Permeation Flux Prediction of Vacuum Membrane Distillation Using Hybrid Machine Learning Techniques
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Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2023-12
Department
Major/Subject
Mcode
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Language
en
Pages
19
Series
Membranes, Volume 13, issue 12
Abstract
Vacuum membrane distillation (VMD) has attracted increasing interest for various applications besides seawater desalination. Experimental testing of membrane technologies such as VMD on a pilot or large scale can be laborious and costly. Machine learning techniques can be a valuable tool for predicting membrane performance on such scales. In this work, a novel hybrid model was developed based on incorporating a spotted hyena optimizer (SHO) with support vector machine (SVR) to predict the flux pressure in VMD. The SVR–SHO hybrid model was validated with experimental data and benchmarked against other machine learning tools such as artificial neural networks (ANNs), classical SVR, and multiple linear regression (MLR). The results show that the SVR–SHO predicted flux pressure with high accuracy with a correlation coefficient (R) of 0.94. However, other models showed a lower prediction accuracy than SVR–SHO with R-values ranging from 0.801 to 0.902. Global sensitivity analysis was applied to interpret the obtained result, revealing that feed temperature was the most influential operating parameter on flux, with a relative importance score of 52.71 compared to 17.69, 17.16, and 14.44 for feed flowrate, vacuum pressure intensity, and feed concentration, respectively.Description
Publisher Copyright: © 2023 by the authors.
Keywords
desalination, flux pressure, global sensitivity analysis, machine learning, spotted hyena optimizer, vacuum membrane distillation
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Citation
Ismael, B H, Khaleel, F, Ibrahim, S S, Khaleel, S R, AlOmar, M K, Masood, A, Aljumaily, M M, Alsalhy, Q F, Mohd Razali, S F, Al-Juboori, R A, Hameed, M M & Alsarayreh, A A 2023, ' Permeation Flux Prediction of Vacuum Membrane Distillation Using Hybrid Machine Learning Techniques ', Membranes, vol. 13, no. 12, 900 . https://doi.org/10.3390/membranes13120900
