect of five parameters (demulsifying agent concentration, temperature, wash water dilution ratio, settling time and mixing time with wash water) on performance of desalting\dehydration process. The performance was evaluated by calculating the Salt Removal Efficiency (SRE) and Water Removal Efficiency (WRE) based on the five process parameters. In order to investigate effect of the parameters on desalting\dehydration efficiencies a 26–1 fractional factorial design with 5 other experiment at the center of the design for analysis of variance was applied. Based on the statistical analysis SRE was expressed by a model for the whole range of variables while WRE was expressed with two models, each is valid in a part of variable domains. The models were satisfactory evaluated with plant experimental data.<\div>

۲A neural network approach for identification and modeling of delayed cocking plant
نویسنده(ها): ، ،
اطلاعات انتشار: پنجمین کنگره بین المللی مهندسی شیمی، سال
تعداد صفحات: ۸
In this study, an Artificial Neural Network (ANN) modeling of a Delayed Cocking Unit (DCU) is proposed. Different data from various DCU have been collected. Feed API and Cat Cracker (CCR) weight percent have been considered as network inputs. Coke, output CCR, light
gases, gasoline, gas–oil and C5 + weight percents are the network outputs. 70 percent of the data have been used for training of ANN. Among the Multi Layer Perceptron (MLP) architectures a network with 31 hidden neurons has been found as best MLP predictor. Radial Basis Function
(RBF) also has been implemented for identification of the plant. An RBF network with 20 spread was found as best estimator of the DCU. Best RBF network and best MLP network performance in prediction of 30 percent of unseen data were compared. It was found that RBF method has the best generalization capability and was used in DCU modeling.<\div>

۳Optimizing a typical crude oil desalting plant by experimental dsign approach
نویسنده(ها): ، ، ،
اطلاعات انتشار: پنجمین کنگره بین المللی مهندسی شیمی، سال
تعداد صفحات: ۸
This paper investigates experimentally the effect of five parameters (demulsifying agent concentration, temperature, wash water dilution ratio, settling time and mixing time with wash water) on performance of the desalting\dehydration process. The performance was evaluated by calculating the Salt Removal Efficiency (SRE) and the Water Removal Efficiency (WRE) based on the five process parameters. In order to investigate the effect of these parameters on desalting\dehydration efficiencies a 26–1 fractional factorial design with five other experiments at
the center of the design for analysis of variance was applied. Based on the statistical analysis, SRE was expressed by a model for the whole range of variables while WRE was expressed with two models, each is valid in a part of variable domains. The models were satisfactorily evaluated with plant experimental data. The optimum values of demulsifying agent concentration, temperature, wash water dilution ratio, settling time and mixing time with wash water were fond to be 15 ppm, 77 oC, 10%, 3 min and 9 min respectively for SRE. For WRE based on two proposed models optimum values were found. 93.28% removal efficiency was found for salt removal. This value was 94.80% and 89.57% for water removal proposed models.<\div>
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