Use of gamma radiation and artificial neural network techniques to monitor characteristics of polyduct transport of petroleum by-products.
Appl Radiat Isot
; 186: 110267, 2022 Aug.
Article
en En
| MEDLINE
| ID: mdl-35561550
ABSTRACT
This study presents a methodology based on the dual-mode gamma densitometry technique in combination with artificial neural networks to simultaneously determine type and quantity of four different fluids (Gasoline, Glycerol, Kerosene and Fuel Oil) to assist operators of a fluid transport system in pipelines commonly found in the petrochemical industry, as it is necessary to continuously monitor information about the fluids being transferred. The detection system is composed of a 661.657 keV (137Cs) gamma-ray emitting source and two NaI(Tl) scintillation detectors to record transmitted and scattered photons. The information recorded in both detectors was directly applied as input data for the artificial neural networks. The proposed intelligent system consists of three artificial neural networks capable of predicting the fluid volume percentages (purity level) with 94.6% of all data with errors less than 5% and MRE of 1.12%, as well as identifying the pair of fluids moving in the pipeline with 95.9% accuracy.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Petróleo
/
Redes Neurales de la Computación
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
Appl Radiat Isot
Asunto de la revista:
MEDICINA NUCLEAR
/
SAUDE AMBIENTAL
Año:
2022
Tipo del documento:
Article