Use of gamma radiation and artificial neural network techniques to monitor characteristics of polyduct transport of petroleum by-products

Use of gamma radiation and artificial neural network techniques to monitor characteristics of polyduct transport of petroleum by-products

Gamma rays

Pipelines

Gasoline

Neural networks

Petroleum transportation

Transport properties

Intelligent systems

Network coding

Sodium Iodide

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. 2022 Elsevier Ltd

186

Applied Radiation and Isotopes

2022

Salgado, W.L.

Dam, R.S.F.

Puertas, E.J.A.

Salgado, C.M.

Silva, A.X.

journalArticle

9698043

10.1016/j.apradiso.2022.110267