University of Tabriz Journal of Agricultural Mechanization 2383-126X 10 1 2025 03 21 Investigating the Effect of Storage Time on Identification and Separation of Different Biodiesel Fuel Blends Using an Electronic Nose Investigating the Effect of Storage Time on Identification and Separation of Different Biodiesel Fuel Blends Using an Electronic Nose 27 39 19034 10.22034/jam.2025.64260.1303 FA Osman Mobaraki Mechanical of Biosystems Engineering Department, Faculty of Agriculture, Razi University, Kermanshah, Iran Mostafa Mostafaei Engineering of Biosystems Department, Agricultural Faculty, University of Tabriz, Tabriz, Iran Leila Naderloo Mechanical of Biosystems Engineering Department, Faculty of Agriculture, Razi University, Kermanshah, Iran Journal Article 2024 11 02 Introduction Energy, as one of the most critical and essential factors of production, plays a vital role in human life. With fossil fuel resources depleting, researchers are exploring alternatives such as biodiesel, a renewable biofuel with properties similar to diesel. Given the growing importance of liquid biofuels, particularly biodiesel, in global markets, it is crucial to ensure high-quality fuel production to gain consumer trust. Additionally, from a commercial perspective, considering fuel storage duration, it is necessary to determine the type of fuel and the biodiesel-to-diesel ratio using accurate, fast, and cost-effective tools. Materials and Methods This study aims to identify and differentiate various blends of biodiesel and diesel fuel (2%, 5%, 10%, and 20% by volume) derived from different vegetable oil sources (rapeseed, sunflower, and waste cooking oil) over different storage periods (immediately after production, 1 month, 2 months, and 3 months after production). Biodiesel was first produced from rapeseed oil, sunflower oil, and waste cooking oil using methanol and a potassium hydroxide (KOH) catalyst. Each biodiesel blend was mixed with diesel fuel at the specified ratios and analyzed using an electronic nose system equipped with 10 sensors. Data were collected over different periods (monthly) and analyzed using methods such as linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and support vector machine (SVM). Results and Discussion The results demonstrated the effectiveness of classification methods in separating pure fuels over four months. The accuracy rates were 97%, 100%, 82%, and 82% for SVM; 100%, 100%, 100%, and 98% for QDA; and 100%, 96%, 100%, and 100% for LDA, respectively. These methods were also capable of distinguishing pure fuels (D100, K100, WCO100, SUN100) from biodiesel-diesel blends at various storage times with high precision. Among the sensors in the system, four sensors (MQ2, TGS2620, MQ4, and TGS2602) showed the highest sensitivity to biodiesel fuels. The analysis revealed that the separation power of the models decreased during the second month of storage, with the lowest performance observed after two months. This suggests that the most significant structural and physicochemical changes in biodiesel properties occurred during this period. Furthermore, the similarity in performance parameters for fuels derived from sunflower oil and waste cooking oil indicates their shared origin. The QDA model outperformed the LDA and SVM models in separating and classifying fuel blends. Using the SVM technique, all 160 data points (40 for pure fuel and 120 for biodiesel-diesel blends) were evaluated. The SVM model achieved a specificity of over 96% for identifying and classifying pure and blended fuels immediately after production. This parameter increased to 94%, 98%, and 99% after the first, second, and third months of storage, respectively. Conclusion The study highlights the effectiveness of electronic nose systems combined with advanced classification methods for analyzing biodiesel-diesel blends. The QDA model demonstrated superior performance in fuel classification, while the SVM model also showed high accuracy in distinguishing pure and blended fuels. The findings underscore the importance of monitoring fuel quality over storage periods, as significant changes occur within the first two months. Acknowledgment This research is based on the results of a master's thesis conducted at Razi University. The authors extend their gratitude to the university officials for providing the necessary facilities and support to carry out this study. Introduction Energy, as one of the most critical and essential factors of production, plays a vital role in human life. With fossil fuel resources depleting, researchers are exploring alternatives such as biodiesel, a renewable biofuel with properties similar to diesel. Given the growing importance of liquid biofuels, particularly biodiesel, in global markets, it is crucial to ensure high-quality fuel production to gain consumer trust. Additionally, from a commercial perspective, considering fuel storage duration, it is necessary to determine the type of fuel and the biodiesel-to-diesel ratio using accurate, fast, and cost-effective tools. Materials and Methods This study aims to identify and differentiate various blends of biodiesel and diesel fuel (2%, 5%, 10%, and 20% by volume) derived from different vegetable oil sources (rapeseed, sunflower, and waste cooking oil) over different storage periods (immediately after production, 1 month, 2 months, and 3 months after production). Biodiesel was first produced from rapeseed oil, sunflower oil, and waste cooking oil using methanol and a potassium hydroxide (KOH) catalyst. Each biodiesel blend was mixed with diesel fuel at the specified ratios and analyzed using an electronic nose system equipped with 10 sensors. Data were collected over different periods (monthly) and analyzed using methods such as linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and support vector machine (SVM). Results and Discussion The results demonstrated the effectiveness of classification methods in separating pure fuels over four months. The accuracy rates were 97%, 100%, 82%, and 82% for SVM; 100%, 100%, 100%, and 98% for QDA; and 100%, 96%, 100%, and 100% for LDA, respectively. These methods were also capable of distinguishing pure fuels (D100, K100, WCO100, SUN100) from biodiesel-diesel blends at various storage times with high precision. Among the sensors in the system, four sensors (MQ2, TGS2620, MQ4, and TGS2602) showed the highest sensitivity to biodiesel fuels. The analysis revealed that the separation power of the models decreased during the second month of storage, with the lowest performance observed after two months. This suggests that the most significant structural and physicochemical changes in biodiesel properties occurred during this period. Furthermore, the similarity in performance parameters for fuels derived from sunflower oil and waste cooking oil indicates their shared origin. The QDA model outperformed the LDA and SVM models in separating and classifying fuel blends. Using the SVM technique, all 160 data points (40 for pure fuel and 120 for biodiesel-diesel blends) were evaluated. The SVM model achieved a specificity of over 96% for identifying and classifying pure and blended fuels immediately after production. This parameter increased to 94%, 98%, and 99% after the first, second, and third months of storage, respectively. Conclusion The study highlights the effectiveness of electronic nose systems combined with advanced classification methods for analyzing biodiesel-diesel blends. The QDA model demonstrated superior performance in fuel classification, while the SVM model also showed high accuracy in distinguishing pure and blended fuels. The findings underscore the importance of monitoring fuel quality over storage periods, as significant changes occur within the first two months. Acknowledgment This research is based on the results of a master's thesis conducted at Razi University. The authors extend their gratitude to the university officials for providing the necessary facilities and support to carry out this study.

https://jam.tabrizu.ac.ir/article_19034_b408758bd39bac2e633bf43f5f512b46.pdf