ArticleScientific reports2024
Development of novel computational models based on artificial intelligence technique to predict liquids mixtures separation via vacuum membrane distillation.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Computational evaluation using machine learning for analysis of membrane desalination process powered by solar energy.Scientific reports · 2025Article
- Research Progress of Computational Fluid Dynamics in Mixed Ionic-Electronic Conducting Oxygen-Permeable Membranes.Membranes · 2025Review
- Computational intelligence analysis on drug solubility using thermodynamics and interaction mechanism via models comparison and validation.Scientific reports · 2024Article
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1 author.
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Abstract
The fundamental objective of this paper is to use Machine Learning (ML) methods for building models on temperature (T) prediction using input features r and z for a membrane separation process. A hybrid model was developed based on computational fluid dynamics (CFD) to simulate the separation process and integrate the results into machine learning models. The CFD simulations were performed to estimate temperature distribution in a vacuum membrane distillation (VMD) process for separation of liquid mixtures. The evaluated ML models include Support Vector Machine (SVM), Elastic Net Regression (ENR), Extremely Randomized Trees (ERT), and Bayesian Ridge Regression (BRR). Performance was improved using Differential Evolution (DE) for hyper-parameter tuning, and model validation was performed using Monte Carlo Cross-Validation. The results clearly indicated the models' effectiveness in temperature prediction, with SVM outperforming other models in terms of accuracy. The SVM model had a mean R
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