Developing a new approach for design support of subsurface constructed wetland using machine learning algorithms.

Knowing the effluent quality of treatment systems in advance to enable the design of treatment systems that comply with environmental standards is a realistic strategy. This study aims to develop machine learning - based predictive models for designing the subsurface constructed wetlands (SCW). Data from the SCW literature during the period of 2009-2020 included 618 sets and 10 features. Five algorithms namely, Random forest, Classification and Regression trees, Support vector machines, K-nearest neighbors, and Cubist were compared to determine an optimal algorithm. All nine input features including the influent concentrations, C:N ratio, hydraulic loading rate, height, aeration, flow type, feeding, and filter type were confirmed as relevant features for the predictive algorithms. The comparative result revealed that Cubist is the best algorithm with the lowest RMSE (7.77 and 21.77 mg.L-1 for NH4-N and COD, respectively) corresponding to 84% of the variance in the effluents explained. The coefficient of determination of the Cubist algorithm obtained for NH4-N and COD prediction from the test data were 0.92 and 0.93, respectively. Five case studies of the application of SCW design were also exercised and verified by the prediction model. Finally, a fully developed Cubist algorithm-based design tool for SCW was proposed.

A hybrid constructed wetland for organic-material and nutrient removal from sewage: Process performance and multi-kinetic models.

A pilot-scale hybrid constructed wetland with vertical flow and horizontal flow in series was constructed and used to investigate organic material and nutrient removal rate constants for wastewater treatment and establish a practical predictive model for use. For this purpose, the performance of multiple parameters was statistically evaluated during the process and predictive models were suggested. The measurement of the kinetic rate constant was based on the use of the first-order derivation and Monod kinetic derivation (Monod) paired with a plug flow reactor (PFR) and a continuously stirred tank reactor (CSTR). Both the Lindeman, Merenda, and Gold (LMG) analysis and Bayesian model averaging (BMA) method were employed for identifying the relative importance of variables and their optimal multiple regression (MR). The results showed that the first-order-PFR (M2) model did not fit the data (P > 0.05, and R2 < 0.5), whereas the first-order-CSTR (M1) model for the chemical oxygen demand (CODCr) and Monod-CSTR (M3) model for the CODCr and ammonium nitrogen (NH4-N) showed a high correlation with the experimental data (R2 > 0.5). The pollutant removal rates in the case of M1 were 0.19 m/d (CODCr) and those for M3 were 25.2 g/m2∙d for CODCr and 2.63 g/m2∙d for NH4-N. By applying a multi-variable linear regression method, the optimal empirical models were established for predicting the final effluent concentration of five days' biochemical oxygen demand (BOD5) and NH4-N. In general, the hydraulic loading rate was considered an important variable having a high value of relative importance, which appeared in all the optimal predictive models.

Can electrocoagulation process be an appropriate technology for phosphorus removal from municipal wastewater?

This paper evaluated a novel pilot scale electrocoagulation (EC) system for improving total phosphorus (TP) removal from municipal wastewater. This EC system was operated in continuous and batch operating mode under differing conditions (e.g. flow rate, initial concentration, electrolysis time, conductivity, voltage) to evaluate correlative phosphorus and electrical energy consumption. The results demonstrated that the EC system could effectively remove phosphorus to meet current stringent discharge standards of less than 0.2mg/L within 2 to 5min. This target was achieved in all ranges of initial TP concentrations studied. It was also found that an increase in conductivity of solution, voltages, or electrolysis time, correlated with improved TP removal efficiency and reduced specific energy consumption. Based on these results, some key economic considerations, such as operating costs, cost-effectiveness, product manufacturing feasibility, facility design and retrofitting, and program implementation are also discussed. This EC process can conclusively be highly efficient in a relatively simple, easily managed, and cost-effective for wastewater treatment system.