R
Robert Hanus
Researcher at Rzeszów University of Technology
Publications - 100
Citations - 1324
Robert Hanus is an academic researcher from Rzeszów University of Technology. The author has contributed to research in topics: Two-phase flow & Flow measurement. The author has an hindex of 17, co-authored 97 publications receiving 806 citations.
Papers
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Journal ArticleDOI
Evaluation of flow pattern recognition and void fraction measurement in two phase flow independent of oil pipeline’s scale layer thickness
M.M. Roshani,Giang T. T. Phan,Peshawa Jammal Muhammad Ali,Gholam Hossein Roshani,Robert Hanus,Trung H. Duong,Enrico Corniani,Ehsan Nazemi,El Mostafa Kalmoun +8 more
TL;DR: In this paper, an intelligent non-destructive technique based on combination of gamma radiation attenuation and artificial intelligence is proposed to determine the type of flow pattern and gas volume percentage in two phase flow independent of petroleum pipeline's scale layer thickness.
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Combination of X-ray tube and GMDH neural network as a nondestructive and potential technique for measuring characteristics of gas-oil–water three phase flows
M.M. Roshani,Giang T. T. Phan,Gholam Hossein Roshani,Robert Hanus,Behrooz Nazemi,Enrico Corniani,Ehsan Nazemi +6 more
TL;DR: In this article, a fan-beam photon attenuation based system, including one X-ray tube and two sodium iodide crystal detectors, combined with group method of data handling (GMDH) neural network is proposed to recognize type of flow regime and predict gas-oil-water volume fractions of a three phase flow.
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Applicability of time-domain feature extraction methods and artificial intelligence in two-phase flow meters based on gamma-ray absorption technique
TL;DR: Three different types of liquid–gas two-phase flow regimes, namely annular, stratified, and homogenous were simulated in various gas volumetric percentages ranging from 5% to 90%.
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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).
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Identification of liquid-gas flow regime in a pipeline using gamma-ray absorption technique and computational intelligence methods
TL;DR: The usefulness of gamma ray absorption in combination with artificial intelligence methods for liquid-gas flow regime classification is confirmed, and all the methods give good recognition results for the types of flow examined.