Increasing the efficiency of a control system for detecting the type and amount of oil product passing through pipelines based on gamma-ray attenuation, time domain feature extraction, and artificial neural networks

Instantaneously determining the type and amount of oil product passing through pipelines is one of the most critical operations in the oil, polymer and petrochemical industries. In this research, a detection system is proposed in order to monitor oil pipelines. The system uses a dual-energy gamma so...

Full description

Autores:

Mayet, Abdulilah
Mehdi Alizadeh, Seyed
Azeez Kakarash, Zana
Al-Qahtani, Ali Awadh
Alanazi, Abdullah
Grimaldo Guerrero, John William
Alhashimi, Hala H.
Eftekhari-Zadeh, Ehsan

Tipo de recurso:

Article of journal

Fecha de publicación:

2022

Institución:

Corporación Universidad de la Costa

Repositorio:

REDICUC - Repositorio CUC

Idioma:

eng

OAI Identifier:

oai:repositorio.cuc.edu.co:11323/9429

Acceso en línea:

https://hdl.handle.net/11323/9429
https://doi.org/10.3390/polym14142852
https://repositorio.cuc.edu.co/

Palabra clave:

Detection system
Feature extraction
RBF neural network
Oil and polymeric fluids
Dual-energy gamma source

Rights

openAccess

License

Atribución 4.0 Internacional (CC BY 4.0)