Integrated machine learning-quantitative structure property relationship (ML-QSPR) and chemical kinetics for high throughput fuel screening toward internal combustion engine
Integrated machine learning-quantitative structure property relationship (ML-QSPR) and chemical kinetics for high throughput fuel screening toward internal combustion engine
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DOI：
10.1016/j.fuel.2021.121908
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发表时间：
2022-01
期刊：
影响因子：
7.4
通讯作者：
Run Li;J. Herreros;A. Tsolakis;Wenzhao Yang
中科院分区：
文献类型：
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作者：
Run Li;J. Herreros;A. Tsolakis;Wenzhao Yang
这项工作提出了一种高通量燃料筛选方法，用于在面向性能的燃料设计的早期阶段识别具有内燃机所需性能的分子。虚拟筛选是一种类似漏斗的方法，包含 Tier 1 燃料理化特性筛选和 Tier 2 化学筛选。第 1 层筛选基于机器学习定量结构属性关系 (ML-QSPR) 模型，针对熔点、沸点、蒸气压、汽化焓、十六烷值、研究法辛烷值、马达辛烷值、着火温度、闪点、烟灰指数、液体密度、低位热值、表面张力、可燃下/上极限等 15 种属性。关键是确定给定发动机架构和燃烧策略的选定属性的目标值。第 2 层筛选检查点火延迟时间、Φ 灵敏度和层流火焰速度，以评估燃料反应性、Φ 分层燃烧潜力、燃烧速率和稀释耐受性。优点函数提供了一个简单的工具，可以根据第 1 层和第 2 层筛选中计算的属性来评估燃料与发动机相互作用的潜在效益。通过对增压火花点火发动机进行案例研究来展示燃料筛选工作流程。虚拟筛选可以以时间、资源、劳动力和成本效益的方式加速以属性为导向的燃料设计，并确定有希望的候选物进行实验测试作为最终验证。该范式旨在激发数据驱动燃料筛选的新思路并推动其在能源领域的应用。
This work proposes a high throughput fuel screening approach to identify molecules with desired properties for internal combustion engine at the early stage of property-oriented fuel design. The virtual screening is a funnel-like approach containing Tier 1 fuel physicochemical property screening and Tier 2 chemical screening. Tier 1 screening is based on the machine learning quantitative structure property relationship (ML-QSPR) models for 15 properties of melting point, boiling point, vapor pressure, enthalpy of vaporization, cetane number, research octane number, motor octane number, ignition temperature, flash point, yield sooting index, liquid density, lower heating value, surface tension, lower/upper flammability limit. The key is to identify the target values for the selected properties for a given engine architecture and combustion strategy. Tier 2 screening inspects the ignition delay time, ϕ-sensitivity and laminar flame speed to evaluate the fuel reactivity, the potential of ϕ stratification combustion, combustion rate and dilution tolerance. Merit function provides a simple tool to assess the potential benefit of fuel-engine interaction based on the properties computed in Tier 1 and Tier 2 screenings. A case study for boosted spark ignition engine is performed to showcase the fuel screening workflow. The virtual screening can accelerate the property-oriented fuel design in a time, resource, labor, cost-effective way and identify the promising candidates for the experimental test as ultimate validation. This paradigm aims at inspiring the new ideas of data-driven fuel screening and promoting the application in the energy sector.