利用液相FTIR和机器学习预测可持续航空燃料性质的结构化框架
A Structured Framework for Predicting Sustainable Aviation Fuel Properties using Liquid-Phase FTIR and Machine Learning
AI总结：
本文提出基于液相FTIR光谱与NMF特征的结构化机器学习框架，构建五个可解释集成模型以预测航空燃料关键理化性质，并通过交互式网络工具开放模型与数据。
摘要：
可持续航空燃料具有减少排放和环境影响的潜力。为帮助识别可行的可持续航空燃料并加快研究，已有若干机器学习模型被开发用于预测相关理化性质。然而，许多模型适用性有限，依赖来自光谱范围受限的复杂分析技术的数据，或使用可解释性有限的特征分解方法。本研究利用液相傅里叶变换红外（FTIR）光谱，提出了一种结构化方法，用于为纯分子、航空燃料和调合物建立准确且可解释的性质预测模型。液相FTIR光谱测量可快速、一致地采集，使用少于2 mL样品即可提供高可靠性、灵敏度和组分特异性。该方法首先使用非负矩阵分解（NMF）将FTIR光谱分解为基本构建块，以实现对FTIR光谱属性和燃料性质的科学分析。随后，NMF特征被用于创建五个集成模型，预测终馏点、闪点、冰点、15°C下的密度以及-20°C下的运动黏度。所有模型均使用来自纯分子、航空燃料和调合物的实验性质数据进行训练。这些模型能够准确预测性质，同时支持解释燃料组成元素（如官能团或化学类别）与其性质之间的关系。为支持可持续航空燃料研发，模型和数据已在一个交互式网络工具上提供。
英文摘要：
Sustainable aviation fuels have the potential for reducing emissions and environmental impact. To help identify viable sustainable aviation fuels and accelerate research, several machine learning models have been developed to predict relevant physiochemical properties. However, many of the models have limited applicability, leverage data from complex analytical techniques with confined spectral ranges, or use feature decomposition methods that have limited interpretability. Using liquid-phase Fourier Transform Infrared (FTIR) spectra, this study presents a structured method for creating accurate and interpretable property prediction models for neat molecules, aviation fuels, and blends. Liquid-phase FTIR spectra measurements can be collected quickly and consistently, offering high reliability, sensitivity, and component specificity using less than 2 mL of sample. The method first decomposes FTIR spectra into fundamental building blocks using Non-negative Matrix Factorization (NMF) to enable scientific analysis of FTIR spectra attributes and fuel properties. The NMF features are then used to create five ensemble models for predicting final boiling point, flash point, freezing point, density at 15C, and kinematic viscosity at -20C. All models were trained using experimental property data from neat molecules, aviation fuels, and blends. The models accurately predict properties while enabling interpretation of relationships between compositional elements of a fuel, such as functional groups or chemical classes, and its properties. To support sustainable aviation fuel research and development, the models and data are available on an interactive web tool.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
