Using artificial intelligence-based algorithms to identify critical fouling factors and predict fouling behavior in anaerobic membrane bioreactors

Membrane fouling is one of the major obstacles that hinder the widespread applications of anerobic membrane bioreactors (AnMBRs) for wastewater treatment. Due to the intrinsic complexity of membrane fouling, identifying critical fouling factors and predicting fouling behavior are of great significance in membrane fouling control. Herein, artificial intelligence (AI) algorithms and their modeling framework were developed to predict membrane fouling behaviors in AnMBRs. Operating parameters, biomass properties, and membrane characteristics were considered as input variables for membrane fouling prediction. The results of hyper-parameter optimization showed that the optimal architecture of artificial neural network (ANN) model for membrane fouling prediction was “14-9-6-1” while the best hyper-parameters of random forest (RF) model were n_trees = 1200 and n_features = 14. After hyper-parameter adjusting, RF had more robustness of predictive capabilities (R2=0.906, mean squared error (MSE) = 0.061) for membrane fouling than the ANN model (R2=0.800, MSE = 0.118). More importantly, the feature importance and Shapley additive explanations analysis indicated SMPp/SMPc (0.281) > EPSp/EPSc (0.110) > organic loading rate (0.106), which were the most critical factors affecting membrane fouling. Partial dependence plot analysis further verified the marginal and interaction effects of various input features on membrane fouling. The revealed partial dependence relationships of critical variables can provide theoretical reference for optimization of practical operation. Overall, this study established a novel AI-based approach for predicting membrane fouling and comprehensively understanding the complicated effects of various influencing factors on membrane fouling while overcoming the “black-box” nature of conventional models.