Abstract
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.
Highlights
Via an artificial neural network, the scholars could predict the density of petroleum products with high accuracy independent of fluid admixture
The monitoring system consists of an X-ray tube, a NaI detector, and a pipe that has been simulated by Monte Carlo N Particle-X version (MCNPX) code
By simulating the combination of four petroleum products in diverse volume ratios and data collection registered by the detector, the time domain feature extraction method was utilized to derive the data characteristics, and a correlation analysis was applied to determine efficient ones
Summary
Poly-pipelines are mostly applied in the petrochemical industry for oil transmission or its derivatives to distribution centers. Using a pipeline to transport diverse petroleum fluids is highly economic; some problems such as mixing various petroleum fluids indicate the significance of extending a sustainable, non-invasive technique to control and detect the interference region. Due to the mentioned issue, a number of verifications have been conducted, which are concisely demonstrated. Salgado et al established a petrochemical product density detection system, including a CS-137 source and a NaI detector [1]. The MCNPX code was utilized in such an examination. Via an artificial neural network, the scholars could predict the density of petroleum products with high accuracy independent of fluid admixture. For Monte Carlo code validation, they staged a laboratory structure using a cesium source, a glass pipe, and a sodium iodide detector
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