Scientific Contributions Oil and Gas
Vol 48 No 4 (2025)

Adaptive Neuro Fuzzy Inference System Mathematical Model for Detecting Gasoline Type Using Inter Digital Capacitance Sensor

Galang Persada Nurani Hakim (Universitas Mercu Buana)
Mohd. Radzi Abu Mansor (Universiti Kebangsaan Malaysia)
Diah Septiyana (Universitas Muhammadiyah Tangerang)



Article Info

Publish Date
16 Dec 2025

Abstract

In the context of global warming, governments worldwide are striving to control emissions from combustion engines by promoting higher RON gasoline types. However, the higher cost of these fuels has led to a decrease in their usage. Detecting the type of gasoline in a vehicle is a complex and inefficient process. Therefore, this research presents a mathematical model for identifying gasoline type and its components using an Inter Digital Capacitor (IDC) sensor, a small and cost-effective sensor. The model aims to establish a relationship between gasoline type and the components, as well as identify gasoline components in the electrical characteristics. The model has achieved high accuracy, with a small error of 4.03 × 10^-5, demonstrating its effectiveness in building these relations. The conclusion of this study is that mathematical modeling with ANFIS can be used to explain the relationship between the components that make up gasoline and the capacitance value of the IDC sensor used to measure it.

Copyrights © 2025






Journal Info

Abbrev

SCOG

Publisher

Subject

Chemical Engineering, Chemistry & Bioengineering Energy

Description

The Scientific Contributions for Oil and Gas is the official journal of the Testing Center for Oil and Gas LEMIGAS for the dissemination of information on research activities, technology engineering development and laboratory testing in the oil and gas field. Manuscripts in English are accepted from ...