Membrane fouling in microfiltration of oil-in-water emulsions; a comparison between constant pressure blocking laws and genetic programming (GP) model

• Individual blocking laws were applied to predict oil/water microfiltration.
• Five combined blocking models were applied to provide better fitting results.
• Combined models did not provide better fits of the data than individual models.
• Genetic programming model was successfully used to model the membrane performance.
• The dominant fouling mechanisms were identified based on the combined models.

Microfiltration of oil-in-water emulsion with different concentrations and TMPs was experimentally performed to investigate the fouling mechanisms of oil droplets. In this work, the performance of both blocking laws and genetic programming model was evaluated. Four individual and five combined blocking models were applied to determine if they would provide acceptable fits of the experimental data. In individual models, the best predictions were obtained by the intermediate model and the cake model failed to provide any fit of the experimental data in all data sets. Although the combined models used two fitted parameters, they did not provide better fits of the data than individual models. The intermediate model combined with the cake filtration model and standard model provided the same fit as the intermediate model alone. In addition, genetic programming as a novel approach in membrane fouling was used to predict both permeate flux and oil rejection. It was found that for the studied system, the GP model not only was able to provide better fits of experimental data, but also predicted the oil rejection with an acceptable accuracy. The dominant fouling mechanisms were also identified in different operating conditions.

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