دانلود مقاله ISI انگلیسی شماره 52452
عنوان فارسی مقاله

دو روش و دو انرژی گامای سنجش تراکم فرآورده های نفتی با استفاده از شبکه عصبی مصنوعی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
52452 2015 9 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Dual-modality and dual-energy gamma ray densitometry of petroleum products using an artificial neural network
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Radiation Measurements, Volume 82, November 2015, Pages 154–162

کلمات کلیدی
اشعه گاما - تغییرات تراکم مستقل - جریان سنج های چندگانه - جریان سه فاز
پیش نمایش مقاله
پیش نمایش مقاله دو روش و دو انرژی گامای سنجش تراکم فرآورده های نفتی با استفاده از شبکه عصبی مصنوعی

چکیده انگلیسی

The prediction of volume fractions in order to measure the multiphase flow rate is a very important issue and is the key parameter of multi-phase flow meters (MPFMs). Currently, the gamma ray attenuation technique is known as one of the most precise methods for obtaining volume fractions. The gamma ray attenuation technique is based on the mass attenuation coefficient, which is sensitive to density changes; density is sensitive in turn to temperature and pressure fluctuations. Therefore, MPFM efficiency depends strongly on environmental conditions. The conventional solution to this problem is the periodical recalibration of MPFMs, which is a demanding task. In this study, a method based on dual-modality densitometry and artificial intelligence (AI) is presented, which offers the advantage of the measurement of the oil–gas–water volume fractions independent of density changes. For this purpose, several experiments were carried out and used to validate simulated dual modality densitometry results. The reference density point was established at a temperature of 20 °C and pressure of 1 bar. To cover the full range of likely density fluctuations, four additional density sets were defined (at changes of ±4% and ±8% from the reference point). An annular regime with different percentages of oil, gas and water at different densities was simulated. Four features were extracted from the transmission and scattered detectors and were applied to the artificial neural network (ANN) as inputs. The input parameters included the 241Am full energy peak, 137Cs Compton edge, 137Cs full energy peak and total scattered count, and the outputs were the oil and air percentages. A multi-layer perceptron (MLP) neural network was used to predict the volume fraction independent of the oil and water density changes. The obtained results show that the proposed ANN model achieved good agreement with the real data, with an estimated root mean square error (RMSE) of less than 3.

خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.