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All Journal Scientific Contributions Oil and Gas
Mohd. Radzi Abu Mansor
Universiti Kebangsaan Malaysia
Chemical Engineering, Chemistry & Bioengineering Energy

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Adaptive Neuro Fuzzy Inference System Mathematical Model for Detecting Gasoline Type Using Inter Digital Capacitance Sensor Galang Persada Nurani Hakim; Mohd. Radzi Abu Mansor; Diah Septiyana
Scientific Contributions Oil and Gas Vol 48 No 4 (2025)
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/scog.v48i4.1862

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.
Co-Authors Galang Persada Nurani Hakim Septiyana, Diah