This study aims to improve the efficiency and reliability of membrane filtration systems used to treat produced water by accurately predicting lift force, transmembrane pressure (TMP), and total resistance (TR) using ML models, namely linear, K-nearest neighbors (KNN), and artificial neural networks (ANN). The dataset was split into training, validation, and test sets, allowing for comprehensive training and evaluation of model performance. Linear, KNN and ANN models achieved high R-squared scores (R-2) of 0.998, 0.996, and 0.999 and low mean squared error (MSE) values of 0.0000046, 0.0002763, and 0.000004621 for Lift force prediction, respectively. For TMP prediction, these models showed R-2 values of 0.801, 0.90, and 0.72 and MSE values of 0.01236, 0.109, and 0.283, respectively. Similarly, these models predicted TR with R-2 scores of 0.76, 0.78, and 0.78 and MSE values of 0.2087, 0.025, and 0.22, respectively. These results indicate that KNN is particularly efficient in accurately predicting the membrane parameters and could be a robust tool to optimize membrane filtration processes. Response surface modelling (RSM) elucidates the relationship between input and output parameters. Initially, the increase in drop size and shear has a minimal impact on the lift force. Beyond a drop size of 0.3 mu m and shear rate 1200 S-1, lift force rises significantly. TMP initially increases with shear rate, peaking at a value of 1200 S-1, then decreases with higher shear rates. Optimal conditions identified are higher shear rates and drop sizes >0.3 mu m to maintain lower TMP and effective filtration. Findings from the study suggest that employing predictive modelling can significantly enhance the efficiency and reliability of produced water treatment, by improving contaminant removal, reducing energy consumption and cost, potentially reducing environmental impacts, and improving sustainability in the oil and gas industry.
