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Single and Ensemble Explainable Machine Learning-based Prediction of Membrane Flux in the Reverse Osmosis Process by Mohammed Talhami; Tadesse Wakjira; Tamara Alomar; Sohila Fouladi; Fatima Fezouni; Usama Ebead; Ali Altaee; Maryam AL-Ejji; Probir Das; Alaa H. Hawari is a Environmental Science article available to read on EtoBox.

What is Single and Ensemble Explainable Machine Learning-based Prediction of Membrane Flux in the Reverse Osmosis Process about?

Reverse osmosis is the most popular membrane-based desalination process that accounts presently for more than half the worldwide desalination capacity. However, the complex involvement of a variety of factors in this process has hindered the efficient assessment of the process performance such as accurately determining the membrane flux. It is therefore indispensable to search for reliable and flexible tools for the estimation of membrane flux in reverse osmosis such as machine learning. In this study, for the first time, nine different machine learning algorithms, ranging from simple white box models to complex black box models, were investigated for the accurate prediction of membrane flux in reverse osmosis using a large dataset of 401 experimental points retrieved from literature with 8 distinct features. The investigation has shown superior predictive performance for ensemble models over single models. In addition, extreme gradient boosting stood out as the best-performing ensemble model for the prediction of membrane flux due to having the lowest statistical errors (MAE = 1.78 LMH, MAPE = 8.88 %, and RMSE = 2.32 LMH) and strongest correlations with R 2 = 98.2 %, IA = 99.55 %,

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Author

Mohammed Talhami; Tadesse Wakjira; Tamara Alomar; Sohila Fouladi; Fatima Fezouni; Usama Ebead; Ali Altaee; Maryam AL-Ejji; Probir Das; Alaa H. Hawari

Publisher

Elsevier BV

Published

2024

Language

EN

Field

Environmental Science (Physical Sciences)