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In this paper, the simultaneous optimization of process structure and operation conditions for the coking wastewater biological treatment process is carried out based on a superstructure-based model. The superstructure proposed covers current widely used biological treatment processes in a coking wastewater treatment plant. The activated sludge model no. 3 calibrated for coking wastewater and thickening factors is chosen to describe the biological processes and the secondary clarifier, respectively. The optimization problem is formulated as a mixed integer nonlinear programming model (MINLP), which is implemented and solved using the general algebraic modeling system (GAMS). For given influent coking wastewater flow rate and composition as input, the O/A/O process and A/O/O process are selected for the influent with COD concentration of 2000 and 3000 g/m3respectively. The results provide the valuable information for the development and operation of activated sludge processes for coking wastewater biological treatment and useful insights for policy makers as well.

In this work, fouling mechanisms of mixed foulants, that is, bovine serum albumin (BSA) and humic acid (HA), on nanofiltration (NF) under different factors, including pH, ionic strength, and Ca2+, were investigated by using atomic force microscopy (AFM). Considering that fouling behavior mainly appeared at the polyamide (PA) separation layer of NF membrane, AFM probes modified with PA which was the dominating material of effective separation layer, and coating [BSA-HA]nwere prepared. Fouling mechanisms of mixed foulants on the NF membrane were studied through measurements of the interfacial forces of [BSA-HA]n–PA membrane and [BSA-HA]n–[BSA-HA]n. Fouling experiments with [BSA-HA] nwere carried out using PA membrane, and the results indicated severe flux reduction occurring at low pH, thereby decreasing the ionic strength or adding Ca2+. The fouling behavior of NF membrane was determined by the interfacial forces (F/R) of [BSA-HA]n–PA and [BSA-HA] n–[BSA-HA]n, thus both forces were measured. Results showed that the forces of [BSA-HA] n–PA membrane were stronger than those of [BSA-HA]n–[BSA-HA]n. Moreover, both forces were enhanced with low pH, ionic strength and added Ca2+. Results verified that the F/R of [BSA-HA]n–PA membrane determined the cake formation occurrence time, and F/R of [BSA-HA]n–[BSA-HA]nmaintained a linear correlation with fouling degree.

AbstractThis work aimed to determine the effect of organic foulants and fouling behavior on separation of multi-ions for nanofiltration (NF) membrane. Three model organic foulants (bovine serum albumin, lysozyme, and humic acid) were selected and studied in depth. The rejections of mono- and di-anions by virgin membrane varied with different organic foulants because of foulant-enhanced co-ion competition. Subsequent multi-ions separations by fouled membranes were different due to the zeta potential and structure of the fouling cake. In contrast to the single protein system, Cl−and rejection was reduced by HA fouled membrane. However, with added proteins in the HA solution, mixed systems experienced decreasing Cl−rejection and increasing rejection. In addition, solution chemistry (solution pH, ionic strength, and Ca2+) had marked effect on the cake layer composition, likely due to the changing interaction between foulant and foulant, as well as between foulant and NF membrane. The suitable foulant cake structure could result enhanced in multi-ions separation. The synergistic mechanisms of organic foulants and cake were analyzed by calculated cake hydraulic resistance and zeta potential of fouled membranes.

This work aimed to determine the effect of organic foulants and fouling behavior on separation of multi-ions for nanofiltration (NF) membrane. Three model organic foulants (bovine serum albumin, lysozyme, and humic acid) were selected and studied in depth. The rejections of mono- and di-anions by virgin membrane varied with different organic foulants because of foulant-enhanced co-ion competition. Subsequent multi-ions separations by fouled membranes were different due to the zeta potential and structure of the fouling cake. In contrast to the single protein system, Cl−and SO42-rejection was reduced by HA fouled membrane. However, with added proteins in the HA solution, mixed systems experienced decreasing Cl−rejection and increasing SO42-rejection. In addition, solution chemistry (solution pH, ionic strength, and Ca2+) had marked effect on the cake layer composition, likely due to the changing interaction between foulant and foulant, as well as between foulant and NF membrane. The suitable foulant cake structure could result enhanced in multi-ions separation. The synergistic mechanisms of organic foulants and cake were analyzed by calculated cake hydraulic resistance and zeta potential of fouled membranes.

AbstractThe fouling behavior of nanofiltration (NF) membranes by proteins bovine serum album (BSA), lysozyme (LYS), and humic acids (HA), as well as their mixtures was studied in a cross flow bench-scale cell. The similarities and differences of the effects of pH on single and mixed organic foulant systems were compared by analyzing the size distribution and zeta potential of foulants in the feed, and in conjunction with the characterization of fouled NF membranes, respectively. Results showed that the fouling behaviors of both single and the mixed systems were significantly correlative with solution pH, moreover BSA/LYS combined with HA appeared more aggravated fouling behavior than single foulant within the test pH range. For solution pH, this can affect interactions of foulants–foulants and foulants–membrane by changing zeta potential of them. Also, therefore, zeta potential value could be used to predict the fouling behavior of NF. Considering the experimental results, more severe fouling behavior by not only single (HA, BSA, and LYS), but mixed systems (BSA + HA and LYS + HA) occurred at acidic and neutral solution pH.

The fouling behavior of nanofiltration (NF) membranes by proteins bovine serum album (BSA), lysozyme (LYS), and humic acids (HA), as well as their mixtures was studied in a cross flow bench-scale cell. The similarities and differences of the effects of pH on single and mixed organic foulant systems were compared by analyzing the size distribution and zeta potential of foulants in the feed, and in conjunction with the characterization of fouled NF membranes, respectively. Results showed that the fouling behaviors of both single and the mixed systems were significantly correlative with solution pH, moreover BSA/LYS combined with HA appeared more aggravated fouling behavior than single foulant within the test pH range. For solution pH, this can affect interactions of foulants–foulants and foulants–membrane by changing zeta potential of them. Also, therefore, zeta potential value could be used to predict the fouling behavior of NF. Considering the experimental results, more severe fouling behavior by not only single (HA, BSA, and LYS), but mixed systems (BSA + HA and LYS + HA) occurred at acidic and neutral solution pH.

AbstractExperiments were conducted to investigate the concentrations of PAHs and their adsorption characteristics by activated sludge in this study. Influence of sorption time, sludge concentration, and pH on PAHs adsorption was evaluated. Furthermore, the isotherms, kinetics of adsorption, and potential risk were also explored. Experimental results show that concentrations of 16 PAHs in coking wastewater sludge were 581.600–2201.633 μg/g, both Linear and Freundlich adsorption models were able to adequately describe the adsorption equilibriums of PAHs onto activated sludge. The adsorption process closely followed the pseudo-second-order kinetic model. The adsorption efficiencies of PAHs depended on the activated sludge concentration and sorption time, while that was not affected by pH. Hydrophobic interaction, distribution, and chemisorption were mainly responsible for the adsorption of PAHs, while the surface area and pore volume of activated sludge had little contribution. Results also indicate that the order of carcinogenic health risk was Benzo[a]pyrene > Dibenzo[a,h]anthracene > Benzo[b]fluoranthene > Indeno[1,2,3-cd]pyrene > Benzo[a]anthracene > Benzo[k]fluoranthene.

Experiments were conducted to investigate the concentrations of PAHs and their adsorption characteristics by activated sludge in this study. Influence of sorption time, sludge concentration, and pH on PAHs adsorption was evaluated. Furthermore, the isotherms, kinetics of adsorption, and potential risk were also explored. Experimental results show that concentrations of 16 PAHs in coking wastewater sludge were 581.600–2201.633 μg/g, both Linear and Freundlich adsorption models were able to adequately describe the adsorption equilibriums of PAHs onto activated sludge. The adsorption process closely followed the pseudo-second-order kinetic model. The adsorption efficiencies of PAHs depended on the activated sludge concentration and sorption time, while that was not affected by pH. Hydrophobic interaction, distribution, and chemisorption were mainly responsible for the adsorption of PAHs, while the surface area and pore volume of activated sludge had little contribution. Results also indicate that the order of carcinogenic health risk was Benzo[a]pyrene > Dibenzo[a,h]anthracene > Benzo[b]fluoranthene > Indeno[1,2,3-cd]pyrene > Benzo[a]anthracene > Benzo[k]fluoranthene.

Coking wastewater is one of the most toxic industrial effluents since it contains high concentration of toxic organic compounds. Biological treatments are widely applied in coking wastewater treatment, pollutants can be degraded completely due to the synergistic effect of the community composition. In this study, the community structure and degradation of organic compounds of a full-scale coking wastewater treatment plant with anaerobic, anoxic, and oxic process (A1/A2/O) were studied. GC-MS results showed that phenols, indole, quinoline and pyridine, accounting for 61.70, 13.63, 7.71 and, 2.30%, respectively, were the main organic pollutants in the raw coking wastewater. Those pollutants were degraded gradually during the A1/A2/O bioprocess, respectively. High throughput pyrosequencing was applied to investigate the bacterial community, the sequences could be affiliated to 21 phylogenetic groups, including Proteobacteria, Chloroflexi, Bacteroidetes, Planctomycetes, Synergistetes, Chlorobi, Acidobacteria, Nitrospira, Firmicutes, and Actinobacteria. Diversity and richness indexes, venn analysis and principal component analysis indicated that the diversity and abundance of species in three samples were different. The abundance of the phylum Proteobacteriaaccounted for 84.64% (A1), 62.73% (A2), and 83.24% (O) of the total reads, respectively. The corresponding most dominant orders in three samples were Pseudomonadales(A1), Syntrophobacterales(A2), and Burkholderiales(O), respectively. While genus Pseudomonas, Desulfoglaeba, and Diaphorobacterwas the dominant bacterium in three samples, respectively.

AbstractCoking wastewater is one of the most toxic industrial effluents since it contains high concentration of toxic organic compounds. Biological treatments are widely applied in coking wastewater treatment, pollutants can be degraded completely due to the synergistic effect of the community composition. In this study, the community structure and degradation of organic compounds of a full-scale coking wastewater treatment plant with anaerobic, anoxic, and oxic process (A1/A2/O) were studied. GC-MS results showed that phenols, indole, quinoline and pyridine, accounting for 61.70, 13.63, 7.71 and, 2.30%, respectively, were the main organic pollutants in the raw coking wastewater. Those pollutants were degraded gradually during the A1/A2/O bioprocess, respectively. High throughput pyrosequencing was applied to investigate the bacterial community, the sequences could be affiliated to 21 phylogenetic groups, including Proteobacteria, Chloroflexi, Bacteroidetes, Planctomycetes, Synergistetes, Chlorobi, Acidobacteria, Nitrospira, Firmicutes, and Actinobacteria. Diversity and richness indexes, venn analysis and principal component analysis indicated that the diversity and abundance of species in three samples were different. The abundance of the phylum Proteobacteriaaccounted for 84.64% (A1), 62.73% (A2), and 83.24% (O) of the total reads, respectively. The corresponding most dominant orders in three samples were Pseudomonadales(A1), Syntrophobacterales(A2), and Burkholderiales(O), respectively. While genus Pseudomonas, Desulfoglaeba, and Diaphorobacterwas the dominant bacterium in three samples, respectively.

Abstract: A large volume of bacterially produced precipitates are generated during the bio-treatment of heavy metal wastewater. The composition of the bacterially produced precipitates and its effects on sulfate reducing bacteria (SRB) in copper-containing waste stream were evaluated in this study. The elemental composition of the microbial precipitate was studied using electrodispersive X-ray spectroscopy (EDX), and it was found that the ratio of S:Cu was 1.12. Combining with the results of copper distribution in the SRB metabolism culture, which was analyzed by the sequential extraction procedure, copper in the precipitates was determined as covellite (CuS). The bacterially produced precipitates caused a decrease of the sulfate reduction rate, and the more precipitates were generated, the lower the sulfate reduction rate was. The particle sizes of bacterially generated covellite were ranging from 0.03 to 2 μm by particles size distribution (PSD) analysis, which was smaller than that of the SRB cells. Transmission electron microscopy (TEM) analysis showed that the microbial covellite was deposited on the surface of the cell. The effects of the microbial precipitate on SRB metabolism were found to be weakened by increasing the precipitation time and adding microbial polymeric substances in later experiments. These results provided direct evidence that the SRB activity was inhibited by the bacterially produced covellite, which enveloped the bacterium and thus affected the metabolism of SRB on mass transfer.