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

Khalid, Faizan; Aslam, Muhammad Nouman; Ghani, Muhammad Abdaal; Ahmad, Nouman; Abdullah,; Sattar, Khurram

doi:10.1016/j.chemolab.2023.105058

Chemometrics and Intelligent Laboratory Systems

2024

DOI: 10.1016/j.chemolab.2023.105058

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

Faizan Khalid,

Muhammad Nouman Aslam,

Muhammad Abdaal Ghani

et al.

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Cited by 4 publications

References 25 publications

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Adsorption Capacity Prediction and Optimization of Electrospun Nanofiber Membranes for Estrogenic Hormone Removal Using Machine Learning Algorithms

Yasir,

Haq,

Khan

et al. 2024

Polymers for Advanced Techs

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This study focuses on developing four machine learning (ML) models (Gaussian process regression (GPR), support vector machine (SVM), decision tree (DT), and ensemble learning tree (ELT)) optimized and hyperparameters tuned via genetic algorithm (GA) and particle swarm optimization (PSO) to analyze and predict the adsorption capacity of four estrogenic hormones. These hormones are a serious cause of fish femininity and various forms of cancer in humans. Their adsorption via electrospun nanofibers offers a sustainable and relatively environmentally friendly solution compared to nanoparticle adsorbents, which require secondary treatment. The intricate task is to find the relationship between input parameters to obtain optimum conditions, which requires an efficient ML model. The GPR integrated GA hybrid model performed the most accurate and precise results with R2 = 0.999 and RMSE = 2.4052e−06, followed by ELT (0.9976 and 4.3458e−17), DT (0.9586 and 2.4673e−16), and SVM (0.7110 and 0.0639). The 2D and 3D partial dependence plots showed temperature, dosage, initial concentration, contact time, and pH as vital adsorption parameters. Additionally, Shapley's analysis further revealed time and dosage as the most sensitive parameters. Finally, a user‐friendly graphical user interface (GUI) was developed as a predictor utilizing the optimum hybrid model (GPR‐GA), and the results were experimentally validated with a maximum error of < 3.3% for all tests. Thus, the GUI can legitimately work for any desired material with given input conditions to efficiently monitor the removal concentration of all four estrogenic hormones simultaneously at wastewater treatment plants.

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Adsorption Capacity Prediction and Optimization of Electrospun Nanofiber Membranes for Estrogenic Hormone Removal Using Machine Learning Algorithms

Yasir,

Haq,

Khan

et al. 2024

Polymers for Advanced Techs

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Enhancing accuracy in equivalent in-service-time assessment for homogeneous solid propellants: A novel temperature-independent predictive model utilizing PCA of FTIR data

Chelouche,

Trache,

Abdelaziz

et al. 2025

FirePhysChem

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Impact of inhibition mechanisms, automation, and computational models on the discovery of organic corrosion inhibitors

Winkler,

Hughes,

Özkan

et al. 2025

Progress in Materials Science

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

Cited by 4 publications

References 25 publications

Adsorption Capacity Prediction and Optimization of Electrospun Nanofiber Membranes for Estrogenic Hormone Removal Using Machine Learning Algorithms

Adsorption Capacity Prediction and Optimization of Electrospun Nanofiber Membranes for Estrogenic Hormone Removal Using Machine Learning Algorithms

Enhancing accuracy in equivalent in-service-time assessment for homogeneous solid propellants: A novel temperature-independent predictive model utilizing PCA of FTIR data

Impact of inhibition mechanisms, automation, and computational models on the discovery of organic corrosion inhibitors

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