Prediction of Sooting Index of Fuel Compounds for Spark-Ignition Engine Applications Based on a Machine Learning Approach

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  • 1. School of Automotive Studies, Tongji University, Shanghai 201804, China
    2. Institute for Combustion Technology, RWTH Aachen University, Aachen 52056, German

网络出版日期: 2023-11-28

基金资助

This work was supported by the Fundamental Research Funds for the Central Universities. The work of Florian vom Lehn and Heinz Pitsch was performed as part of the Cluster of Excellence “The Fuel Science Center”, which is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy - Cluster of Excellence 2186 “The Fuel Science Center” ID: 390919832.

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Science Press, Institute of Engineering Thermophysics, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2023

Prediction of Sooting Index of Fuel Compounds for Spark-Ignition Engine Applications Based on a Machine Learning Approach

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  • 1. School of Automotive Studies, Tongji University, Shanghai 201804, China
    2. Institute for Combustion Technology, RWTH Aachen University, Aachen 52056, German

Online published: 2023-11-28

Supported by

This work was supported by the Fundamental Research Funds for the Central Universities. The work of Florian vom Lehn and Heinz Pitsch was performed as part of the Cluster of Excellence “The Fuel Science Center”, which is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy - Cluster of Excellence 2186 “The Fuel Science Center” ID: 390919832.

Copyright

Science Press, Institute of Engineering Thermophysics, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2023

摘要

在设计汽油替代物和添加剂时需要综合考虑其潜在的燃烧和排放性能,同时需要对燃料特性与结构的关系有深入的了解。本研究的目标是通过预测燃料分子的碳烟指数(YSI)来评估其在发动机应用中的排放特性。借助之前建立的燃料化合物数据库,其中包括了600多种化合物的物理和化学特性,例如辛烷值、层流燃烧速度和蒸汽化热,开发了一种基于人工神经网络技术的定量结构-性质关系(QSPR)模型,利用21个经过筛选的功能基描述符作为输入特征,预测燃料物种的YSI。该模型使用耶鲁大学提供的YSI数据库进行训练和交叉验证,并应用于探索燃料的成烟倾向性对分子基团的敏感性。此外,还研究了YSI与汽油燃料数据库中其他可用特性之间的相关性,以了解这些特性的依赖关系。最后,研究者通过考虑燃料的潜在热机效率和油烟形成特性,分别采用效率增益函数和YSI作为综合考虑因素,对潜在的汽油混合组分进行了选择。

本文引用格式

CHEN Zhuo, VOM LEHN Florian, PITSCH Heinz, CAI Liming . Prediction of Sooting Index of Fuel Compounds for Spark-Ignition Engine Applications Based on a Machine Learning Approach[J]. 热科学学报, 2023 , 32(2) : 521 -530 . DOI: 10.1007/s11630-023-1765-3

Abstract

A joint consideration of potential combustion and emission performance in spark-ignition engines is essential for designing gasoline fuel replacements and additives, for which the knowledge of the fuels’ characteristic properties forms the backbone. The aim of this study is to predict sooting tendency of fuel molecules for spark-ignition engine applications in terms of their yield sooting indexes (YSI). In conjunction with our previously developed database for gasoline compounds, which includes the physical and chemical properties, such as octane numbers, laminar burning velocity, and heat of vaporization, for more than 600 species, the identification of fuel replacements and additives can thus be performed jointly with respect to both their potential thermal efficiency benefits and emission formation characteristics in spark-ignition engines. For this purpose, a quantitative structure-property relationship (QSPR) model is developed to predict the YSI of fuel species by using artificial neural network (ANN) techniques with 21 well-selected functional group descriptors as input features. The model is trained and cross-validated with the YSI database reported by Yale University. It is then applied to estimate the YSI values of fuels available in the database for gasoline compounds and to explore the sensitivity of fuel’s sooting tendency on molecular groups. In addition, the correlation of YSI values with other properties available in the gasoline fuel database is examined to gain insights into the dependence of these properties. Finally, a selection of potential gasoline blending components is carried out exemplarily, by taking the fuels’ potential benefits in thermal engine efficiency and their soot formation characteristics jointly into account in terms of efficiency merit function and YSI, respectively.

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