No abstract

A waste load allocation model using linear programming has been developed for economic water quality management. A modified Qual2e model was used for water quality calculations and transfer coefficients were derived from the calculated water quality. This allocation model was applied to the heavily polluted Gyungan River, located in South Korea. For water quality management of the river, two scenarios were proposed. Scenario 1 proposed to minimise the total waste load reduction in the river basin. Scenario 2 proposed to minimise waste load reduction considering regional equity. Waste loads, which have to be reduced at each sub-basin and WWTP, were determined to meet the water quality goal of the river. Application results of the allocation model indicate that advanced treatment is required for most of the existing WWTPs in the river basin and construction of new WWTPs and capacity expansion of existing plants are necessary. Distribution characteristics of pollution sources and pollutant loads in the river basin was analysed using Arc/View GIS.

Abstract:Harmful cyanobacteria and their metabolites often contaminate drinking water resources, and effective control remains challenging. Here, we developed a physical algal pre-treatment method, the vertical weir curtain (VWC), to mitigate cyanobacteria and some of their metabolites (geosmin, 2-methylisoborneol (2-MIB), and microcystins) in situ and evaluated its performance in a raw water reservoir used for drinking water supply. The VWC was manufactured with two fibrous polypropylene mats (0% and 92% porosity) which were mounted to maintain a constant underwater depth. We installed the VWC to cover the entire epilimnion of the drinking water intake zone and monitored its efficiency during an algal bloom period (July-October 2015). Reduction rates were 40-59% for total algae, 60-75% for cyanobacteria, 23-55% for geosmin, 30-51% for 2-MIB, and 47-89% for microcystin-LR during the study period. Significant reductions were observed in the shallow layer of the water column (1-3 m water depth), particularly during August, when cyanobacterial density was the highest. The results indicate that the VWC can effectively mitigate harmful cyanobacteria and their metabolites when suitably applied, serving as a valuable reference for the algal reduction in raw drinking water resources.

This paper describes the prediction of flux behavior in an ultrafiltration (UF) membrane system using a Kalman neuro training (KNT) network model. The experimental data was obtained from operating a pilot plant of hollow fiber UF membrane with groundwater for 7 months. The network was trained using operating conditions such as inlet pressure, filtration duration, and feed water quality parameters including turbidity, temperature and UV254. Pre-processing of raw data allowed the normalized input data to be used in sigmoid activation functions. A neural network architecture was structured by modifying the number of hidden layers, neurons and learning iterations. The structure of KNT-neural network with 3 layers and 5 neurons allowed a good prediction of permeate flux by 0.997 of correlation coefficient during the learning phase. Also the validity of the designed model was evaluated with other experimental data not used during the training phase and nonlinear flux behavior was accurately estimated with 0.999 of correlation coefficient and a lower error of prediction in the testing phase. This good flux prediction can provide preliminary criteria in membrane design and set up the proper cleaning cycle in membrane operation. The KNT-artificial neural network is also expected to predict the variation of transmembrane pressure during filtration cycles and can be applied to automation and control of full scale treatment plants.

The performance of enriched sludge augmented with the B21 strain of Alcaligenes defragrans was compared with that of enriched sludge, as well as with pure Alcaligenes defragrans B21, in the context of a sulfur-oxidizing denitrification (SOD) process. In synthetic wastewater treatment containing 100-1,000 mg NO3-N/L, the single strain-seeded system exhibited superior performance, featuring higher efficiency and a shorter startup period, provided nitrate loading rate was less than 0.2 kg NO3-N/m(3) per day. At nitrate loading rate of more than 0.5 kg NO3-N/m(3) per day, the bioaugmented sludge system showed higher resistance to shock loading than two other systems. However, no advantage of the bioaugmented system over the enriched sludge system without B21 strain was observed in overall efficiency of denitrification. Both the bioaugmented sludge and enriched sludge systems obtained stable denitrification performance of more than 80% at nitrate loading rate of up to 2 kg NO3-N/m(3) per day.