Estimation of petroleum contents in bituminous soil using compact submersible radio frequency sensor based on artificial neural network. Issue 12 (30th July 2021)

• Record Type: Journal Article
• Authors: Verma, Aman; Jain, Surabhi; Tiwari, Nilesh Kumar; Akhtar, M. Jaleel
• Is Part Of: Microwave and optical technology letters. Volume 63:Issue 12(2021)
• Page Start: 2951
• Page End: 2957
• Publication Date: 2021-07-30
• Subjects: CSRR -- detection -- neural network -- permittivity measurement -- petroleum -- soil pH -- submersible sensor; Microwaves -- Periodicals; Optics -- Periodicals; 621
• DOI: 10.1002/mop.32999
• Languages: English
• ISSNs: 0895-2477

Abstract

Abstract: This paper presents a novel miniaturized noninvasive submersible compact planar sensor to estimate petroleum contents for various industrial and remote sensing applications. The proposed sensor is based on a microstrip technique wherein a complementary split‐ring resonator is used to sense samples under interrogation. The proposed compact sensor works at a resonating frequency of 8.87 GHz with quite high sensitivity. The designed sensor can accurately detect the shift in resonant frequency for corresponding change in soil samples and different level of petroleum contents. Furthermore, the sensor gives accurate permittivity extraction of various soil samples sample using an innovative automated neural network‐based algorithm. The experimental results show that the proposed sensor can detect materials contents with good repeatability, which can be used to detect the presence of petroleum constituents in transportation pipelines and mining exploration.