Simulated Wastewater Treatment Process Research Articles

Degradation of phenol by peroxymonosulfate catalyzed by cerium-doped amino-functionalized metal-organic frameworks (NH2-MIL-101 (Fe, Ce))

As an organic pollutant difficult to be degraded in water bodies, phenol is toxic even at low concentrations. Therefore, studying the degradation technology of phenol is of great significance. In order to investigate the effect of transition metal doping on the performance of oxidative degradation of phenol by the persulfate activated by metal-organic frameworks, the different doping levels of Ce were tested based on the NH2-MIL-101(Fe). Then, NH2-MIL-101(Fe,Ce) with different Fe/Ce ratios was successfully prepared by solvothermal method. They were characterized by means of XRD, SEM, TEM, XPS and FT-IR. Selecting phenol as the target pollutant, the NH2-MIL-101(Fe,Ce)/PMS system was constructed to study the degradation rate. The factors of the different temperature, initial pH, anions, Ce doping ratios, catalyst dosage and PMS concentration were investigated. The mechanism of degradation of phenol for the above system was analyzed through free radical burst test, and the metal ion dissolution of catalytic materials in simulated wastewater treatment process was monitored by ICP-OES instrument. Finally, the intermediate products of phenol in the degradation process were inferred using LC-MS method. The results showed that when the molar ratio of Ce/(Ce+Fe) was 15 %, under the same reaction conditions, NH2-MIL-101(Fe,Ce)-15 % (referred to as NM-15 %) had the highest degradation rate for phenol. After the 50 min, phenol could be almostly completely removed, with K value of 0.059 min−1. This system could achieve the efficient degradation of phenol under the range of pH values (pH=3–9). The results of the free radical burst test and the free radical trapping test showed that the dominant oxidising groups were SO4•−, •OH, •O2− and non-free radical 1O2.

Community detection based process decomposition and distributed monitoring for large‐scale processes

AbstractDistributed architectures wherein multiple decision‐making units are employed to coordinate their decision‐making/actions based on real‐time communication have become increasingly important for monitoring processes that have large scales and complex structures. Typically, the development of a distributed monitoring scheme involves two key steps, that is, the decomposition of the process into subsystems, and the design of local monitors based on the configured subsystem models. In this article, we propose a distributed process monitoring approach that tackles both steps for large‐scale processes. A data‐driven process decomposition approach is proposed by leveraging community structure detection to divide variables into subsystems optimally via finding a maximal value of the metric of modularity. A two‐layer distributed monitoring scheme is developed where local monitors are designed based on the configured subsystems of variables using canonical correlation analysis. Inner‐subsystem interactions and inter‐subsystem interactions are tackled by the two layers separately, such that the sensitivity of this monitoring scheme to certain types of faults is improved. We utilize a numerical example to illustrate the effectiveness and superiority of the proposed method. It is then applied to a simulated wastewater treatment process.

Effluent quality prediction in papermaking wastewater treatment processes using dynamic Bayesian networks

Effective online modeling of papermaking wastewater treatment processes (WWTPs) is an important means to ensure wastewater recycling and harmless discharge. A composite model integrating variable importance in projection with dynamic Bayesian networks (VIP-DBN) is proposed to improve the modeling ability and reliability in WWTPs. First, a variable selection method is applied to simplify the network structure and reduce modeling costs. Then the augmented matrices technique is embedded into the Bayesian networks to cope with the dynamic characteristics, nonlinearity, and uncertainty simultaneously. The modeling performance of VIP-DBN is evaluated through two case studies, a simulated WWTP based on benchmark simulation model no. 1 (BSM1) and a real papermaking WWTPs, in which the VIP-DBN model shows better modeling performance than its rivals. Specifically, compared with partial least squares, artificial neural networks, and Bayesian networks, the determination coefficient value of VIP-DBN is increased by 34.36%, 20.55%, and 3.30%, respectively, for the prediction of effluent nitrate in BSM1. The VIP-DBN can be used to deal with complex WWTPs in industrial applications, which provides a better practical method for soft sensor modeling and a guarantee for the effective decision-making of wastewater treatment processes for papermaking enterprises.

Effect of Graphene Oxide on the Ammonia Removal and Bacterial Community in a Simulated Wastewater Treatment Process

AbstractPotential environmental risks posed by graphene oxide (GO) should be evaluated. Wastewater treatment plants will be one of the last barriers for GO wastes. Hence, this study investigated th...

Transformation of PVP coated silver nanoparticles in a simulated wastewater treatment process and the effect on microbial communities

BackgroundManufactured silver nanoparticles (AgNPs) are one of the most commonly used nanomaterials in consumer goods and consequently their concentrations in wastewater and hence wastewater treatment plants are predicted to increase. We investigated the fate of AgNPs in sludge that was subjected to aerobic and anaerobic treatment and the impact of AgNPs on microbial processes and communities. The initial identification of AgNPs in sludge was carried out using transmission electron microscopy (TEM) with energy dispersive X-ray (EDX) analysis. The solid phase speciation of silver in sludge and wastewater influent was then examined using X-ray absorption spectroscopy (XAS). The effects of transformed AgNPs (mainly Ag-S phases) on nitrification, wastewater microbial populations and, for the first time, methanogenesis was investigated.ResultsSequencing batch reactor experiments and anaerobic batch tests, both demonstrated that nitrification rate and methane production were not affected by the addition of AgNPs [at 2.5 mg Ag L-1 (4.9 g L-1 total suspended solids, TSS) and 183.6 mg Ag kg -1 (2.9 g kg-1 total solids, TS), respectively].The low toxicity is most likely due to AgNP sulfidation. XAS analysis showed that sulfur bonded Ag was the dominant Ag species in both aerobic (activated sludge) and anaerobic sludge. In AgNP and AgNO3 spiked aerobic sludge, metallic Ag was detected (~15%). However, after anaerobic digestion, Ag(0) was not detected by XAS analysis. Dominant wastewater microbial populations were not affected by AgNPs as determined by DNA extraction and pyrotag sequencing. However, there was a shift in niche populations in both aerobic and anaerobic sludge, with a shift in AgNP treated sludge compared with controls. This is the first time that the impact of transformed AgNPs (mainly Ag-S phases) on anaerobic digestion has been reported.ConclusionsSilver NPs were transformed to Ag-S phases during activated sludge treatment (prior to anaerobic digestion). Transformed AgNPs, at predicted future Ag wastewater concentrations, did not affect nitrification or methanogenesis. Consequently, AgNPs are very unlikely to affect the efficient functioning of wastewater treatment plants. However, AgNPs may negatively affect sub-dominant wastewater microbial communities.

Removal of ZnO nanoparticles in simulated wastewater treatment processes and its effects on COD and NH4+-N reduction

For many engineered nanoparticles, the primary pathway of release into the environment is via sewage and industrial wastewater discharges. In this work, the removal of uncoated ZnO nanoparticles (ZnO NPs) during simulated wastewater treatment processes and its impact on treatment performance were examined. Simulated primary clarification removed the majority (about 70%) of the dosed ZnO NPs. During simulated sequencing batch reactor (SBR) processes, ZnO NPs were completely removed in each cycle throughout the 11-day experimental duration (two cycles per day). Continuous input of ZnO NPs into the wastewater (at concentrations up to 5 mg L(-1)) did not reduce chemical oxygen demand (COD) removal. NH(4)(+)-N removal was reduced at a dosing concentration of 5 mg L(-1) ZnO NPs per cycle. Inhibition of respiration of nitrifying microorganisms by ZnO NPs corroborated the reduction of NH(4)(+)-N removal. These results indicate that if the wastewater is treated, the release of ZnO NPs into receiving water bodies would be minimal and ZnO NPs would mainly accumulate in biosolids. Uncoated ZnO NPs in wastewater at very high concentrations may have some adverse effects on activated sludge process.

Removal of silver nanoparticles in simulated wastewater treatment processes and its impact on COD and NH4 reduction

The increasing utilization of silver nanoparticles in industrial and consumer products has raised concern to wastewater treatment utilities due to its antimicrobial activity. In this work, the removal of citrate stabilized silver nanoparticles (Ag-NPs) during the wastewater treatment processes and its impact on treatment performance were examined. During simulated primary clarification, over 90% of the Ag-NPs remained in the wastewater, indicating that the majority of silver nanoparticles in sewage would enter the subsequent treatment units. During sequencing batch reactor processes, silver nanoparticles were effectively removed in each cycle throughout the 15-d experimental duration. Continuous input of silver nanoparticles into the wastewater did not significantly alter chemical oxygen demand (COD) removal. NH4 removal was reduced at the beginning of the SBR experiment but quickly recovered at the later stage of the experiment. This study demonstrated that in the near future it is unlikely that citrate-stabilized Ag-NPs released into sewage will cause significant adversary effects on the COD and NH4 removal of activated sludge processes in municipal wastewater treatment plants.

Fate and biological effects of silver, titanium dioxide, and C 60 (fullerene) nanomaterials during simulated wastewater treatment processes

As engineered nanomaterials (NMs) become used in industry and commerce their loading to sewage will increase. In this research, sequencing batch reactors (SBRs) were operated with hydraulic (HRT) and sludge (SRT) retention times representative of full-scale biological WWTPs for several weeks. Under environmentally relevant NM loadings and biomass concentrations, NMs had negligible effects on ability of the wastewater bacteria to biodegrade organic material, as measured by chemical oxygen demand (COD). Carboxy-terminated polymer coated silver nanoparticles ( fn-Ag) were removed less effectively (88% removal) than hydroxylated fullerenes (fullerols; >90% removal), nano TiO 2 (>95% removal) or aqueous fullerenes ( nC 60; >95% removal). Experiments conducted over 4 months with daily loadings of nC 60 showed that nC 60 removal from solution depends on the biomass concentration. Under conditions representative of most suspended growth biological WWTPs (e.g., activated sludge), most of the NMs will accumulate in biosolids rather than in liquid effluent discharged to surface waters. Significant fractions of fn-Ag were associated with colloidal material which suggests that efficient particle separation processes (sedimentation or filtration) could further improve removal of NM from effluent.

Effect of oxic and anoxic conditions on nitrous oxide emissions from nitrification and denitrification processes

A lab-scale sequencing batch reactor fed with real municipal wastewater was used to study nitrous oxide (N(2)O) emissions from simulated wastewater treatment processes. The experiments were performed under four different controlled conditions as follows: (1) fully aerobic, (2) anoxic-aerobic with high dissolved oxygen (DO) concentration, (3) anoxic-aerobic with low DO concentration, and 4) intermittent aeration. The results indicated that N(2)O production can occur from both incomplete nitrification and incomplete denitrification. N(2)O production from denitrification was observed in both aerobic and anoxic phases. However, N(2)O production from aerobic conditions occurred only when both low DO concentrations and high nitrite concentration existed simultaneously. The magnitude of N(2) O produced via anoxic denitrification was lower than via oxic denitrification and required the presence of nitrite. Changes in DO, ammonium, and nitrite concentrations influenced the magnitude of N(2)O production through denitrification. The results also suggested that N(2)O can be produced from incomplete denitrification and then released to the atmosphere during aeration phase due to air stripping. Therefore, biological nitrogen removal systems should be optimized to promote complete nitrification and denitrification to minimize N(2)O emissions.

Persistence of Pathogenic Prion Protein during Simulated Wastewater Treatment Processes

Transmissible spongiform encephalopathies (TSEs, prion diseases) are a class of fatal neurodegenerative diseases affecting a variety of mammalian species including humans. A misfolded form of the prion protein (PrP(TSE)) is the major, if not sole, component of the infectious agent. Prions are highly resistant to degradation and to many disinfection procedures suggesting that, if prions enter wastewater treatment systems through sewers and/or septic systems (e.g., from slaughterhouses, necropsy laboratories, rural meat processors, private game dressing) or through leachate from landfills that have received TSE-contaminated material, prions could survive conventional wastewater treatment. Here, we report the results of experiments examining the partitioning and persistence of PrPTSE during simulated wastewater treatment processes including activated and mesophilic anaerobic sludge digestion. Incubation with activated sludge did not result in significant PrPTSE degradation. PrPTSE and prion infectivity partitioned strongly to activated sludge solids and are expected to enter biosolids treatment processes. A large fraction of PrPTSE survived simulated mesophilic anaerobic sludge digestion. The small reduction in recoverable PrPTSE after 20-d anaerobic sludge digestion appeared attributable to a combination of declining extractability with time and microbial degradation. Our results suggest that if prions were to enter municipal wastewater treatment systems, most would partition to activated sludge solids, survive mesophilic anaerobic digestion, and be present in treated biosolids.

Sensor fault identification based on time-lagged PCA in dynamic processes

Principal component analysis (PCA) is widely employed as a multivariate statistical method for fault detection, isolation and diagnosis in chemical processes. Previously, PCA has been successfully used to identify faulty sensors under normal static operating conditions. In this paper, we extend the reconstruction-based sensor fault isolation method proposed by Dunia et al. to dynamic processes. We develop a new method for identifying and isolating sensor faults in an inherent dynamic system. First, we describe how to reconstruct noisy or faulty measurements in dynamic processes. The reconstructed measurements are obtained by simple iterative optimization based on the correlation structure of the time-lagged data set. Then, based on the sensor validity index (SVI) approach developed by Dunia et al., we propose an SVI for fault isolation in dynamic processes. The proposed method was applied to sensor fault isolation in two strongly dynamic systems: a simulated 4×4 dynamic process and a simulated wastewater treatment process (WWTP). In these experiments, the proposed sensor fault identification method correctly and rapidly identified the faulty sensor; in contrast, the traditional PCA-based sensor fault isolation approach showed unsatisfactory results when applied to the same systems.