Journal of Water Process Engineering

Micellar enhanced ultrafiltration (MEUF) of mercury-contaminated wastewater: Experimental and artificial neural network modeling MEUF was applied efficiently to remove mercury (Hg) from simulated wastewater by using polyacrylonitrile membrane and sodium dodecyl sulfate (SDS) as surfactant. In this process leakage of surfactant monomer to permeate water causing a secondary pollution that was addressed by using MEUF followed by activated carbon fiber (MEUF-ACF) process. The effect of operating parameters including, concentration of Hg and pH of feed solution, molar ratio of SDS to Hg, and retentate pressure was explored to optimize MEUF process. Moreover, artificial neural network (ANN) model was proposed to predict Hg removal efficiency to optimize MEUF process without laborious and time-consuming experimental work. ANN model performance was evaluated on the basis of statistical values such as mean square error (MSE) and coefficient of determination (R2). The experimental results presented that optimum operating parameters were 10ppm of Hg concentration, pH 7.0, molar ratio of SDS to Hg 8:1 and retentate pressure was 1.5bar. MEUF results showed 95.75% and 50.91% removal of Hg and SDS, respectively, while 96.83% Hg and 97.15% of SDS rejection was achieved using MEUF-ACF. The statistical values of proposed ANN model presented high degree of agreement between experimental and predicted values (R2 was found greater than 95% for training, validation and testing dataset). MEUF-ACF can eradicate issue of secondary pollution and proposed ANN model can be a competitive, powerful and fast alternate to laborious experimental work for MEUF process optimization.

The worksheets attached are related to the data collected in Maryhill and Parelheiros wastewater treatment systems.

The worksheets attached referred to data collected in Maryhill and Parelheiros wastewater treatment systems.

Data elicited from experts on the failure and repair of components at water treatment plants.

Simulation of concentrations of nitrate, nitrite, dissolved oxygen, dissolved hydrogen, and biomass of denitrifying and nitrifying bacteria in long-term continuous treatment using a experimental scale aquifer

This manuscript provides new materials for the removal of micro-nutrient, namely, phosphorus, from water

The experimental data are grouped into tabs in the uploaded Excel file.Tabs 1-4 are the UV 478 nm absorption data for orange G degradation by various concentrations of persulphate activated by various forms of iron material. Tab 5 (PS-addition-mode) shows UV 478 absorption data for the various modes of adding persulphate. Tab 6 (SA) shows HPLC data for salicylic acid degradation by persulphate activated by steel wool. Tab 7 (1NA) and Tab 8 (Ph) contain HPLC data for 1-naphthylamine and phenol, respectively, degradation by persulphate activated by steel wool. Table 9 (TC-Ph-1NA) contain total carbon content analysis for phenol and 1-naphthylamine degradation. Tab 10 (PS-leftover-TC-reduction) contain estimations of residual persulphates after degradation experiments for phenol, 1-naphthylamine, and salicylic acids. The total carbon content analysis for salicylic acid degradation is also presented in this tab.

Original data

This dataset contains all the raw and processed data for the research in the paper: Recovery of microbial biomass and purification performance after scraping of full-scale slow sand filters. This work investigates the response of the deeper layers to filter performance after Schmutzdecke scraping. The research is performed in an operating full-scale slow sand filters monitored for over four months.

This dataset contains all the raw and processed data for the research in the paper: Recovery of microbial biomass and purification performance after scraping of full-scale slow sand filters. This work investigates the response of the deeper layers to filter performance after Schmutzdecke scraping. The research is performed in an operating full-scale slow sand filters monitored for over four months.