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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| Veröffentlicht in: | Chemometrics and intelligent laboratory systems Jg. 245; S. 105058 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier B.V
15.02.2024
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| ISSN: | 0169-7439, 1873-3239 |
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| Abstract | 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.
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•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. |
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| AbstractList | 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. |
| ArticleNumber | 105058 |
| Author | Sattar, Khurram Khalid, Faizan Ahmad, Nouman Ghani, Muhammad Abdaal Abdullah Aslam, Muhammad Nouman |
| Author_xml | – sequence: 1 givenname: Faizan surname: Khalid fullname: Khalid, Faizan organization: School of Chemical and Materials Engineering, National University of Science and Technology, Islamabad, Pakistan – sequence: 2 givenname: Muhammad Nouman orcidid: 0000-0002-5201-221X surname: Aslam fullname: Aslam, Muhammad Nouman email: mnouman@scme.nust.edu.pk organization: School of Chemical and Materials Engineering, National University of Science and Technology, Islamabad, Pakistan – sequence: 3 givenname: Muhammad Abdaal surname: Ghani fullname: Ghani, Muhammad Abdaal organization: School of Chemical and Materials Engineering, National University of Science and Technology, Islamabad, Pakistan – sequence: 4 givenname: Nouman surname: Ahmad fullname: Ahmad, Nouman organization: School of Chemical and Materials Engineering, National University of Science and Technology, Islamabad, Pakistan – sequence: 5 surname: Abdullah fullname: Abdullah organization: Queen’s University Belfast, Belfast, United Kingdom – sequence: 6 givenname: Khurram surname: Sattar fullname: Sattar, Khurram organization: School of Chemical and Materials Engineering, National University of Science and Technology, Islamabad, Pakistan |
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| Cites_doi | 10.1007/s11750-021-00594-1 10.3390/app9153019 10.5897/AJPAC2020.0859 10.1007/s10973-020-09304-8 10.1016/j.ijpe.2020.107776 10.1002/prep.201500196 10.1016/j.eng.2022.01.008 10.1007/s10853-017-1474-y 10.1007/s11042-020-10139-6 10.1016/j.physleta.2020.126500 10.1016/j.csda.2007.11.008 10.1016/j.physc.2022.1354062 10.1063/1.4946894 10.1016/j.jaap.2023.105879 10.3390/molecules28010322 10.1002/prep.201500040 10.1080/07370652.2020.1859646 10.1002/prep.202200267 10.1038/s41562-018-0297-4 10.1016/j.talanta.2012.10.035 10.1016/j.saa.2022.121906 10.1002/prep.202100210 10.1016/j.cherd.2022.06.020 10.3390/s23115064 10.1016/0141-3910(93)90211-Z |
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| Keywords | Propellants Genetic algorithm Energetic material Machine learning Nitro cellulose |
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| Title | Aging prediction in single based propellants using hybrid strategy of machine learning and genetic algorithm |
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