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Keywords = AAO-MBR process

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16 pages, 3161 KiB  
Article
Multi-Objective Optimization Based on Simulation Integrated Pareto Analysis to Achieve Low-Carbon and Economical Operation of a Wastewater Treatment Plant
by Jianbo Liao, Shuang Li, Yihong Liu, Siyuan Mao, Tuo Tian, Xueyan Ma, Bing Li and Yong Qiu
Water 2024, 16(7), 995; https://doi.org/10.3390/w16070995 - 29 Mar 2024
Cited by 2 | Viewed by 1625
Abstract
It is essential to reduce carbon emissions in wastewater treatment plants (WWTPs) to achieve carbon neutrality in society. However, current optimization of WWTPs prioritizes the operation cost index (OCI) and effluent quality index (EQI) over greenhouse gas (GHG) emissions. This study aims to [...] Read more.
It is essential to reduce carbon emissions in wastewater treatment plants (WWTPs) to achieve carbon neutrality in society. However, current optimization of WWTPs prioritizes the operation cost index (OCI) and effluent quality index (EQI) over greenhouse gas (GHG) emissions. This study aims to conduct a multi-objective optimization of a WWTP, considering GHG emissions, EQI, and OCI. The anaerobic-anoxic-oxic integrated membrane bioreactor (AAO-MBR) process in an actual WWTP was selected as a typical case, tens of thousands of scenarios with combinations of six operational parameters (dissolved oxygen (DO), external carbon resource (ECR), poly aluminum chloride (PAC), internal reflux ratio (IRR), external reflux ratio (ERR), and sludge discharge (SD)) were simulated by GPS-X software (Hydromantics 8.0.1). It was shown that ECR has the greatest impact on optimization objectives. In the optimal scenario, the main parameters of ATDO, MTDO, IRR, and ERR were 0.1 mg/L, 4 mg/L, 50%, and 100%, respectively. The EQI, OCI, and GHG of the best scenario were 0.046 kg/m3, 0.27 ¥/m3, and 0.51 kgCO2/m3, which were 2.1%, 72.2%, and 34.6% better than the current situation of the case WWTP, respectively. This study provides an effective method for realizing low-carbon and economical operation of WWTPs. Full article
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<p>The research framework of the multi-objective optimization.</p>
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<p>Generalized model of the case WWTP established in GPX-S software.</p>
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<p>The effect of simulated value changes in each operational parameter on EQI for the case plant. (<b>a</b>) ATDO; (<b>b</b>) PAC; (<b>c</b>) ECR; (<b>d</b>) IRR; (<b>e</b>) ERR; (<b>f</b>) SD, and the values of the baseline scenario is denoted by (B) in the abscissa.</p>
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<p>The effect of simulated value changes in each operational parameter on OCI for the case plant. (<b>a</b>) ATDO; (<b>b</b>) PAC; (<b>c</b>) ECR; (<b>d</b>) IRR; (<b>e</b>) ERR; (<b>f</b>) SD, and the values of the baseline scenario is denoted by (B) in the abscissa.</p>
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<p>The effect of simulated value changes in each operational parameter on GHGI for the case plant. (<b>a</b>) ATDO; (<b>b</b>) PAC; (<b>c</b>) ECR; (<b>d</b>) IRR; (<b>e</b>) ERR; (<b>f</b>) SD, and the values of the baseline scenario is denoted by (B) in the abscissa.</p>
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<p>Scenarios with different Pareto levels and distribution of optimal scenario indicators. (<b>a</b>) Blue, orange, and yellow dots indicated the scenarios with Pareto levels of 1, 50, and 127, respectively; the relationship of OCI and EQI (<b>b</b>), GHG and EQI (<b>c</b>), and OCI and GHG (<b>d</b>) for the “Pareto-optimal” scenarios.</p>
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15 pages, 2977 KiB  
Article
Spatial Distribution and Risk Assessment of Antibiotics in 15 Pharmaceutical Plants in the Pearl River Delta
by Yuanfei Liu, Xiaoxia Shi, Xiaoxia Chen, Ping Ding, Lijuan Zhang, Jian Yang, Jun Pan, Yunjiang Yu, Jinhua Wu and Guocheng Hu
Toxics 2023, 11(4), 382; https://doi.org/10.3390/toxics11040382 - 17 Apr 2023
Cited by 3 | Viewed by 1893
Abstract
Pharmaceutical plants are an essential source of antibiotics emitted into the aqueous environment. The monitoring of target antibiotics in pharmaceutical plants through various regions is vital to optimize contaminant release. The occurrence, distribution, removal, and ecological risk of 30 kinds of selected antibiotics [...] Read more.
Pharmaceutical plants are an essential source of antibiotics emitted into the aqueous environment. The monitoring of target antibiotics in pharmaceutical plants through various regions is vital to optimize contaminant release. The occurrence, distribution, removal, and ecological risk of 30 kinds of selected antibiotics in 15 pharmaceutical plants in the Pearl River Delta (PRD) were investigated in this study. Lincomycin (LIN) showed the highest concentration (up to 56,258.3 ng/L) in the pharmaceutical plant influents from Zhongshan city. Norfloxacin (NFX) showed a higher detection frequency than other antibiotics. In addition, the spatial distribution of antibiotics in pharmaceutical plants showed significant differences, with higher concentrations of total antibiotics found in pharmaceutical plant influents in Shenzhen City than those of different regions in PRD. The treatment processes adopted by pharmaceutical plants were commonly ineffective in removing antibiotics, with only 26.7% of antibiotics being effectively removed (average removal greater than 70%), while 55.6% of antibiotics had removal rates of below 60%. The anaerobic/anoxic/oxic (AAO)-membrane bioreactor (MBR) combined process exhibited better treatment performance than the single treatment process. Sulfamethoxazole (SMX), ofloxacin (OFL), erythromycin-H2O (ETM-H2O), sulfadiazine (SDZ), sulfamethazine (SMZ), norfloxacin (NFX), and ciprofloxacin (CIP) in pharmaceutical plant effluents posed high or moderate ecological risk and deserve particular attention. Full article
(This article belongs to the Section Emerging Contaminants)
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<p>Concentration and distribution of antibiotics in influents samples from 12 pharmaceutical plants of China. (<b>a</b>): map of sampling locations. (<b>b</b>): the concentration of antibiotics in influent of Guangzhou pharmaceutical plants. (<b>c</b>): the concentration of antibiotics in influent of Foshan pharmaceutical plants. (<b>d</b>): the concentration of antibiotics in influent of Zhongshan pharmaceutical plants. (<b>e</b>): the concentration of antibiotics in influent of Shenzhen pharmaceutical plants. (<b>f</b>): the concentration of antibiotics in influent of Zhuhai pharmaceutical plants. (<b>g</b>): the total antibiotics concentration in pharmaceutical plants influent. (Abbreviations: SAs: sulfonamides; FQs: fluoroquinolones; MLs: macrolides; and TCs: tetracyclines) A–O: pharmaceutical plants A–O respectively.</p>
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<p>Composition profiles of different antibiotics in influent, effluent, and excess sludge from 15 pharmaceutical plants of China (Abbreviations: SAs: sulfonamides; FQs: fluoroquinolones; MLs: macrolides; and TCs: tetracyclines).</p>
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<p>Correlation between the total concentration of 30 target antibiotics and the population served by the pharmaceutical plants, GDP per capita, water consumption per capita, and pharmaceutical usage amount per day in the regions with pharmaceutical plants.</p>
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<p>Removal efficiencies of 30 individual antibiotics in the pharmaceutical plants (Abbreviations: SDZ: Sulfadiazine; SPD: Sulfapyridine; TMP: Trimethoprim; SMZ: Sulfamethazine; SM: Sulfameter; SMM: Sulfamonomethoxine; SCP: Sulfachlorpyridazine; SMX: Sulfamethoxazole; SA: Sulfadoxine; SQX: Sulfaquinoxaline; SDM: Sulfadimethoxine; SCT: Sulfacetamide; STZ: Sulfathiazole; MAR: Marbofloxacin; NFX: Norfloxacin; OFL: Ofloxacin; CIP: Ciprofloxacin; EFX: Enrofloxacin; PEF: Pefloxacin; LIN: Lincomycin; ETM-H<sub>2</sub>O: Erythromycin-H<sub>2</sub>O; CTM: Clarithromycin; RTM: Roxithromycin; OTC: Oxytetracycline; TC: Tetracycline; CTC: Chlorotetracycline; and DC: Doxycycline).</p>
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<p>Mean removal efficiencies (%) of antibiotics in pharmaceutical plants with different treatment processes (Abbreviations: AO: anaerobic/aerobic; CASS: conventional activated sludge system; AAO: anaerobic/anoxic/aerobic; MBR: membrane bioreactor; and AAO + MBR: anaerobic/anoxic/aerobic and membrane bioreactor).</p>
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<p>Risk quotients (RQs) for 27 target antibiotics in the pharmaceutical plant effluents (Abbreviations: SDZ: Sulfadiazine; SPD: Sulfapyridine; TMP: Trimethoprim; SMZ: Sulfamethazine; SM: Sulfameter; SMM: Sulfamonomethoxine; SCP: Sulfachlorpyridazine; SMX: Sulfamethoxazole; SA: Sulfadoxine; SQX: Sulfaquinoxaline; SDM: Sulfadimethoxine; SCT: Sulfacetamide; STZ: Sulfathiazole; MAR: Marbofloxacin; NFX: Norfloxacin; OFL: Ofloxacin; CIP: Ciprofloxacin; EFX: Enrofloxacin; PEF: Pefloxacin; LIN: Lincomycin; ETM-H<sub>2</sub>O: Erythromycin-H<sub>2</sub>O; CTM: Clarithromycin; RTM: Roxithromycin; OTC: Oxytetracycline; TC: Tetracycline; CTC: Chlorotetracycline; and DC: Doxycycline).</p>
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17 pages, 2528 KiB  
Article
Characterization and Disinfection by Product Formation of Dissolved Organic Matter in Anaerobic–Anoxic–Oxic Membrane Bioreactor (AAO-MBR) Process
by Xueli Ren, Feng Wang, Yajing Zhang, Jiali Wang and Hengfeng Miao
Water 2023, 15(6), 1076; https://doi.org/10.3390/w15061076 - 10 Mar 2023
Cited by 2 | Viewed by 3058
Abstract
In the process of sewage treatment, the characteristics of dissolved organic matter (DOM) are always changed during chemical and biological processes, affecting the generation of disinfection by-products (DBPs) compositions at the following disinfection stage. The present study systematically investigated the effect of DOM [...] Read more.
In the process of sewage treatment, the characteristics of dissolved organic matter (DOM) are always changed during chemical and biological processes, affecting the generation of disinfection by-products (DBPs) compositions at the following disinfection stage. The present study systematically investigated the effect of DOM characterization on C- and N-DBPs formation at AAO-MBR reactor when treating wastewater. The results showed that the AAO-MBR treatment process could efficiently eliminate dissolved organic carbon (DOC) and ammonium nitrogen (NH4+-N) from wastewater with an elimination rate of 89% and 98%, respectively. Most of the precursors (i.e., 56.8% C-DBPs and 78.1% N-DBPs) were removed at the MBR unit, while AGC and AAO units promoted the formation of DBPs precursors. More specifically, soluble microbial products (SMPs) and humus acid were increased, which led to improved C- and N-DBPs via aerated grit chamber (AGC) treatment. At the AAO treatment unit, the content of low MW hydrophobic SMPs, humus acid, and polysaccharides was increased, indicating low MW and HPO fractions dominating the C- and N-DBPs. MBR treatment improved C-DBPs in high MW and HPO fractions and N-DBPs in low MW and HPO fractions, which is explained by higher MW hydrophobic SMPs and humus acids, compared to the AAO unit. The present study provided deep insight into the linkage of DOM characteristics and C- and N-DBPs formation at each treatment unit during the AAO-MBR process. Full article
(This article belongs to the Section Water Quality and Contamination)
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<p>Flowchart of treatment process in the WWTP and sampling scheme ((<b>A</b>) Influent; (<b>B</b>) AGC effluent; (<b>C</b>) AAO effluent; (<b>D</b>) MBR effluent).</p>
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<p>Three-dimensional EEM of DOM in AAO-MBR treatment process: (<b>a</b>) Influent, (<b>b</b>) AGC effluent, (<b>c</b>) AAO effluent, and (<b>d</b>) MBR effluent.</p>
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<p>FT–IR spectra of the WWTP.</p>
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<p><sup>1</sup>H-NMR spectra of the WWTP.</p>
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<p>DOC percentage of various MW fractions (<b>a</b>) and hydrophobicity fractions (<b>b</b>) (HPI: hydrophilic fraction; HPO: hydrophobic fraction; TPI: transphilic fraction).</p>
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<p>DBPFP of the WWTP ((<b>a</b>) the formation of C-DBPFP; (<b>b</b>) the formation of N-DBPFP;) (A: influent of wastewater, B: AGC effluent, C: AAO effluent, and D: MBR effluent).</p>
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<p>C-DBPFP of MW fractions and polarity fractions and their contributions for diffident fractions after chlorination (<b>a</b>) C-DBPFP of MW fractions; (<b>b</b>) C-DBPFP of polarity fractions; (<b>c</b>) C-DBPFP contribution of MW fractions; (<b>d</b>) C-DBPFP contribution of polarity fractions) (B: AGC effluent, C: AAO effluent, and D: MBR effluent).</p>
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<p>N-DBPFP of MW fractions and polarity fractions and their contributions for diffident fractions after chlorination (<b>a</b>) N-DBPFP of MW fractions; (<b>b</b>) N-DBPFP of polarity fractions; (<b>c</b>) N-DBPFP contribution of MW fractions; (<b>d</b>) N-DBPFP contribution of polarity fractions) (B: AGC effluent, C: AAO effluent, and D: MBR effluent).</p>
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<p>C-DBP and N-DBP yield of water samples (<b>a</b>) C-DBP yield; (<b>b</b>) N-DBP yield) (A: influent of wastewater, B: AGC effluent, C: AAO effluent, and D: MBR effluent).</p>
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