In this study, the performances of GAC adsorption and GAC bioadsorption in terms of dissolved organic carbon (DOC) removal were investigated with synthetic biologically treated sewage effluent (BTSE), synthetic primary treated sewage effluent (PTSE), real BTSE and real PTSE. The main aims of this study are to verify and compare the efficiency of DOC removal by GAC (adsorption) and acclimatized GAC (bioadsorption). The results indicated that the performance of bioadsorption was significantly better than that of adsorption in all cases, showing the practical use of biological granular activated carbon (BGAC) in filtration process. The most significance was observed at a real PTSE with a GAC dose of 5g/L, having 54% and 96% of DOC removal by adsorption and bioadsorption, respectively. In addition, it was found that GAC adsorption equilibrium was successfully predicted by a hybrid Langmuir-Freundlich model whilst integrated linear driving force approximation (LDFA)+hybrid isotherm model could describe well the adsorption kinetics. Both adsorption isotherm and kinetic coefficients determined by these models will be useful to model the adsorption/bioadsorption process in DOC removal of BGAC filtration system.

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Urban water demand is influenced by a variety of factors such as climate change, population growth, socioeconomic conditions and policy issues. These variables are often correlated with each other, which may create a problem in building appropriate water demand forecasting model. Therefore, selection of the appropriate predictor variables is important for accurate prediction of future water demand. In this study, seven variable selection methods in the context of multiple linear regression analysis were examined in selecting the optimal predictor variable set for long-term residential water demand forecasting model development. These methods were (i) stepwise selection, (ii) backward elimination, (iii) forward selection, (iv) best model with residual mean square error criteria, (v) best model with the Akaike information criterion, (vi) best model with Mallow's C p criterion and (vii) principal component analysis (PCA). The results showed that different variable selection methods produced different multiple linear regression models with different sets of predictor variables. Moreover, the selection methods (i)-(vi) showed some irrational relationships between the water demand and the predictor variables due to the presence of a high degree of correlations among the predictor variables, whereas PCA showed promising results in avoiding these irrational behaviours and minimising multicollinearity problems.

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