Selection of effective combination of time and frequency features using PSO-based technique for monitoring oil pipelines
學年 112
學期 1
出版（發表）日期 2023-11-01
作品名稱 Selection of effective combination of time and frequency features using PSO-based technique for monitoring oil pipelines
作品名稱（其他語言）
著者 Tzu-Chia Chen; Hani Almimi; Mohammad Sh. Daoud; John William Grimaldo Guerrero; Rafał Chorzępa
單位
出版者
著錄名稱、卷期、頁數 Alexandria Engineering Journal 82, p.518-530
摘要 Pipeline installation is a time-consuming and expensive process in the oil sector. Because of this, a pipe is often utilized to carry diverse petroleum products; hence, it is crucial to use a precise and dependable control system to identify the kind and quantity of oil products being transported. This study attempts to identify four petroleum products by using an X-ray tube-based system, feature extraction in the frequency and temporal domains, and feature selection using Particle Swarm Optimization (PSO) in conjunction with a Group Method of Data Handling (GMDH) neural network. A sodium iodide detector, a test pipe that simulates petroleum compounds, and an X-ray source make up the implemented system. The detector's output signals were transmitted to the frequency domain, where the amplitudes of the top five dominant frequencies could be determined. Furthermore, the received signals were analyzed to extract five temporal characteristics-MSR, 4th order moment, skewness, WL, and kurtosis. The PSO system takes into account the extracted time and frequency features as input in order to introduce the optimal combination. Four different GMDH neural networks were constructed, and the chosen characteristics were used as inputs for those networks. Finding the volume ratio of each product was the responsibility of each neural network. The four designed neural networks were able to predict the amount of ethylene glycol, crude oil, gasoil, and gasoline with RMSE of 0.26, 0.17, 0.19, and 0.23, respectively. One compelling argument for using the proposed approach in the oil industry is that it can calculate the volume ratio of products with a root mean square error of no more than 0.26. The adoption of a feature selection method to choose the best ones is credited with this remarkable degree of precision. By providing appropriate inputs to neural networks, the control system has significantly outperformed its predecessors in terms of precision and efficiency.
關鍵字 Feature extraction;Feature selection technique;Group method of Data Handling (GMDH) neural network;Particle Swarm Optimization;X-ray tube-based system
語言 en
ISSN 2090-2670; 1110-0168
期刊性質 國外
收錄於 SCI EI Scopus
產學合作 國外
通訊作者
審稿制度 是
國別 EGY
公開徵稿
出版型式 ,電子版,紙本
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機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/125176 )