Design and optimization of higher efficiency, lower-emission internal combustion engines are highly dependent on fuel chemistry. Resolving chemistry for complex fuels, like gasoline, is challenging. A solution is to study a fuel surrogate: a blend of a small number of well-characterized hydrocarbons to represent real fuels by emulating their thermophysical and chemical kinetics properties. In the current study, an existing gasoline surrogate formulation algorithm is further enhanced by incorporating novel chemometric models. These models use infrared spectra of hydrocarbon fuels to predict octane numbers and are valid for a wide array of neat hydrocarbons and mixtures of such. This work leverages 14 hydrocarbon species to form tailored surrogate palettes for the fuels for advanced combustion engine (FACE) gasolines, including candidate component species not previously considered, namely, n-pentane, 2-methylpentane, 1-pentene, cyclohexane, and o-xylene. We evaluate the performance of “full” and “reduced” surrogates for the 10 fuels for advanced combustion engine gasolines, containing between 8–12 and 4–7 components, respectively. These surrogates match the target properties of the real fuels, on average, within 5%. This close agreement demonstrates that the algorithm can design surrogates matching the wide array of target properties, such as octane numbers, density, hydrogen-to-carbon ratio, distillation characteristics, and proportions of carbon–carbon bond types. We also compare our surrogates to those available in literature (FACE gasolines A, C, F, G, I, and J). Our surrogates for these fuels, on average, better match RON, MON, and distillation characteristics within 0.5%, 0.7%, and 0.9%, respectively, with literature surrogates at 1.2%, 1.1%, and 1.8% error, respectively. However, our surrogates perform slightly worse for density, hydrogen-to-carbon ratio, and carbon–carbon bond types at errors of 3.3%, 6.8%, and 2.2%, respectively, with literature surrogates at 1.3%, 2.3%, and 1.9%, respectively. Overall, the approach demonstrated here offers a promising method to better design surrogates for gasoline-like fuels with a wide array of properties.

中文翻译：

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通过增强的多元优化框架制定的FACE汽油替代品

高效，低排放内燃机的设计和优化高度依赖于燃料化学成分。解决复杂燃料（例如汽油）的化学反应具有挑战性。一种解决方案是研究燃料替代物：通过模拟其热物理和化学动力学特性，将少量特征明确的碳氢化合物的混合物代表真实的燃料。在当前的研究中，通过合并新的化学计量模型进一步增强了现有的汽油替代物制定算法。这些模型使用碳氢化合物燃料的红外光谱预测辛烷值，并且适用于各种各样的纯净碳氢化合物及其混合物。这项工作利用14种碳氢化合物来形成量身定制的替代调色板，用于高级内燃机（FACE）汽油的燃料，正戊烷，2-甲基戊烷，1-戊烯，环己烷和邻-二甲苯。我们评估了高级内燃机汽油的10种燃料的“完全”和“减少”替代物的性能，这些替代燃料分别包含8-12和4-7之间的成分。这些替代物与真实燃料的目标特性相匹配，平均在5％以内。这一密切的协议表明，该算法可以设计出与各种目标性质相匹配的替代物，例如辛烷值，密度，氢碳比，蒸馏特性和碳碳键类型比例。我们还将我们的替代品与文献（FACE汽油A，C，F，G，I和J）中的替代品进行比较。我们对这些燃料的替代物，平均而言，RON，MON和蒸馏特性的匹配度分别在0.5％，0.7％和0.9％之内，与文献替代物的误差分别为1.2％，1.1％和1.8％。然而，我们的替代指标在密度，氢碳比和碳-碳键类型方面表现稍差，误差分别为3.3％，6.8％和2.2％，文献替代指标分别为1.3％，2.3％和1.9％，分别。总体而言，此处演示的方法提供了一种有前途的方法，可以更好地设计具有多种特性的类汽油燃料的替代物。