Journal ArticleDOI
Developing a gamma ray fluid densitometer in petroleum products monitoring applications using Artificial Neural Network
M. Khorsandi,Seyed Amir Hossein Feghhi,Abolfazl Salehizadeh,Gholam Hossein Roshani,Gholam Hossein Roshani +4 more
TLDR
Results show that proposed ANN model represents a good estimation of the density in petroleum products monitoring application and can be used as a reliable and accurate tool.About:
This article is published in Radiation Measurements.The article was published on 2013-12-01. It has received 41 citations till now. The article focuses on the topics: Densitometer.read more
Citations
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Journal ArticleDOI
Flow regime identification and void fraction prediction in two-phase flows based on gamma ray attenuation
TL;DR: In this article, a method based on dual modality densitometry using artificial neural network (ANN) was presented to first identify the flow regime and then predict the void fraction in two-phase flows.
Journal ArticleDOI
Precise Void Fraction Measurement in Two-phase Flows Independent of the Flow Regime Using Gamma-ray Attenuation
Ehsan Nazemi,Seyed Amir Hossein Feghhi,Gholam Hossein Roshani,R. Gholipour Peyvandi,Saeed Setayeshi +4 more
TL;DR: In this paper, a multilayer perceptron neural network was used to predict void fraction in gas-eliquid two-phase flows with a mean relative error of < 1.4%.
Journal ArticleDOI
Density and velocity determination for single-phase flow based on radiotracer technique and neural networks
TL;DR: In this paper, the authors demonstrate the measurements of these parameters precisely for different fluids and various diameters of pipes by using radiotracer injection and Artificial Neural Network (ANN).
Journal ArticleDOI
Online measuring density of oil products in annular regime of gas-liquid two phase flows
TL;DR: In this article, a novel method is proposed for online measuring density of liquid phase in annular regime of liquid-gas two-phase flows using dual modality densitometry technique and artificial neural network (ANN).
Journal ArticleDOI
Density prediction for petroleum and derivatives by gamma-ray attenuation and artificial neural networks
TL;DR: The results show that the proposed approach may be successfully applied for prediction of density for these types of materials and can be automatically predicted without a prior knowledge of the actual material composition.
References
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Journal ArticleDOI
Training feedforward networks with the Marquardt algorithm
TL;DR: The Marquardt algorithm for nonlinear least squares is presented and is incorporated into the backpropagation algorithm for training feedforward neural networks and is found to be much more efficient than either of the other techniques when the network contains no more than a few hundred weights.
MCNP-A General Monte Carlo N-Particle Transport Code
TL;DR: In this article, the authors present a practical guide for the use of general-purpose Monte Carlo code MCNP, including several examples and a discussion of the particular techniques and the Monte Carlo method itself.
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Original Contribution: On learning the derivatives of an unknown mapping with multilayer feedforward networks
A. Ronald Gallant,Halbert White +1 more
TL;DR: It is shown that a net can be trained so that the map and its derivatives are learned, and least squares and similar estimates are strongly consistent in Sobolev norm provided the number of hidden units and the size of the training set increase together.
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Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir
TL;DR: Based on a real case of MPFM, a new method for oil rate prediction of wells base on Fuzzy logic, Artificial Neural Networks (ANN) and Imperialist Competitive Algorithm is presented, proving the effectiveness, robustness and compatibility of the ICA-ANN model.