Aging prediction in single based propellants using hybrid strategy of machine learning and genetic algorithm

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 proced...

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Bibliographic Details
Published in:Chemometrics and intelligent laboratory systems Vol. 245; p. 105058
Main Authors: Khalid, Faizan, Aslam, Muhammad Nouman, Ghani, Muhammad Abdaal, Ahmad, Nouman, Abdullah, Sattar, Khurram
Format: Journal Article
Language:English
Published: Elsevier B.V 15.02.2024
Subjects:
Energetic material
Genetic algorithm
Machine learning
Nitro cellulose
Propellants
Propellants
Genetic algorithm
Energetic material
Machine learning
Nitro cellulose
ISSN:0169-7439, 1873-3239
Online Access:Get full text
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Summary: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. [Display omitted] •Prediction of Enhanced Aging of Single Base Propellants using ML models.•Utilization of Genetic Algorithm for feature selection and hyperparameters tuning.•Understand features importance and their interaction effects on Enhanced Aging.
ISSN:0169-7439
1873-3239
DOI:10.1016/j.chemolab.2023.105058