Design optimization and predictive modeling for TSS in mega surface WWTPs: A machine learning approach.

Surface wastewater originates from various sources, including domestic, commercial, and industrial activities, and contains a mix of organic and inorganic wastes along with suspended and dissolved solids that require effective treatment. This study proposes a treatment process design for surface wastewater, utilizing unit-by-unit mass balances for flow rates (Q), total suspended solids (TSS), and total dissolved solids (TDS). Additionally, machine learning (ML) techniques were employed to develop predictive models aimed at achieving high efficiency in TSS removal. A flow rate (Q) of 50 mgd was selected, based on a target output (Q) of 45 mgd, with influent concentrations of TSS at 100 mg/L and TDS at 260 mg/L. Training involved four input-parameter models: Q-input, TDS-input, Q-TDS-input, TDS-Q-input, and Q-TDS-Y/N-input. Results indicated that screening and sedimentation processes could reduce TSS by 50% and over 95%, respectively, with an overall flow recovery of 90% and removal efficiencies of 99.8% for TSS. In stratified mono-media filters, the use of coarse sand particles (0.8-2 mm) achieved a 500% reduction in drag coefficient (C) and a 10-fold decrease in head loss (h). The Fine-Trees (FT) from the Q-TDS ML regression/classification model demonstrated the lowest mean squared error (MSE), with high-to-low classification accuracies from FT, RLR/ILR, LSVM, and MGSVM models, respectively. The ML models reliably predicted whether TSS<50 mg/L or not, indicating suitability of treated water for irrigation applications. This study establishes a foundation for linking design optimization with predictive modeling to effectively select critical design parameters for surface wastewater treatment.