Thermal stability enhancement and prediction by ANN model

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Energy and AI Pub Date : 2024-01-28 DOI:10.1016/j.egyai.2024.100348
Ziyu Liu , Xiaoyi Yang
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Abstract

Supersonic aircraft requires thermal endurance of aviation fuel in the process of cooling engine and aircraft. As the composition of petroleum-based jet fuel (RP-3) is confined by crude oil and refining process, sustainable alternative jet fuel with green house gas reduction become to undertake the composition optimization for improving thermal stability. For designing aviation fuel with robust thermal stability and the detail understanding of thermal stability mechanism, RP-3, Fischer–Tropsch fuel, and additives with cyclic structure for absorbing free radical, were investigated thermal stability by modifying different blend ratios under different conditions. Thermal endurance degree was assessed by chroma and deposition tendency. FT blend with cyclic hydrocarbon can improve thermal endurance degree. In compliance with individual optimized blend ratio, the contribution follows methyl cyclopentane > decalin > methyl cyclohexane > tetralin > n-propyl-benzene > 1,2,4 trimethyl-benzene. The appropriate blend ratio could undertake hydrogen donors for terminating the propagation of oxygen-carrying radical, but hydrocarbons with cyclic structure could enhance deposition tendency. Methyl cyclopentane and its oxidation derivatives take the roles of solvent by anti-polymerization and hydrogen donor by opening cyclic structure in the thermal endurance process, and thus lead to a wide range of blend ratio for improving significantly thermal stability. β-scission leading to C–C bond cleavage is the major reaction at the early decomposition stage, which resulted in most abundant derivatives plus C2. The effects of additives on thermal stability are complex and nonlinear on the tendency of thermal deposits and thermal endurance degree, and thus the appropriate ANN-thermal stability model has been trained based on the experiment data and can achieve above 0.995 correlation coefficient. ANN - thermal stability model can predict not only the content of derivatives including ester, olefin, alcohol, ketone, cyclic oxide, aromatics but also the degree of thermal endurance.

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热稳定性增强和 ANN 模型预测
超音速飞机在冷却发动机和飞机的过程中需要航空燃料的热稳定性。由于石油基喷气燃料(RP-3)的成分受原油和提炼工艺的限制,因此,可持续的、减少温室气体排放的替代喷气燃料就成为提高热稳定性的成分优化对象。为了设计热稳定性强的航空燃料,并详细了解热稳定性机理,研究人员在不同条件下,通过改变不同的混合比例,研究了 RP-3、费托燃料和具有吸收自由基的环状结构添加剂的热稳定性。通过色度和沉积趋势评估热稳定性。FT 与环状碳氢化合物的混合可提高热稳定性。根据各自优化的混合比例,其贡献依次为甲基环戊烷、癸醛、甲基环己烷、四萘、正丙基苯和 1,2,4-三甲基苯。适当的混合比例可以为终止携氧自由基的传播提供氢供体,但具有环状结构的碳氢化合物会增强沉积趋势。甲基环戊烷及其氧化衍生物在热稳定性过程中既是抗聚合的溶剂,又是开启环状结构的供氢体,因此可在较宽的混合比范围内显著提高热稳定性。在早期分解阶段,C-C 键裂解导致的 β 裂解是主要反应,从而产生了最丰富的衍生物加 C2。添加剂对热稳定性的影响是复杂的,与热沉积趋势和热耐受程度呈非线性关系,因此根据实验数据训练了合适的 ANN 热稳定性模型,相关系数可达 0.995 以上。ANN 热稳定性模型不仅能预测酯、烯烃、醇、酮、环氧化物、芳烃等衍生物的含量,还能预测热稳定性的程度。
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来源期刊
Energy and AI
Energy and AI Engineering-Engineering (miscellaneous)
CiteScore
16.50
自引率
0.00%
发文量
64
审稿时长
56 days
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