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

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.

Employing GMDH-Type Neural Network and Signal Frequency Feature Extraction Approaches for Detection of Scale Thickness inside Oil Pipelines

“…In study [28], using correlation analysis, the characteristics that have the least similarity with other characteristics were selected as effective characteristics. In this study, the frequency domain property is extracted, following which the received signal can be converted to the frequency range using Fast Fourier Transform (FFT) using Equation (1) [29]. Further, the amplitudes of the first to the fourth dominant frequency of the signals, which are characteristics necessary for neural network training, are extracted.…”

Accurate Flow Regime Classification and Void Fraction Measurement in Two-Phase Flowmeters Using Frequency-Domain Feature Extraction and Neural Networks

“…Multilayer perceptron (MLP) is a common type of neural network [44,45]. ANN is a suitable technique which is applied for handling the models and classification, as well as prediction [46][47][48][49][50][51][52][53][54][55][56][57][58][59].…”

Повышение Эффективности Систем Улавливания Паров Нефти При Товарно-Транспортных Операциях На Нефтеналивных Терминалах

“…The principal diagram of vapor release during cargo loading provides a line: "tanker gas systemgas phase pipelinevapor recovery unitgas vent" (Fig. 2) [29,30].…”