Predictive modelling of aerobic membrane bioreactors using machine learning algorithms for the treatment of wastewater from fish canneries
DOI:
https://doi.org/10.24425/jwld.2025.155304Abstract
Process modelling is an effective tool for describing and predicting the performance of an aerobic membrane bioreactor (AeMBR) for fish canning wastewater (FCWW) treatment under different operating conditions. Three machine learning (ML) algorithms were developed, random forest (RF), decision tree regressor (DTR) and adaptive boosting regression (AdaBoost-R), based on various physico-chemical characteristics of the influent and operating conditions, including hydraulic retention times (HRT), organic loading rates (OLR), total dissolved solids (TDS), aeration rate and permeate volumetric rates. Predicted values for chemical oxygen demand (COD), biochemical oxygen demand (BOD5 ), total Kjeldahl nitrogen (TKN), and nitrate (NO3-) are compared with those reported from the experiment. As regards the quantitative assessment of the three predictive models, the DTR model demonstrated a modest determination coefficient (R2) value of 0.654, the AdaBoost-R model achieved 0.739, whereas the RF model showed the highest performance at 0.98. Due to its robustness and accuracy, the RF model was chosen for its superior ability to predict the performance of the AeMBR. Based on OLR of 4.27 (kg COD)∙(m33 ∙d)-1, a HRT of 24 h, a TDS of 3 g∙dm-3 , an aeration rate of 1,300 Ndm3 ∙h-1 and a permeate volumetric rate of 15 dm3 ∙h-1, the average effluent characteristics comply with discharge and reuse limits.References
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