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To continuously monitor information about the transport of fluids by sequential batches in polyduct, found in the petrochemical industry, it is necessary to manage the mixing zone – transmix – that occurs when two fluids are being transported. This scenario demonstrates the need to estimate the interface region and the purity of the fluids in this region to improve the management of the pipeline and, thus, reduce associated costs. This study presents a measurement system based on the dual-modality gamma densitometry technique in combination with a deep neural network with seven hidden layers to predict the purity level of four different fluids (Gasoline, Glycerol, Kerosene and Oil Fuel) in the transmix. The detection geometry is composed of a137Cs radioactive source (emitting gamma rays of 661.657 keV) and two NaI(Tl) scintillator detectors to record the transmitted and scattered photons. The study was performed by computer simulations using the MCNP6 code, and the information recorded in the detectors was used as input data for training and evaluating the deep neural network. The proposed intelligent measurement system is able to predict the purity level of fluids with errors with mean squared error values below 1.4 and mean absolute percentage error values below 5.73% for all analyzed data. • A methodology to identify the interface region of petroleum by-products transportation in polyducts. • The identification of the interface region was performed for several petroleum by-products. • This study has been used the MCNP6 code and deep neural network. • The geometry consists of a gamma-ray source and uses dual-modality measurements. • The static models used for a stratified flow regime on the biphasic system were developed using the MCNP6. [ABSTRACT FROM AUTHOR]

Measuring water consumption by different users (industrial, agricultural, urban) is essential, since in many countries water royalties per consumer are differentiated by the customer's profile. Therefore, the aim of this study was to develop an alternative flow rate measurement method with low uncertainty for low sampling frequency systems using radiotracers and the Cross-Correlation function. The experimental tests were carried out on a closed water transport line built in Polyvinyl Chloride with 30 m length. The radiotracer used was Na82Br injected as a rapid pulse with volume of 3.0 ml (3 MBq) per test. Data acquisitions were made with a sampling rate ranging between 20 Hz and 1 kHz. A measurement correlator (hardware and software) was developed using special mathematical methods, such as: Savitzky-Golay filtering and Lagrange interpolation to reduce the uncertainty of the flow rate measurement. The correlator was tested in a water flow rate range of 200 L/h to 800 L/h with an uncertainty of 2.0% at a flow rate of 200 L/h. • A methodology to develop an alternative method for water flow rate measurement using low sampling frequency equipment. • Radiotracers and Cross-Correlation function with low uncertainties have been used in this study. • The Na82Br radiotracer was injected as a rapid pulse on a closed water experimental transport line built. • A measurement correlator (hardware and software) was developed using Savitzky-Golay filtering and Lagrange interpolation. • The correlator was tested in a water flow with a theoretical uncertainty of 1.0% at a flow rate of 200 L h−1. [ABSTRACT FROM AUTHOR]

The NaI(Tl) detector is one of the most used in gamma ray spectrometry because it presents high counting efficiency for gamma rays in a wide energy range. This study presents the modeling of a real 1¼ x ¾" NaI(Tl) scintillation detector + photomultiplier using computational simulations carried out by the MCNP6 code, which considers energy resolution. In the mathematical model, the sensitive volume of the detector was adjusted using 241Am and 137Cs radiation sources by means of the absolute photopeak efficiency. Moreover, the MCNP6 code was used to calculate the effective solid angle of the detector in order to obtain the intrinsic efficiency response function. The mathematical model was experimentally validated using calibrated radioactive sources (241Am, 133Ba, 137Cs and 60Co) and the response functions - efficiency curve and energy resolution - were obtained. In addition, an experimental evaluation of the crystal homogeneity of the detector was made using a137Cs radiation source and a 3D printed device developed without stepper motors. The proposed methodology is able to obtain absolute photopeak efficiency in agreement with experimental data with maximum relative error of 5.64%. The gamma scanning procedure indicated that the crystal of the detector remained homogeneous. • Modeling of a 1¼ x ¾" NaI (Tl) scintillation detector using MCNP6 code. • The model was experimentally validated using 241Am, 133Ba, 137Cs and 60Co sources. • Detector function fit parameters were experimentally obtained using radiation sources. • The effective solid angle and intrinsic efficiency were calculated using MCNP6 code. • Development of an experimental gamma scanning device without step motors. [ABSTRACT FROM AUTHOR]

In the oil industry, during the production of oil and gas, barium sulfate (BaSO 4) scale may occur on the inner walls of the pipelines leading to the reduction of the internal diameter, making the fluids' passage difficult and complicating the calculation of the fluids volume fraction. This paper presents a methodology to predict volume fraction of fluids and BaSO 4 scale thickness from obtaining spectra of two NaI(Tl) detectors that record the transmitted and scattered beams of gamma-rays. Theoretical models for a multiphase annular flow regime (gas-saltwater-oil-scale) were developed using MCNP6 code, which is a mathematical code based on the Monte Carlo method. The simulated data was used to train a deep neural network (DNN) to predict the volume fraction of gas, saltwater and oil, and the concentric scale thickness. A Python optimization library called Optuna was used for the hyperparameters search to design the DNN architecture. The methodology presented great results, especially for scale thickness prediction. Although the results for saltwater did not reach the same level, it was still possible to predict approximately 70% of the patterns up to 10% relative error. This achievement indicates the possibility to calculate the volume fraction of fluids and the concentric scale thickness in the offshore oil industry using gamma densitometry and deep learning models. • Prediction of the volume fraction of fluids and scale in an annular multiphase system. • Methodology based on transmitted and scattered beams of gamma-rays (dual-modality). • Application of deep neural network using Python hyperparameters library. • Dataset was developed using the MCNP6 code. [ABSTRACT FROM AUTHOR]