[Purification Efficiency and Mechanism of Integrated Al Salt Floc-ultrafiltration Membrane Process].

Owing to the small land use and high pollutant removal efficiency, the integrated ultrafiltration (UF) membrane process has been gradually applied in water treatment. However, not only would the membrane surface be damaged by the commonly used granular adsorbents over time, but these types of adsorbents are expensive, such as nano zerovalent iron, carbon nanotubes, etc. To overcome these disadvantages, the loose Al-based flocs were directly injected into the membrane tank in the presence of humic acid and Miyun reservoir water. Results showed that severe membrane fouling was induced by HA alone, and the transmembrane pressure (TMP) significantly increased to 76.4 kPa after 12 d of operation. However, it dramatically decreased to 10.1 kPa after washing with tap water on day 13, indicating that the cake layer was the main fouling mechanism. The average HA removal efficiency was only 23.3% during filtration. In addition, the performance of the integrated flocs-membrane process could be influenced by floc dosage, injection frequency, and solution pH. The integrated membrane performed well with flocs (43.2 mmol·L-1) continuously injected at pH 6.0 (aeration rate at 0.3 L·min-1). The corresponding TMP only increased to 19.5 kPa after running for 12 d, which decreased to 5.6 kPa after washing with tap water on day 13. The average HA removal efficiency increased to 61.2%. Additionally, serious membrane fouling was also induced by Miyun reservoir alone. The TMP increased to 38.0 kPa on day 12, while it decreased to 3.8 kPa after washing with tap water on day 13. The cake layer was also the main fouling mechanism, and the average pollutant removal efficiency was only 7.5%. With floc continuously injected, however, the TMP only increased to 6.1 kPa on day 12. After washing with tap water, the TMP decreased to 2.3 kPa on day 13, and the average pollutant removal efficiency was as high as 58.6%. Based on the excellent membrane performance, the integrated membrane process exhibited potential application in drinking water treatment.

[Removal Efficiency and Mechanism of Removal by Humic Acid of the Integrated Floc-ultrafiltration Process].

In recent years, the integrated ultrafiltration (UF) membrane process has been widely used due to its high removal efficiency, slight membrane fouling, and small land use. However, a number of problems gradually occurred regarding the integrated UF process caused by the granular adsorbents used, such as powdered activated carbon, carbon nano-tube, nanoscale zerovalent iron, etc. Severe membrane surface damage was easily caused by these granular adsorbents after a long running time, and the cost of most adsorbents was very high. In this study, to effectively overcome these problems, cheap and loose aluminum hydrolyzed flocs were directly injected into the membrane tank in the presence of humic acid (HA), with the aim of investigating the removal efficiency of HA and the corresponding membrane behavior. The results showed that the removal efficiency of HA could be influenced by aeration mode, floc injection frequency, and floc dosage. Compared with intermittent aeration and one-time injection, a loose "protection membrane" layer was formed with continuous aeration and batch injections. Therefore, HA molecules were largely removed, leading to the dramatic alleviation of membrane fouling. The transmembrane pressure significantly increased to 74.8 kPa in the absence of flocs after running for 5 days, but that only increased by 6.3 kPa with continuous aeration and an injection frequency of once every 2 d (each addition consisted of 5.4 mmol·L-1 flocs) after running for 8 days. The removal efficiency of HA was 73.3% (8 d), which was much higher than in the absence of flocs (5 d, 32.1%). Additionally, only a few HA molecules were adsorbed onto the membrane pores with the batch injections, and a loose cake layer was the main fouling mechanism. With higher dosages of flocs injected each time, the average membrane pore diameter was larger after washing. Based on this excellent performance, this floc-integrated UF membrane technology indeed shows large application potential in water treatment.

[Community Structure and Activity Analysis of the Nitrifiers in Raw Sewage of Wastewater Treatment Plants].

The communities and activity of nitrifiers collected from the raw sewage of the 2nd and 3rd wastewater treatment plants (WWTP) in Xi'an were investigated. FISH results indicated that the (AOB+NOB)/EUB percentages were (5.35±2.1)% and(6.0±2.8)% in the 2nd and 3rd WWTP, respectively. The dominant AOB was Nitrosomonas europaea/Nitrosococcus mobilis lineage and the dominant NOB was Nitrospira, the sub-dominant NOB was Nitrobacter, and coexisted with Nitrococcus, Nitrospina. Respirometric assays showed that the influent nitrifiers were active following a 2-16 hour period of metabolic induction. The ammonium utilized rate was (0.32±0.12) mg·(L·h)-1 and (0.43±0.17) mg·(L·h)-1, nitrite utilized rate was (0.71±0.18) mg·(L·h)-1 and (0.58±0.27) mg·(L·h)-1 for nitrifers in raw sewage fed to the 2nd and 3rd WWTP, respectively. Therefore, nitrifiers were present and active in the raw sewage, and played the role of natural continuous seeding in the activated sludge system. Based on the nitrification activity, the estimated continuous seeding intensity of AOB and NOB was 0.08-0.09 g·(g·d)-1 and 0.11-0.24 g·(g·d)-1, respectively.

[Classification of plastics with laser-induced breakdown spectroscopy based on principal component analysis and artificial neural network model].

The classification of seven kinds of plastic (ABS, PET, PP, PS, PVC, HDPE and PMMA) with the laser-induced breakdown spectroscopy based on artificial neural network model was investigated in the present paper. One hundred seventy LIBS spectra for each type of plastic were collected. Firstly, all 1 190 plastics LIBS spectra were studied with principal component analysis. The first five principal components (PC) totally explain 78.4% of the original spectrum information. Therefore, the scores of five PCs of 130 LIBS spectra for each kind of plastic were chosen as the training set to build a back-propagation artificial network model. And the other 40 LIBS spectra of each sample were used as the testing set for the trained model. The classification accuracy was 97.5%. Experimental results demonstrate that plastics can be classified by using principal component analysis and artificial neural network (BP) method.