Applying Data Mining and Artificial Intelligence Techniques for High Precision Measuring of the Two-Phase Flow’s Characteristics Independent of the Pipe’s Scale Layer

• DOI: 10.3390/electronics11030459
• Publication Date: 2022-02-04T16:35:17Z
• Authors: Abdulilah Mohamma...; Ahmed S. Salama; Seyed Mehdi Alizadeh; Slavko Nesic; John William Grim...; Ehsan Eftekhari-Z...; Ehsan Nazemi; Abdullah M. Iliyasu
• Keywords: RBF neural network; Computer. Automation; Artificial intelligence; Physics; Oil and gas; Pipeline’s scale; pipeline’s scale; RBF neural network; two-phase flow; oil and gas; artificial intelligence; 530; Two-phase flow; 620

Scale formation inside oil and gas pipelines is always one of the main threats to efficiency equipment their depreciation. In this study, an artificial intelligence method presented provide flow regime volume percentage a two-phase while considering presence scale test pipe. non-invasive method, dual-energy source barium-133 cesium-137 isotopes irradiated, photons are absorbed by detector as they pass through pipe on other side The Monte Carlo N Particle Code (MCNP) simulates structure frequency features, such amplitudes first, second, third, fourth dominant frequencies, which extracted from data recorded detector. These features use radial basis function neural network (RBFNN) inputs, where two networks also trained accurately determine correctly classify all patterns, independent thickness in advantage proposed system study compared conventional systems that it has better measuring precision well simpler (using instead two).