Machine Learning Assisted Prediction of Fouling Recovery Ratio of Ultrafiltration Membranes
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Publish Year: 1401
Type: Conference paper
Language: English
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OILANDGAS01_039
Index date: 26 August 2023
Machine Learning Assisted Prediction of Fouling Recovery Ratio of Ultrafiltration Membranes abstract
Intelligent approaches based on multilayer perceptron (MLP) and gaussian process regression (GPR) were applied for modelling to estimate the fouling recovery ratio (FRR) of ultrafiltration membrane for waste water treatment. The pressure, temperature, and pH were used as variables. The GPR model showed an excellent agreement with experimental data with average absolute relative error (AARE) of 0.87% relative root mean squared error (RRMSE) of 1.40% and R2 of 99.29%. The performance of the GPR model for prediction FRR were assessed and acceptable results were obtained. A sensitivity analysis was showed that the pressure is the most effective parameter on membrane FRR, which is followed by pH and temperature, respectively
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Machine Learning Assisted Prediction of Fouling Recovery Ratio of Ultrafiltration Membranes authors
T Kikhavani
Assistant Professor, Department of Chemical Engineering, Ilam University, Ilam ۶۹۳۱۵-۵۱۶, Iran
M. Tavakol moghadam
Assistant Professor, Deputy of Technology and International Affairs, Research Institute of Petroleum Industry, (RIPI) Tehran, Iran