{"title":"The Innovative Approach to Real-Time Detection of Fuel Types Based on Ultrasonic Sensor and Machine Learning","authors":"Mevlüt Patlak, Mehmet Çunkaş, Ugur Taskiran","doi":"10.1007/s13369-024-09092-5","DOIUrl":null,"url":null,"abstract":"<div><p>This paper presents an approach to prevent the incorrect transfer of fuel to the wrong tank during refueling. An experimental setup was developed to perform ultrasonic and dielectric measurements on diesel, gasoline, ethanol, and water. The fuel types were determined using an ultrasonic sensor and time-of-flight values were measured at various temperatures. Additionally, the dielectric coefficients of these liquids were measured to determine the liquid types using a dielectric sensor. The results obtained from both the ultrasonic and dielectric methods are systematically compared, and the advantages and disadvantages of each approach are thoroughly discussed. It was observed that dielectric method does not always yield accurate results. The proposed system effectively prevents erroneous transfer of fuel to the tank during refueling. The developed system may be used in practice to distinguish fuel types. In addition, a new approach using machine learning techniques to determine fuel type is presented. Fuel types were classified using 33 machine learning algorithms such as support vector machines, artificial neural networks and K-Nearest neighbors. It seems that artificial neural network with first layer size 25 and quadratic discriminant classifiers have achieved remarkable results with a success rate of 94% in classification. The results highlight the important and effective role of ultrasonic sensors in accurately identifying fuel types, leading to more efficient and safer storage and transportation of fuel. It is also concluded that machine learning techniques can be used effectively in identifying and classifying fuel types. The approach involving ultrasonic and artificial intelligence techniques was particularly innovative in distinguishing fuel types.</p></div>","PeriodicalId":54354,"journal":{"name":"Arabian Journal for Science and Engineering","volume":"49 12","pages":"16571 - 16591"},"PeriodicalIF":2.6000,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13369-024-09092-5.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Arabian Journal for Science and Engineering","FirstCategoryId":"103","ListUrlMain":"https://link.springer.com/article/10.1007/s13369-024-09092-5","RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
This paper presents an approach to prevent the incorrect transfer of fuel to the wrong tank during refueling. An experimental setup was developed to perform ultrasonic and dielectric measurements on diesel, gasoline, ethanol, and water. The fuel types were determined using an ultrasonic sensor and time-of-flight values were measured at various temperatures. Additionally, the dielectric coefficients of these liquids were measured to determine the liquid types using a dielectric sensor. The results obtained from both the ultrasonic and dielectric methods are systematically compared, and the advantages and disadvantages of each approach are thoroughly discussed. It was observed that dielectric method does not always yield accurate results. The proposed system effectively prevents erroneous transfer of fuel to the tank during refueling. The developed system may be used in practice to distinguish fuel types. In addition, a new approach using machine learning techniques to determine fuel type is presented. Fuel types were classified using 33 machine learning algorithms such as support vector machines, artificial neural networks and K-Nearest neighbors. It seems that artificial neural network with first layer size 25 and quadratic discriminant classifiers have achieved remarkable results with a success rate of 94% in classification. The results highlight the important and effective role of ultrasonic sensors in accurately identifying fuel types, leading to more efficient and safer storage and transportation of fuel. It is also concluded that machine learning techniques can be used effectively in identifying and classifying fuel types. The approach involving ultrasonic and artificial intelligence techniques was particularly innovative in distinguishing fuel types.
期刊介绍:
King Fahd University of Petroleum & Minerals (KFUPM) partnered with Springer to publish the Arabian Journal for Science and Engineering (AJSE).
AJSE, which has been published by KFUPM since 1975, is a recognized national, regional and international journal that provides a great opportunity for the dissemination of research advances from the Kingdom of Saudi Arabia, MENA and the world.