Performances of PTFE and PVDF membranes in achieving the discharge limit of mixed anodic oxidation coating wastewaters treated by membrane distillation.

The mixed wastewater generated by anodic oxidation coating facilities contains high levels of various contaminants, including iron, aluminum, conductivity, chemical oxygen demand (COD), and sulfate. In this study, the effectiveness of the membrane distillation (MD) process using polytetrafluoroethylene (PTFE) and polyvinylidene fluoride (PVDF) membranes was investigated to treat mixed wastewater from an anodized coating factory. The results indicate that both hydrophobic membranes effectively removed targeted contaminants. However, the PTFE membrane achieved higher removal efficiencies, with over 99% removal of sulfate, conductivity, iron, and aluminum, 85.7% of COD, and 86% of total organic carbon (TOC). In contrast, the PVDF membrane exhibited a significant decline in removal efficiency as the temperature increased and performed well only at lower feed temperatures. The PTFE membranes outperformed the PVDF membranes in treating chemically intensive anodic oxidation wastewaters. This superiority can be attributed to the PTFE membrane's morphology and structure, which are less influenced by feed water temperature and chemicals. Additionally, its slippery surface imparts anti-adhesion properties, effectively preventing membrane fouling, and maintaining the treated water quality and flux for longer operation time.

Removal of micropollutants from Sakarya River water by ozone and membrane processes.

The removal of some pollutants in the Sakarya River was investigated in this study. Sakarya River located in Turkey flows from the northeast of Afyonkarahisar City to the Black Sea. Nineteen different micropollutants including trihalomethanes (THMs), haloacetic acids (HAAs), endocrine disrupting compound (EDC) and pharmaceuticals personal care product (PPCP) groups, and water quality parameters such as dissolved organic carbon (DOC), ultraviolet absorbance at 254 nm wavelength (UV254), hardness, and conductivity values were examined. To remove the micropollutants and improve the water quality, the treatment was performed with ozone, microfiltration (MF), and ultra-filtration (UF) membranes. The highest treatment efficiency was obtained with 1 mg/L ozone dosage and UP005 UF membrane. The trihalomethan formation potential (THMFP) and haloacetic acid formation potential (HAAFP) decreased with ozone + membrane at a concentration of 79 and 75%, respectively. After the treatment with ozone + membrane, the concentration of the micropollutants in the EDC and PPCP group remained below the detection limit. It was found that by using only membrane and only ozone, the maximum DOC removal efficiency achieved was 46 and 18%, respectively; and with ozone + membrane, this efficiency increased up to 82%. The results from the High-Pressure Size Exclusion Chromatography (HPSEC) analyses pointed that the substances with high molecular weight were converted into substances with low molecular weight after the treatment. The Fourier Transform Infrared (FTIR) analysis results showed that the aromatic and aliphatic functional groups in water changed after the treatment with ozone and that the peak values decreased more after the ozone + membrane treatment.

Harvesting microalgal biomass using crossflow membrane filtration: critical flux, filtration performance, and fouling characterization.

The purpose of this study was to investigate the efficient harvesting of microalgal biomass through crossflow membrane filtration. The microalgal biomass harvesting experiments were performed using one microfiltration membrane (pore size: 0.2 µm, made from polyvinylidene fluoride) and three ultrafiltration membranes (molecular weight cut-off: 150, 50, and 30 kDa, made from polyethersulfone, hydrophilic polyethersulfone, and regenerated cellulose, respectively). Initially, to minimize membrane fouling caused by microalgal cells, experiments with the objective of determining the critical flux were performed. Based on the critical flux calculations, the best performing membrane was confirmed to be the UH050 membrane, produced from hydrophilic polyethersulfone material. Furthermore, we also evaluated the effect of transmembrane pressure (TMP) and crossflow velocity (CFV) on filtration flux. It was observed that membrane fouling was affected not only by the membrane characteristics, but also by the TMP and CFV. In all the membranes, it was observed that increasing CFV was associated with increasing filtration flux, independent of the TMP.

Case study on prediction of remaining methane potential of landfilled municipal solid waste by statistical analysis of waste composition data.

Main objective of this study was to develop a statistical model for easier and faster Biochemical Methane Potential (BMP) prediction of landfilled municipal solid waste by analyzing waste composition of excavated samples from 12 sampling points and three waste depths representing different landfilling ages of closed and active sections of a sanitary landfill site located in Istanbul, Turkey. Results of Principal Component Analysis (PCA) were used as a decision support tool to evaluation and describe the waste composition variables. Four principal component were extracted describing 76% of data set variance. The most effective components were determined as PCB, PO, T, D, W, FM, moisture and BMP for the data set. Multiple Linear Regression (MLR) models were built by original compositional data and transformed data to determine differences. It was observed that even residual plots were better for transformed data the R(2) and Adjusted R(2) values were not improved significantly. The best preliminary BMP prediction models consisted of D, W, T and FM waste fractions for both versions of regressions. Adjusted R(2) values of the raw and transformed models were determined as 0.69 and 0.57, respectively.

Electricity generating capacity and performance deterioration of a microbial fuel cell fed with beer brewery wastewater.

This study focused on using beer brewery wastewater (BBW) to evaluate membrane concentrate disposal and production of electricity in microbial fuel cells. In the membrane treatment of BBW, the membrane permeate concentration was 570 ± 30 mg/L corresponding to a chemical oxygen demand (COD) removal efficiency of 75 ± 5%, and the flux values changed between 160 and 40 L/m(2)-h for all membrane runs. For electricity production from membrane concentrate, the highest current density in the microbial fuel cell (MFC) was observed to be 1950 mA/m(2) according to electrode surface area with 36% COD removal efficiency and 2.48% CE with 60% BBW membrane concentrate. The morphologies of the cation exchange membrane and the MFC deterioration were studied using a scanning electron microscope (SEM), attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy, differential scanning calorimetry (DSC), and thermal gravimetric analysis (TGA). A decrease in the thermal stability of the sulfonate (-SO3H) groups was demonstrated and morphological changes were detected in the SEM analysis.

Natural organic matter removal and fouling in a low pressure hybrid membrane systems.

The objective of this study was to investigate powdered activated carbon (PAC) contribution to natural organic matter (NOM) removal by a submerged MF and UF hybrid systems. It was found that filtration of surface waters by a bare MF and UF membranes removed negligible TOC; by contrast, significant amounts of TOC were removed when daily added PAC particles were predeposited on the membrane surfaces. These results support the assumption that the membranes surface properties and PAC layer structure might have considerably influential factor on NOM removal. Moreover, it was concluded that the dominant removal mechanism of hybrid membrane system is adsorption of NOM within PAC layer rather than size exclusion of NOM by both of membrane pores. Transmembrane pressure (TMP) increases with PAC membrane systems support the view that PAC adsorption pretreatment will not prevent the development of membrane pressure; on the contrary, PAC particles themselves caused membrane fouling by blocking the entrance of pores of MF and UF membranes. Although all three source waters have similar HPI content, it appears that the PAC interaction with the entrance of membrane pores was responsible for offsetting the NOM fractional effects on membrane fouling for these source waters.

Molecular weight distribution of a full-scale landfill leachate treatment by membrane bioreactor and nanofiltration membrane.

In this study, Molecular weight (MW) distributions of a full-scale landfill leachate treatment plant consisting of membrane bioreactor (MBR) and nanofiltration (NF) membrane were investigated. The leachate was sampled from the equalization tank, and effluents of MBR and NF membrane in the landfill leachate treatment plant. Parameters of COD, TOC, TKN, NH4(+)-N and UV(254, 280 and 320) absorbance were analyzed to evaluate both the removal performance of the plant and MW distributions. MW distribution of samples were determined by ultrafiltration (UF) (100 kDa, 10 kDa, 5 kDa, 1 kDa and 500 Da) membranes. The results indicated that organic matter of one third percent is particulate or colloidal form and almost half of the organic fraction has a lower MW than 500 Da. In addition, organic matter had hydrophilic character. Most part of TKN was>500 Da with the corresponding rate of 92%. Further, UV absorbance of raw leachate (RW) decreased 85% after 500 Da.

Electricity generation from young landfill leachate in a microbial fuel cell with a new electrode material.

This study aims at evaluating the performance of a two-chambered continuously fed microbial fuel cell with new Ti-TiO2 electrodes for bioelectricity generation from young landfill leachate at varying strength of wastewater (1-50 COD g/L) and hydraulic retention time (HRT, 0.25-2 days). The COD removal efficiency in the MFC increased with time and reached 45 % at full-strength leachate (50 g/L COD) feeding. The current generation increased with increasing leachate strength and decreasing HRT up to organic loading rate of 100 g COD/L/day. The maximum current density throughout the study was 11 A/m² at HRT of 0.5 day and organic loading rate of 67 g COD/L/day. Coulombic efficiency (CE) decreased from 57 % at feed COD concentration of 1 g/L to less than 1 % when feed COD concentration was 50 g/L. Increase in OLR resulted in increase in power output but decrease in CE.

Aerobic composting leachate treatment by the combination of membrane processes.

The main product of the conversion process of organic wastes to a useful organic fertilizer, known as compost, has gained an increasing interest in management of organic wastes recently. One of the main problems arising in the composting facilities is the high organic loaded leachate. In this study, a treatability experiment for composting leachate from a full-scale composting facility was carried out with the combination of membrane processes. The parameters such as chemical oxygen demand, total organic carbon, Cl⁻ and NH4⁺ were analysed to evaluate the membrane treatment performances of single and combined membrane systems consisting centrifuge, cartridge filter, ultrafiltration and nanofiltration membranes. The removal efficiencies of all pollutants were observed between 4.4 and 98%. The highest removal efficiencies were observed with the nanofiltration membrane (NF90) having a lower molecular weight cut-off than the others used in this study. It was observed that the effluent of NF90 membrane did not exceed the allowed maximum COD value.

Adaptive neuro-fuzzy modeling of head loss in iron removal with rapid sand filtration.

Breakthrough and terminal head loss are the main parameters that determine the performance of rapid sand filters. Carman-Kozeny and Ergun equations can be applied to estimate head loss, but can only be applied to clean filter beds. Elaborated models are needed to predict head loss in dirty filters. In this study, a neuro-fuzzy modeling approach was proposed to estimate head loss in dirty filters. Hydraulic loading rate, influent iron concentration, bed porosity, and operating time were selected as input variables. Various types of membership functions were tried. Two rule-base generation methods--subtractive clustering and grid partition--were used for a first-order, Sugeno-type inference system. Using 11 rules and the grid-partition method, an optimum rule base set was developed and the lowest root mean squared error (RMSE) was obtained. Tap and deionized waters were used to obtain testing RMSE values of 1.094 and 0.926, respectively. The fit between experimental results and model outputs was excellent, with the multiple correlation coefficient (R2) greater than 0.99. Based on these findings, the authors conclude that neuro-fuzzy modeling may successfully be used to predict filter head loss.

Removal of trihalomethanes from drinking water by nanofiltration membranes.

Chlorine reacts with the natural organic matter (NOM) in waters and forms disinfection by-products (DBP). Major of these by-products are trihalomethanes (THM) and haloacetic acids (HAA). They have been known to cause cancer and other toxic effects to human beings. This study determined the removal efficiencies of THM by nanofiltration (NF) techniques with NF200 and DS5 membrane. The rejection of this chlorination by-products was studied at various feed concentration by changing transmembrane pressure. Experimental results indicated that in general increasing operating pressure produces a higher flux but does not have a significant effect on THM rejection. On the other hand, increasing the feed concentration produces a little change in the overall flux and rejection capacity. NF200 membrane removed more THM than DS5 membrane. The higher removal efficiency of dibromochloromethane (DBCM) was attributed to brominating characteristics (higher molecular weight (MW) and molecular size). As a consequence, the results of this study suggest that the NF membrane process is one of the best available technologies for removing THM compounds.

Adaptive neuro-fuzzy modelling of anaerobic digestion of primary sedimentation sludge.

Modelling of anaerobic digestion systems is difficult because their performance is complex and varies significantly with influent characteristics and operational conditions. In this study, Adaptive Neuro-Fuzzy Inference System (ANFIS) were used for modelling of anaerobic digestion system of primary sludge of Kayseri municipal WasteWater Treatment Plant (WWTP). Effluent Volatile Solid (VS) and methane yield were predicted by the ANFIS. Two stage models were performed. In the first stage, effluent VS concentration was predicted using pH, VS concentration, flowrate of pre-thickened sludge and temperature of the influent as input parameters. In the second stage, effluent VS concentration in addition to first stage input parameters were used as input parameters to predict methane yield. The low Root Mean Square Error (RMSE) and high Index of agreement (IA) values were obtained with subtractive clustering method of a first order Sugeno type inference. The model performance was evaluated with statistical parameters. According to statistical evaluations, the models satisfactorily predict effluent VS concentration and methane yield.