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BasiratPour, AmirHossein, Safieddin Ardebili, Seyed Mohamamd, Mostafaei, Mostafa, Zakidizaji, hasan. (1404). Development of an algorithm to detect ratio of the blend of biodiesel- fusel oil with diesel fuel using an e-nose system. , 1(2), 115-120. doi: 10.22069/bere.2025.24064.1029
AmirHossein BasiratPour; Seyed Mohamamd Safieddin Ardebili; Mostafa Mostafaei; hasan Zakidizaji. "Development of an algorithm to detect ratio of the blend of biodiesel- fusel oil with diesel fuel using an e-nose system". , 1, 2, 1404, 115-120. doi: 10.22069/bere.2025.24064.1029
BasiratPour, AmirHossein, Safieddin Ardebili, Seyed Mohamamd, Mostafaei, Mostafa, Zakidizaji, hasan. (1404). 'Development of an algorithm to detect ratio of the blend of biodiesel- fusel oil with diesel fuel using an e-nose system', , 1(2), pp. 115-120. doi: 10.22069/bere.2025.24064.1029
BasiratPour, AmirHossein, Safieddin Ardebili, Seyed Mohamamd, Mostafaei, Mostafa, Zakidizaji, hasan. Development of an algorithm to detect ratio of the blend of biodiesel- fusel oil with diesel fuel using an e-nose system. , 1404; 1(2): 115-120. doi: 10.22069/bere.2025.24064.1029

Development of an algorithm to detect ratio of the blend of biodiesel- fusel oil with diesel fuel using an e-nose system

Biosystems Engineering and Renewable Energies
دوره 1، شماره 2، آذر 2025، صفحه 115-120
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22069/bere.2025.24064.1029
نویسندگان
AmirHossein BasiratPour1؛ Seyed Mohamamd Safieddin Ardebili* 1؛ Mostafa Mostafaei2؛ hasan Zakidizaji3
1Shahid Chamran University of Ahvaz, Ahvaz, Iran
2Biosystems Engineering Department,Faculty of Agriculture, University of Tabriz, Tabriz, Iran
3Department of Biosystems Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
چکیده
In this study, diesel, biodiesel, and fusel oil blends were meticulously prepared in different ratios. Subsequently, an electronic nose system equipped with eight metal oxide semiconductor sensors was employed to analyze these fuel mixtures. Various analytical techniques, including linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and support vector machine (SVM) analysis, were utilized. Notably, sensors such as MQ2, MQ5, MQ138, MQ8, and MQ135 demonstrated significant sensitivity in detecting the fuel compositions. Results indicated a detection accuracy of 66.35% for the LDA method, while the QDA method achieved a remarkable 99.98% classification accuracy on the training dataset. The SVM analysis showed impressive classification and separation accuracies, with SVM1 and SVM2 methods achieving 100% and 98.65%, respectively. Evaluation based on desirability function values revealed that SVM2 outperformed other methods, scoring the highest value of 334.0. Consequently, SVM2 emerged as the optimal model for accurately classifying and separating the 15 distinct fuel combinations with exceptional precision in this study.
کلیدواژه‌ها
Diesel؛ Sensors؛ Electronic nose؛ Fusel oil؛ Support Vector Machine
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