Nallakukkala, Sirisha and Tackie-Otoo, Bennet Nii and Aliyu, Ruwaida and Lal, Bhajan and Nallakukkala, Jagadish Ram Deepak and Devi, Gayathri (2025) Application of machine learning algorithms to predict removal efficiency in treating produced water via gas hydrate-based desalination. Desalination, 612. ISSN 00119164
Full text not available from this repository.Abstract
The integration of machine learning (ML) with gas hydrate-based desalination (GHBD) presents a significant advancement in the produced water treatment with special focus on efficient prediction of removal efficiency. GHBD operates by forming gas hydrates under controlled thermodynamic conditions, selectively encapsulating gas within water molecules while excluding dissolved ions. However, the stochastic nature of hydrate formation, is influenced by gas composition, temperature, pressure, and ion concentration, makes it difficult to predict accurately removal efficiency. In this context. ML algorithms provide powerful data driven means to model complex relationship within experimental datasets to improve process optimisation This study systematically evaluated several supervised ML models, including Random Forest (RF) Support Vector Machines (SVM), Ridge Regression, Lasso Regression, Decision Tree, Extra Tree Regression, Gradient Boost, and XGBoost, to predict removal efficiency in GHBD system. Among these, the SVM model showed the best predictive accuracy, R2 of 0.98, with the lowest AIC (56.75), RMSE (1.50), and MAE (1.22) values, highlighting its robustness in capturing the intricate dependencies between operational parameters and removal performance. Additionally, graphical analysis confirmed that the predictive accuracy of SVM model is superior, compared to other models. Furthermore, sensitivity analyses validated SVM's robustness in capturing the nonlinear relationships governing ion removal efficiency. These findings demonstrate that integration of ML with GHBD significantly improved predictive capabilities, enabled real time application, reduce experimental effort, as well as improve the development of intelligent, sustainable, and scalable water treatment technology. © 2025 Elsevier B.V.
Item Type: | Article |
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Impact Factor: | Cited by: 0 |
Uncontrolled Keywords: | Random forests; Efficient predictions; Forming gas; Machine learning algorithms; Machine-learning; Predictive accuracy; Produced water treatments; Removal efficiencies; Support vector machine models; Thermodynamic conditions; Water molecule; algorithm; desalination; gas hydrate; machine learning; prediction; water treatment; Support vector regression |
Depositing User: | Mr Ahmad Suhairi Mohamed Lazim |
Date Deposited: | 04 Jul 2025 16:32 |
Last Modified: | 04 Jul 2025 16:32 |
URI: | http://scholars.utp.edu.my/id/eprint/38852 |