12 مهر 1401
غلامحسين روشني

غلامحسین روشنی

مرتبه علمی: دانشیار
نشانی:
تحصیلات: دکترای تخصصی / مهندسی هسته ای
تلفن:
دانشکده: دانشکده انرژی

مشخصات پژوهش

عنوان
Enhanced Gamma-Ray Attenuation-Based Detection System Using an Artificial Neural Network
نوع پژوهش مقاله چاپ شده
کلیدواژه‌ها
artificial intelligence; pipeline’s scale; two-phase flow; feature extraction; MLP; artificial neural networks
پژوهشگران احسان افتخاری زاده (نفر اول)، عبدالله بن سلما (نفر دوم)، غلامحسین روشنی (نفر سوم)، احمد سلاما (نفر چهارم)، کریستین اشپیلمان (نفر پنجم)، عبدالله الیاسو (نفر ششم به بعد)

چکیده

Scale deposition is the accumulation of various materials in the walls of transmission lines and unwanted parts in the oil and gas production system. It is a leading moot point in all transmission lines, tanks, and petroleum equipment. Scale deposition leads to drastic detrimental problems, reduced permeability, pressure and production losses, and direct financial losses due to the failure of some equipment. The accumulation of oil and gas leads to clogged pores and obstruction of fluid flow. Considering the passage of a two-phase flow, our study determines the thickness of the scale, and the flow regime is detected with the help of two Multilayer Perceptron (MLP) networks. First, the diagnostic system consisting of a dual-energy source, a steel pipe, and a NaI detector was implemented, using the Monte Carlo N Particle Code (MCNP). Subsequently, the received signals were processed, and properties were extracted using the wavelet transform technique. These features were considered as inputs of an Artificial Neural Network (ANN) model used to determine the type of flow regimes and predict the scale thickness. By accurately classifying the flow regimes and determining the scale inside the pipe, our proposed method provides a platform that could enhance many areas of the oil industry.