This study proposes novel approach that combines optimization and machine learning to predict the aging of single-base propellants in alignment with the high-performance liquid chromatography (HPLC). To obtain aging and reduce HPLC experimentation, the proposed algorithm provides an efficient procedure. A hybrid strategy of genetic algorithm and ML models including support vector machine, ensemble trees, Gaussian process, and regression tree is combined to optimize SBPs aging. The correlation map provides an interdependence table of aging with initial composition, caliber, and environmental factors. The coefficient of determination and RMSE will determine predictive capabilities of ML models. ET-GA is an optimum-performing ML model with a 0.89 coefficient of determination. Partial dependence plots give an overview of the impact on aging by several variables with maximum impact by temperature, humidity, and diphenylamine. ET-GA shows 95% agreement with experimental data. This results in an economically viable interface that reduces experimentation and provides real-time aging prediction.

这项研究提出了结合优化和机器学习的新方法，通过高效液相色谱 (HPLC) 预测单碱推进剂的老化。为了获得老化并减少 HPLC 实验，所提出的算法提供了一个有效的程序。遗传算法和 ML 模型（包括支持向量机、集成树、高斯过程和回归树）的混合策略相结合，以优化 SBP 老化。相关图提供了老化与初始成分、口径和环境因素的相互依赖性表。决定系数和RMSE将决定 ML 模型的预测能力。ET-GA 是一种性能最佳的 ML 模型，其决定系数为 0.89。偏相关图概述了几个变量对老化的影响，其中温度、湿度和二苯胺的影响最大。ET-GA 显示与实验数据的一致性为 95%。这产生了经济上可行的界面，减少了实验并提供实时老化预测。