Geosmin and 2-methylisoborneol adsorption using different carbon materials: Isotherm, kinetic, multiple linear regression, and deep neural network modeling using a real drinking water source

Abstract

High concentrations of geosmin (GSM) and 2-methylisoborneol (MIB) caused by cyanobacterial blooms are problematic issues in drinking water treatment plants on the Nak-Dong River in South Korea. The goal of this study was to identify the best-performing carbon materials to treat GSM and MIB in river water and to predict the final concentrations of GSM and MIB according to different water quality parameters using computational predictive modeling, multiple linear regression (MLR), and a deep neural network (DNN) for practical applications. Three types of powdered activated carbon (PAC) and three types of mesoporous carbon (MC) were compared in terms of their adsorption capacities using a source of drinking water from the Nak-Dong River. The highest maximum adsorption capacities were achieved when using C-PAC (1485.06 mu g/g) in both distilled water and river water. C-PAC possesses a high micropore volume (0.45 cm3/g), low mesopore volume (0.11 cm3/g), and small, narrow pore size distribution, yielding optimal adsorption conditions. PACs yielded better results than MCs because dominant mesopore structures can hinder the adsorption of small molecules (0.6-0.8 nm) and allow them to pass through pores. GSM was removed more effectively than MIB because GSM is more hydrophobic and has a flatter structure. The high dissolved organic carbon concentration in the river water caused no reduction in GSM and MIB adsorption, but actually enhanced adsorption because a small portion of natural organic matter can compete with adsorption sites and provide additional adsorption sites based on the shrunk pore effect. The MLR and DNN models were used to predict the removal efficiency of C-PAC for GSM and MIB using 72 individual datasets with cross-validation for robust prediction and sensitivity analysis. MLR predicted the relationships between the input variables and GSM and MIB concentrations with an acceptable mean absolute error (MAE) of 6.43 ng/L for GSM and 5.80 ng/L for MIB. However, the DNN provided better agreement with a higher R2 value (>0.99) and lower MAE of 1.67 ng/L for GSM and 1.24 ng/L for MIB.