Comparisons of grey and neural network prediction of industrial park wastewater effluent using influent quality and online monitoring parameters.

In this study, Grey model (GM) and artificial neural network (ANN) were employed to predict suspended solids (SSeff) and chemical oxygen demand (CODeff) in the effluent from a wastewater treatment plant in industrial park of Taiwan. When constructing model or predicting, the influent quality or online monitoring parameters were adopted as the input variables. ANN was also adopted for comparison. The results indicated that the minimum MAPEs of 16.13 and 9.85% for SSeff and CODeff could be achieved using GMs when online monitoring parameters were taken as the input variables. Although a good fitness could be achieved using ANN, they required a large quantity of data. Contrarily, GM only required a small amount of data (at least four data) and the prediction results were even better than those of ANN. Therefore, GM could be applied successfully in predicting effluent when the information was not sufficient. The results also indicated that these simple online monitoring parameters could be applied on prediction of effluent quality well.

Biofilm bacteria inactivation by citric acid and resuspension evaluations for drinking water production systems.

The study investigates the inactivation of biofilm bacteria colonized on the surface of polyvinyl chloride (PVC) pipes delivering either groundwater or treated wastewater. It does so using a citric acid (C6H8O7) solution. The results of the study showed that the optimal conditions of the biofilm bacteria inactivation were over 10,000 mg/L citric acid concentration and 60 minutes of contact time at least. Under these conditions, the removal efficiency could reach above 99.999% for heterotrophic plate count (HPC) bacteria and 99.95% for coliform bacteria. The study also showed that the biofilm bacteria were the major sources of planktonic bacteria resuspended into water purified by drinking water production systems (DWPS).