Improvement in measurement of radiation based two-phase flowmeters independent of flow regime and scale thickness using ant colony optimization and GMDH

The formation of scales in pipes is one element that has a major impact on the efficiency of machinery used in the oil and gas sector. With the help of artificial intelligence, this new, non-invasive device was able to figure out the volume fraction of a two-phase flow by taking into account the thi...

Full description

Autores:
Mayet, Abdulilah Mohammad
Ilyinichna, Evgeniya Gorelkina
AlShaqsi, Jamil
Parayangat, Muneer
Grimaldo Guerrero, John William
Raja, M. Ramkumar
Muqeet, Mohammed Abdul
Mohammed, Salman Arafath
Tipo de recurso:
Article of investigation
Fecha de publicación:
2024
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/14208
Acceso en línea:
https://hdl.handle.net/11323/14208
https://repositorio.cuc.edu.co/
Palabra clave:
Dual-energy gamma source
Group method of data handling
Scale thickness
Two phase-flows
Ant colony optimization
Rights
openAccess
License
Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
Description
Summary:The formation of scales in pipes is one element that has a major impact on the efficiency of machinery used in the oil and gas sector. With the help of artificial intelligence, this new, non-invasive device was able to figure out the volume fraction of a two-phase flow by taking into account the thickness of the scale in the tested pipeline. The proposed design uses an isotope pair of barium-133 and cesium-137 as a dual-energy gamma generator. One detector records photons that are transmitted, and another detector records photons that are scattered. The signals from the detectors were simulated using the Monte Carlo N-Particle (MCNP) code, and then ten frequency and wavelet characteristics were extracted. To choose the best inputs from the collected features for computing the volume fraction, an ant colony optimization (ACO)-based method is applied. Six attributes, representing the optimal combination, were developed using this method. In order to forecast the volume percentage of two-phase flows independently of flow regime and scale thickness, we fed the characteristics introduced by ACO into a group method of data handling (GMDH) neural network. Volume fraction calculations had a maximum RMSE of 0.056, which is quite little compared to previous research. By using the ACO to choose the best characteristics, the current work has significantly increased its accuracy in identifying volume fractions