Optimization of radioactive sources to achieve the highest precision in three-phase flow meters using Jaya algorithm

Enhanced Gamma-Ray Attenuation-Based Detection System Using an Artificial Neural Network

Scale deposition is the accumulation of various materials in the walls of transmission lines and unwanted parts in the oil and gas production system. It is a leading moot point in all transmission lines, tanks, and petroleum equipment. Scale deposition leads to drastic detrimental problems, reduced permeability, pressure and production losses, and direct financial losses due to the failure of some equipment. The accumulation of oil and gas leads to clogged pores and obstruction of fluid flow. Considering the passage of a two-phase flow, our study determines the thickness of the scale, and the flow regime is detected with the help of two Multilayer Perceptron (MLP) networks. First, the diagnostic system consisting of a dual-energy source, a steel pipe, and a NaI detector was implemented, using the Monte Carlo N Particle Code (MCNP). Subsequently, the received signals were processed, and properties were extracted using the wavelet transform technique. These features were considered as inputs of an Artificial Neural Network (ANN) model used to determine the type of flow regimes and predict the scale thickness. By accurately classifying the flow regimes and determining the scale inside the pipe, our proposed method provides a platform that could enhance many areas of the oil industry.

Extraction of Time-Domain Characteristics and Selection of Effective Features Using Correlation Analysis to Increase the Accuracy of Petroleum Fluid Monitoring Systems

In the current paper, a novel technique is represented to control the liquid petrochemical and petroleum products passing through a transmitting pipe. A simulation setup, including an X-ray tube, a detector, and a pipe, was conducted by Monte Carlo N Particle-X version (MCNPX) code to examine a two-by-two mixture of four diverse petroleum products (ethylene glycol, crude oil, gasoline, and gasoil) in various volumetric ratios. As the feature extraction system, twelve time characteristics were extracted from the received signal, and the most effective ones were selected using correlation analysis to present reasonable inputs for neural network training. Three Multilayers perceptron (MLP) neural networks were applied to indicate the volume ratio of three kinds of petroleum products, and the volume ratio of the fourth product can be feasibly achieved through the results of the three aforementioned networks. In this study, increasing accuracy was placed on the agenda, and an RMSE < 1.21 indicates this high accuracy. Increasing the accuracy of predicting volume ratio, which is due to the use of appropriate characteristics as the neural network input, is the most important innovation in this study, which is why the proposed system can be used as an efficient method in the oil industry.

An Intensive and Comprehensive Overview of JAYA Algorithm, its Versions and Applications

In this review paper, JAYA algorithm, which is a recent population-based algorithm is intensively overviewed. The JAYA algorithm combines the survival of the fittest principle from evolutionary algorithms as well as the global optimal solution attractions of Swarm Intelligence methods. Initially, the optimization model and convergence characteristics of JAYA algorithm are carefully analyzed. Thereafter, the proposed versions of JAYA algorithm have been surveyed such as modified, binary, hybridized, parallel, chaotic, multi-objective and others. The various applications tackled using relevant versions of JAYA algorithm are also discussed and summarized based on several problem domains. Furthermore, the open sources code of JAYA algorithm are identified to provide enrich resources for JAYA research communities. The critical analysis of JAYA algorithm reveals its advantages and limitations in dealing with optimization problems. Finally, the paper ends up with conclusion and possible future enhancements suggested to improve the performance of JAYA algorithm. The reader of this overview will determine the best domains and applications used by JAYA algorithm and can justify their JAYA-related contributions.

X-ray tube with artificial neural network model as a promising alternative for radioisotope source in radiation based two phase flowmeters

In this paper, X-ray tube is introduced as a potential alternative for radioisotope sources used in radiation based liquid-gas two-phase flowmeters. X-ray tubes have lots of advantages over the radioisotope sources such as having an adjustable emitting photon's energy, being safer from point of view of radiation health physics during the transportation of the source, having ability to generate a high flux photon beam, and etc. The proposed radiation based system in this study composes an X-ray tube with a tube voltage of 150 kV and a 2.5 mm aluminum filter as the radiation source and one sodium iodide crystal as the photon detector. A pipe was positioned between the X-ray tube and the detector. Two main flow regimes of annular and stratified with different void fractions were modelled inside the pipe. Artificial neural network model of multi-layer perceptron (MLP) was also used in this study for analyzing the obtained data. The output spectrum of sodium iodide detector with 150 samples was applied as the input of multi-layer perceptron network and void fraction was considered as its output. The root mean squared error of proposed measuring system was 4.13 which shows the X-ray tube can be implemented as a promising alternative for radioisotope in radiation based two phase flow meters.

Proposing an intelligent approach for measuring the thickness of metal sheets independent of alloy type

Radiation based gauges have been widely utilized as a nondestructive and robust tool for measuring the thickness of metal sheets in industry. The typical radiation thickness meter can just work accurately when the composition of the material is fixed during the measurement process. In conditions that material composition may differ substantially from the nominal composition, such as manufacturing rolled metals factories, the thickness measurements would be along with errors. The purpose of the present research is resolving the problem of measuring the thickness of metal sheets with various alloys. The aluminum is investigated in this work as a case study but the procedure can be applied for other types of metals. As the first step, the performance of various arrangements of two main detection techniques, named dual energy and dual modality, were investigated using MCNPX code to obtain optimum technique and arrangement. The simulation results indicated that a binary combination of ²⁴¹Am-⁶⁰Co isotopes as the source and one transmission detector in dual energy technique is the most appropriate choice. After then, an experimental setup based on the obtained optimal technique from simulation investigations was established. The aluminum sheets with 4 alloy types of 1050, 3105, 5052 and 6061 and thicknesses in the range of 0.2-4 cm with a step of 0.2 cm were tested and the obtained data were implemented for testing and training the artificial neural network (ANN). The proposed methodology could predict the thickness of aluminum sheet independent of its alloy type with an error of less than 0.04 cm in experiments.