Quantification of detergent-dispersant additives in gasoline by thermogravimetry

Santos, Amanda Pereira Franco dos; Silva, Kissya Kropf da; Dweck, Jo; d’Avila, Luiz Antonio

doi:10.1016/j.tca.2019.178400

Cited by 7 publications

(2 citation statements)

References 10 publications

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“…Fuel quality is a critical factor affecting vehicle emissions, with gasoline detergency serving as a pivotal environmental attribute for evaluation. Vehicle gasoline detergency refers to the performance of vehicle gasoline to reduce or prevent deposits in the engine fuel line, intake valve, and combustion chamber. , It is an important index that reflects the tendency of vehicle gasoline to generate carbon deposits during use and the potential for removing carbon deposits that have already been generated. − Adding detergent additives to gasoline is an economical and effective way to improve the detergency of gasoline and keep engines clean. ,, Previous studies have substantiated that the incorporation of appropriate detergent additives into gasoline can effectively cleanse engine carbon deposits and uphold engine performance, ultimately enhancing fuel efficiency and mitigating emissions. , …”

Section: Introductionmentioning

confidence: 99%

Improvement of a Rapid Method of Detecting Gasoline Detergency Based on the Image Recognition

Zhang,

Zhu,

Jia

et al. 2023

ACS Omega

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The detergency of motor gasoline is closely related to vehicle exhaust emissions and fuel economy. This paper proposed an improved method for the rapid detection of gasoline detergency based on the deposit images of test gasoline on aluminum plates produced by a multichannel gasoline detergency simulation test (MGST). The detection algorithm system was structured to recognize the deposit plate images by computer vision based on the convolutional neural networks (CNNs). Compared with the traditional simulation test, the improved MGST method resulted in significant reductions in fuel consumption, cost, and test time. The performance of three transfer learning models (Inception-ResNet-V2, Inception-V3, and ResNet50-V2) and a customized CNN was evaluated in the detection algorithm system, and their detection accuracies reached 94, 94, 88, and 82%. Inception-RsNet-V2 was selected due to its higher accuracy and better robustness. Based on the model interpretation, it is evident that the model undergoes feature extraction from the sediment deposits on the deposit plate. Subsequently, it employed the acquired deposit features to accurately detect gasoline samples that failed to meet detergency standards. This approach was proved to be effective in enhancing the detection process and ensuring reliable results for gasoline detergency evaluation. It is beneficial to environmental protection regulators for managing market gasoline detergency and urban mobile source pollution. In addition, a deposit plate image database should be established to further improve the detection model performance during the environmental regulation.

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Section: Introductionmentioning

confidence: 99%

Improvement of a Rapid Method of Detecting Gasoline Detergency Based on the Image Recognition

Zhang,

Zhu,

Jia

et al. 2023

ACS Omega

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show abstract

“…Vehicle gasoline detergency refers to the efficacy of fuel in preventing deposits within engine components such as the fuel line, intake valve, and combustion chamber [15]. It reflects both the tendency to form carbon deposits during usage and the potential to remove existing deposits [16]. The addition of gasoline detergent was regarded as an economical approach to enhance gasoline detergency, achieving higher fuel consumption efficiency and lower pollutant emission [17].…”

Section: Introductionmentioning

confidence: 99%

Exhaust Emissions from Gasoline Vehicles with Different Fuel Detergency and the Prediction Model Using Deep Learning

Zhang,

Chen,

Xie

et al. 2023

Sensors

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Enhancing gasoline detergency is pivotal for enhancing fuel efficiency and mitigating exhaust emissions in gasoline vehicles. This study investigated gasoline vehicle emission characteristics with different gasoline detergency, explored synergistic emission reduction potentials, and developed versatile emission prediction models. The results indicate that improved fuel detergency leads to a reduction of 5.1% in fuel consumption, along with decreases of 3.2% in total CO2, 55.4% in CO, and 15.4% in HC emissions. However, during low-speed driving, CO2 and CO emissions reductions are limited, and HC emissions worsen. A synergistic emission reduction was observed, particularly with CO exhibiting a pronounced reduction compared to HC. The developed deep-learning-based vehicle emission model for different gasoline detergency (DPVEM-DGD) enables accurate emission predictions under various fuel detergency conditions. The Pearson correlation coefficients (Pearson’s r) between predicted and measured values of CO2, CO, and HC emissions before and after adding detergency agents are 0.913 and 0.934, 0.895 and 0.915, and 0.931 and 0.969, respectively. The predictive performance improves due to reduced peak emissions resulting from improved fuel detergency. Elevated gasoline detergency not only reduces exhaust emissions but also facilitates more refined emission management to a certain extent.

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Quantum molecular modeling of oxazolidines as detergent-dispersant additives for gasoline: A valuable technological adviser

Oviedo‐Roa

Ramírez-Pérez

Servín-Nájera

et al. 2022

Fuel

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Quantification of detergent-dispersant additives in gasoline by thermogravimetry

Cited by 7 publications

References 10 publications

Improvement of a Rapid Method of Detecting Gasoline Detergency Based on the Image Recognition

Improvement of a Rapid Method of Detecting Gasoline Detergency Based on the Image Recognition

Exhaust Emissions from Gasoline Vehicles with Different Fuel Detergency and the Prediction Model Using Deep Learning

Quantum molecular modeling of oxazolidines as detergent-dispersant additives for gasoline: A valuable technological adviser

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