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Towards Model-Based Identification of Biofuels for Compression Ignition Engines

机译：朝向基于模型的基于模型识别压缩点火发动机的生物燃料

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Depleting fossil resources, a constantly rising energy demand and worries about greenhouse gas emissions force society to explore novel concepts for future mobile propulsion. In the context of biofuels, the identification of novel, sustainably producible, tailored molecules meeting the property specifications derived from advanced engine combustion concepts therefore constitutes a major objective. Due to the tremendous amount of possible molecular structures, solely experimental search strategies are infeasible and highlight the need for a computer-aided biofuel identification framework. To this end, a holistic approach for deriving truly predictive Quantitative-Structure-Property-Relationships (QSPRs) for engine-relevant fuel properties is presented. Such QSPRs are combined with a rigorous generation of molecular structures aiming at identification of single-compound fuel candidates for use in compression ignition (CI) engines. Besides addressing thermophysical fuel properties relevant to CI engines, an accurate QSPR model for the cetane number has been established and integrated into the fuel identification framework. A comprehensive list of potential CI fuel molecules is generated automatically and subsequently screened by sequentially applying property constraints to identify those compounds predicted to exhibit all desired physicochemical properties. In conjunction with the rigorous generation of structures, the outlined holistic QSPR methodology thus represents a feasible strategy for rapid computer-aided identification of fuel candidates tailored for optimal engine performance.
机译：耗尽化石资源，不断上升的能源需求和对温室气体排放力量社会的忧虑探索未来移动推进的新概念。在生物燃料的背景下，鉴定新颖的，可持续生产的，定制分子会满足来自先进的发动机燃烧概念的物质规范，因此构成了一个主要目标。由于可能的分子结构的巨大量，仅实验搜索策略是不可行的，并突出了对计算机辅助生物燃料识别框架的需求。为此，提出了一种用于导出真正预测定量结构 - 性质 - 关系（QSPRS）的整体方法，用于发动机相关的燃料特性。这种QSPRS与旨在鉴定用于压缩点火（CI）发动机的单复合燃料候选的严格产生的分子结构。除了寻址与CI发动机相关的热物理燃料特性外，已经建立并整合到燃料识别框架中建立并集成了Cetane号码的精确QSPR模型。通过顺序地施加特性约束来自动地产生综合的潜在CI燃料分子列表，以依次施加属性约束来鉴定预测的那些化合物表现出所有所需的物理化学性质。结合严谨的结构，概述的整体QSPR方法因此代表了用于快速计算机辅助识别的可行策略，用于针对最佳发动机性能而定制的燃料候选。

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