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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]

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]

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]