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Radioactive particle tracking is a nuclear technique that tracks a sealed radioactive particle inside a volume through a mathematical location algorithm, which is widely applied in many fields such as chemical and civil engineering in hydrodynamics flows. It is possible to reconstruct the trajectory of the radioactive particle using a traditional mathematical algorithm or artificial intelligence methods. In this paper, the traditional algorithm is based on solving a minimization problem between the simulated events and a calibration dataset, and it was written using C++ language. The artificial intelligence method is represented by a deep neural network, in which hyperparameters were defined using a Python optimization library called Optuna. This paper aims to compare the potentiality of both methods to evaluate the accuracy of the radioactive particle tracking technique. This study proposes a simplified model of a concrete mixer, six NaI(Tl) detectors, and a137Cs sealed radioactive particle. The simulated measurement geometry and the dataset (3615 patterns) were developed using the MCNPX code, which is a mathematical code based on the Monte Carlo Method. The results show a mean absolute percentage error (MAPE) of 20.81%, 10.33%, and 16.84% for x, y and z coordinates, respectively, for the traditional algorithm. For the deep neural network, MAPE is 6.87%, 2.70%, and 22.79% respectively for x, y and z coordinates. In addition, an investigation is carried out to analyze whether the size of the calibration dataset influences the performance of both methods. • Comparison of location algorithms applied in a radioactive particle tracking system. • RPT system is a simplified concrete mixer, six NaI(Tl) detectors and 137Cs source. • Traditional algorithm was written in C++ and is based on solving a minimization problem. • Deep learning model is represented by a deep neural network developed in Python. • Dataset with 3615 patterns was developed using the MCNPX code. [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]

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]

Scale formation is one of the major problems in the oil industry as it can accumulate on the surface of the pipelines, which could even fully block the fluids' passage. It was developed a methodology to detect and quantify the maximum thickness of eccentric scale inside pipelines using nuclear techniques and an artificial neural network. The measurement procedure is based on gamma-ray scattering using NaI(Tl) detectors and a137Cs radiation source that emits gamma-rays of 662 keV. The simulations considered an annular flow regime composed of barium sulfate scale, oil, saltwater and gas, and three percentages of these fluids were used. In the present investigation, a study of detectors configuration was carried out to improve the measurement geometry and the simulations were made using the MCNP6 code, which is a mathematical code based on the Monte Carlo method. The counts registered in the detectors were used as input data to train a deep neural network (DNN) that uses rectifier activation functions instead of the usually sigmoid-based ones. In addition, a hyperparameters search was made using open software to develop the final DNN architecture. Results showed that the best detector configuration was able to predict 100% of the patterns with the maximum relative error of 5%. Moreover, the achieved mean absolute percentage error was 0.42% and the regression coefficient was 0.99996 for all data. The results are promising and encourage the use of DNN to calculate inorganic scale regardless of the fluids volume fraction inside pipelines. • Prediction of the maximum eccentric scale thickness using gamma-ray scattering. • Application of deep neural network using hyperparameters search software. • The prediction is made regardless of the fluids volume fraction in the multiphase system. • Measurement geometry is composed of two detectors and a137Cs radiation source. • Patterns of the eccentric scale thickness were simulated using the MCNP6 code. [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]

A multiphase flow is defined as the transport of two or more fluids with different properties flowing together inside a pipeline. After offshore oil production, it is necessary to control the amount of transported fluids based on flow rate measurements. Therefore, in this study, we developed a simulation method for predicting the volume fraction and calculating the superficial velocity for a two-phase flow based on radioactive particle tracking, which involves using a sealed radiation source inside the pipeline in order to obtain volume fraction measurements. The test section for the multiphase flow comprised oil and saltwater under a stratified flow regime, with a polyvinyl chloride pipe, four NaI(Tl) detectors, and a137Cs radioactive particle that emitted gamma-rays at 662 keV. Simulations were conducted using the MCNP6 code, which is a mathematical code based on the Monte Carlo method. Volume fraction predictions were obtained using a multilayer perceptron neural network with a backpropagation algorithm. The novel feature of this method is the combination of radioactive particle tracking with an artificial neural network in order to predict volume fractions in multiphase flows. The results showed that 91.65% of the predicted patterns were within 5% of the relative error. In addition, the time delay was determined using the cross-correlation function to obtain the superficial velocity in three different volume fractions, which allowed each phase flow rate to be calculated in these cases. • Application of radioactive particle tracking to multiphase flows. • Volume fraction predicted using an artificial neural network. • Flow velocity calculated using a cross-correlation function. • Flow rate calculated in a stratified flow regime. [ABSTRACT FROM AUTHOR]

The methodology presented in this study is based on a 149.5 keV X-ray beam and two planar germanium detectors for X-ray transmission and scattering measurements for prediction of volume fractions in a three-phase system. Fluid volume fractions have been modeled using the MCNP6 code for an annular flow regime. A mathematical algorithm based on an artificial neural network was used to correlate the energy spectra from both detectors with the fluids volume fractions. The pulse height distributions obtained by the detectors are used as input data of the network that outputs the volume fractions of gas and water. The mean relative error, using the procedure presented here, for all data, was below 2.5% for both phases investigated. These results show that the methodology based on an X-ray beam has the potential to be used with flow meters. • An artificial neural network was used to predict volume fraction of gas, water, and oil. • X-ray beam and two planar germanium detectors for X-ray transmission and scattering measurements for prediction of volume fractions. • Fluid volume fractions have been modeled using the MCNP6 code for an annular flow regime. • The spectra obtained by the detectors are used as input data of the network that outputs gas and water volume fractions. [ABSTRACT FROM AUTHOR]

A recent investigation proposed a simulated radioactive particle tracking (RPT) system using eight scintillator detectors in order to predict instantaneous positions of a radioactive particle inside a concrete mixer using an artificial neural network as a location algorithm. In the context of RPT, the aim of the present study is to propose an optimization in the number of detectors in a single-phase flow RPT system. The new detection geometry consists of an array of six NaI(Tl) detectors, a 137Cs point source with isotropic emission of gamma-rays (radioactive particle) and a polyvinyl chloride mixer filled with concrete made with Portland cement as a homogenous flow regime. Another feature of this study is the use of MCNP6 code, which is based on Monte Carlo Method. In addition, three feed-forward multilayer perceptron networks with different configuration are tested as a location algorithm. All three networks showed good statistical results and the root mean square error is 1.18 in the worst scenario. The results also showed an agreement with previous study, which indicates that this methodology reducing two detectors works satisfactorily and maintain a good accuracy in position prediction. • Optimization of the detection system reducing two detectors. • Radioactive Particle Tracking methodology developed using MCNP6 code. • An artificial neural network predicts the position of the radioactive particle. • System based on RPT to evaluate industrial agitators in a homogeneous flow regime. [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 proposed geometry composes gamma-rays and two NaI(Tl) detectors. • Different thicknesses and relative positions of scale for the annular flow regime were modeled using the MCNP code. • A Backpropagation 5-layer perceptron network was used for scale prediction. • The gamma-ray scattering was used to quantify the maximum thickness of eccentric scales (BaSO 4). • The maximum scale was predicted independently of its position inside the tube and the presence of the fluids. This study investigates a methodology to study the deposition of barium sulfate scales (BaSO 4) commonly found in the oil industry; it causes an internal diameter decrease, making it difficult for the flow. A measurement procedure was elaborated on gamma-ray scattering with three NaI(Tl) detectors and a 137Cs gamma-ray source to detect and quantify the maximum thickness of eccentric scale. The detectors data were used to train the artificial neural network for the prediction of the maximum scale thickness values regardless of oil, saltwater, gas and scale inside the tube. A data subset for training and evaluation of the artificial neural network generalization capability was generated using the MCNP6 code. Different thicknesses and positions of the maximum scale value were considered. The results show that more than 90% of the patterns presented relative errors lower than ±10%. [ABSTRACT FROM AUTHOR]