ACO-based feature selection and neural network modeling for accurate gamma-radiation based pipeline monitoring in the oil industry.

This work presents a novel technique to improve oil pipeline monitoring capabilities, a vital activity in the oil and gas sector. Using Monte Carlo simulations, the work meticulously records data from a pipeline testing environment with various petroleum products and volume ratios. We apply the presented technique to mix four petroleum products-ethylene glycol, gasoline, crude oil, and gasoil-in different volumetric fractions to precisely determine their volume ratios.

Application of frequency characteristic extraction in increasing the accuracy of X-ray based thickness gauges used for aluminum alloys employing GMDH neural network.

Radiation-based gauges have been widely utilized in the industry as a dependable, non-destructive method of measuring metal layer thickness. It is only possible to trust the conventional radiation thickness meter when the material's composition is known in advance. Thickness measurement errors are to be anticipated in contexts like rolled metal factories, where the real component of the material could diverge greatly from the stated composition.

Intelligent Measuring of the Volume Fraction Considering Temperature Changes and Independent Pressure Variations for a Two-Phase Homogeneous Fluid Using an 8-Electrode Sensor and an ANN.

Two-phase fluids are widely utilized in some industries, such as petrochemical, oil, water, and so on. Each phase, liquid and gas, needs to be measured. The measuring of the void fraction is vital in many industries because there are many two-phase fluids with a wide variety of liquids.