Operation Of Wastewater Treatment Plants Research Articles

Evaluation of the Performance of Wastewater Treatment Plant at Steel Mill Karachi, Pakistan

The purpose of this study is to evaluate how well Steel Mill Karachi's wastewater treatment system works. Wastewater treatment facilities employ the activated sludge technique. Several influent and effluent locations allowed for the collection of influent and effluent samples. A number of physicochemical parameters, such as total dissolved solids (TDS), biochemical oxygen demand (BOD5), total suspended solids (TSS), chemical oxygen demand (COD), electric conductivity (EC), pH, and other metals, such as potassium (K), iron (Fe), sulphate (SO4-2), calcium (Ca), sodium (Na), zinc (Zn), and magnesium (Mg), were measured in order to evaluate the effectiveness of wastewater treatment plants. In a lab environment, the sample's analysis findings were compared to Sindh Environmental Quality Standards (SEQS) for Pakistan. EC, BOD5, COD, SO4-2, Ca, Mg, Na, K, TDS, TSS, EC, BOD5, COD, SO4-2, Ca, Mg, Na, K, Fe, and Zn were removed to different degrees: 42.93%, 60.33%, 43.63%, 51.53%, 40.5%, 35.56%, 26.33%, 22.12%, 34.09%, 20.75%, 55.87%, 22.12%, and 76.10%. Only two metrics, the COD and Zn concentrations, were over the SEQS limits, keeping other effluent results parameters within the limits. Additionally, it is advised that proper maintenance be carried out to increase the effectiveness of wastewater treatment plants.

EDAR 4.0: Machine Learning and Visual Analytics for Wastewater Management

Wastewater treatment plant (WWTP) operations manage massive amounts of data that can be gathered with new Industry 4.0 technologies such as the Internet of Things and Big Data. These data are critical to allow the wastewater treatment industry to improve its operation, control, and maintenance. However, the data available need to be improved and enriched, partly due to their high dimensionality and low reliability, and the lack of appropriate data analysis and processing tools for such systems. This paper presents a visual analytics-based platform for WWTP that allows users to identify relationships among data through data inspection. The results show that the tool developed and implemented for a full-scale WWTP allows operators to construct machine learning (ML) models for water quality and other water treatment process variables. Consequently, analyzing and optimizing plant operation scenarios can enhance key variables, including energy, reagent consumption, and water quality. This improvement facilitates the development of a more sustainable WWTP, contributing to a beneficial environmental impact. Domain experts validated the variables influencing the created ML models and proved their appropriateness.

Modeling the effects of sidestreams recycling on wastewater treatment plant performance operated by anaerobic-anoxic-oxic (A2/O) processes using GPS-X8 simulation

The water separated by sludge treatment processes is known as the sidestreams, such as the centrate, underflow belt thickener, and supernatant gravity thickener. The sidestreams have significant amounts of contaminants, particularly nutrients, and are returned to the wastewater treatment plant head. Returning the sidestreams to the head of the plant causes both technical and financial problems. This paper models the effect of sidestreams on the treatment of mainstream in full-scale conventional activated sludge systems operated with anaerobic/anoxic/oxic (A2/O). The effects of sidestreams nutrients and inert materials, the pollutants fate in the A2/O system, oxygen consumed, and energy required by sidestreams are modeled using GPS-X8 simulation. The results showed that the discharge ratio of the sidestreams to the mainstream is 4 %. The proportion of the pollutants present in the sidestream to the mainstream were 4.3 %, 4.7 %, 8.8 %, 21 %, 40 %, and 11 % for BOD, COD, TSS, NH4, PO4, and H2S, respectively. The GPS-X model showed that high orthophosphate and hydrogen sulfide concentrations were seen in the anaerobic zone. While all other pollutants begin to decay, phosphates and hydrogen sulfide gas also begin to decrease gradually in anoxic and oxic zones, noting the high concentrations of nitrates and sulfates in the oxic zone. An added 10 % of oxygen is needed for the sidestreams ammonia oxidation, which is more than the amount needed for the oxidation of BOD. The percentage of alkali used to improve the nitrification process due to the ammonia in the sidestreams was 30 % of the specified amount of ammonia in the mainstream. This study proved that the plant's sidestreams adversely increased oxygen, energy, and alkali consumption.

The potential for water recovery from urban waste water - The perspective of urban waste water treatment plant operators in Poland

Water recovery from waste water has become an essential element of the circular economy in the Baltic Sea region. However, there is little data on the possibility of using water recovered from urban waste water. A survey was conducted to learn the opinions of Poland waste water treatment plant operators. They were asked whether they recovered water for internal or external needs. Respondents indicated opportunities and barriers in this activity. The opinions of 107 operators show that work is underway on closing internal circuits in urban WWTPs. These solutions are technically relatively easy to implement and show measurable benefits (i.e., saving drinking water). However, water recovery for external purposes is rare and is at a very early stage. Despite this, the potential is significant, although many financial, organizational, technical, and mental barriers exist. The most critical challenge is the safe use of reclaimed water and the cost-effectiveness of the solutions. The survey also shows a need for education and involvement of the public.

Insightful Analysis and Prediction of SCOD Component Variation in Low-Carbon/Nitrogen-Ratio Domestic Wastewater via Machine Learning

The rapid identification of the amount and characteristics of chemical oxygen demand (COD) in influent water is critical to the operation of wastewater treatment plants (WWTPs), especially for WWTPs in the face of influent water with a low carbon/nitrogen (C/N) ratio. Given that, this study carried out batch kinetic experiments for soluble chemical oxygen demand (SCOD) and nitrogen degradation for three WWTPs and established machine learning (ML) models for the accurate prediction of the variation in SCOD. The results indicate that four different kinds of components were identified via parallel factor (PARAFAC) analysis. C1 (Ex/Em = 235 nm and 275/348 nm, tryptophan-like substances/soluble microbial by-products) contributes to the majority of internal carbon sources for endogenous denitrification, whereas C4 (230 nm and 275/350 nm, tyrosine-like substances) is crucial for readily biodegradable SCOD composition according to the machine learning (ML) models. Furthermore, the gradient boosting decision tree (GBDT) algorithm achieved higher interpretability and generalizability in describing the relationship between SCOD and carbon source components, with an R2 reaching 0.772. A Shapley additive explanations (SHAP) analysis of GBDT models further validated the above result. Undoubtedly, this study provided novel insights into utilizing ML models to predict SCOD through the measurements of the excitation–emission matrix (EEM) in specific Ex and Em positions. The results could help us to identify the degradation and transformation relationship between different kinds of carbon sources and nitrogen species in the wastewater treatment process, and thus provide a novel guidance for the optimized operation of WWTPs.

Effect of Nanobubble Injector–Based Aeration on the Performance of Wastewater Treatment Plant

Effect of Nanobubble Injector–Based Aeration on the Performance of Wastewater Treatment Plant

Using Quantitative Microbial Risk Assessment (QMRA) of SARS-CoV-2 to understand possible exposure to health risks in selected wastewater treatment plants located in the Eastern region of South Africa

In the past two years, Covid-19 has emerged as the most severe and pressing public health issue, causing a great deal of damage to societal and economic welfare, as well as causing illness and mortality. The operators in wastewater treatment plants (WWTPs), particularly those employed in rural communities, appear to often exhibit a lack of adherence to proper safety protocols by not utilizing sufficient protective equipment while handling unprocessed sewage samples throughout the different phases of wastewater treatment and disposal. This study aimed at examining the potential health risk of infection among WWTP operators, as a result of unintentional ingestion of wastewater during routine duties in facilities that receive influent containing Severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) from various areas. This study examined the prevalence of SARS-CoV-2 in grab samples of untreated wastewater samples using the real-time quantitative polymerase chain reaction (RT-qPCR) technique and quantitative microbial risk assessment (QMRA) model was employed on three probable exposure of SARS-CoV-2 scenarios that are expressed as moderate, aggressive and extreme (2 mL, 10 mL, 20 mL) to evaluate the probability of infection to WWTP workers based on the 6 h that the workers spent in WWTPs performing their daily activities which exposed them to potential health risk of various pathogens. At the highest SARS-CoV-2 genome of 266.23 × 102 gc/mL, the findings indicated that there was no statistically significant difference in the probability of infections with respect to seasonal differences because the P(i) value was greater than 0.05 (p > 0.05). Overall, P(i) was highly significant across all volumetric scenarios in the study with p value that was p < 0.001. The probability of getting infected during the different seasons is assumed to be low since there was no statistically difference in P(i) with respect to season however it can be assumed that there is a high chance of getting infected regardless of volumetric intake. Our study suggests that the risk of accidental occupational exposure to SARS-CoV-2 in raw wastewater is negligible to workers whereby workers would perform their daily activities without wearing protective gear. Nevertheless, the importance and work of WWTPs by workers should not be overlooked. Regardless of the situation, it is widely recognized that residential wastewater poses a potential risk of infection due to the presence of several enteric pathogens, therefore, it is crucial to ensure that those who are occupationally exposed to untreated wastewater are well equipped with suitable personal protective equipment (PPE).

Factors affecting the performance of a pharmaceutical wastewater treatment plant: Characterization of effluent and environmental risk

Pharmaceutical industries produce a huge volume of emerging pollutants (EPs) that pose a threat to the aqueous environment. Biological processes have shown their inefficacy in treating many pharmaceutical products. The study assessed physicochemical parameters, EPs, heavy metals in pharmaceutical industrial wastewater, and the removal efficiency (RE) of an aerobic biological treatment plant. The study also assessed the contamination levels and risk using several indices, such as the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI), heavy metal pollution index (HPI), heavy metal evaluation index (HEI), and risk quotients index (RQs). The study found that the treated water quality was poor, having antibiotics, nonsteroidal anti-inflammatory drugs, and others, along with several transformation products (TPs) and heavy metals, which were unsafe for consumption with high environmental risk. The analysis results showed that the RE for TSS, BOD5, COD, TDS, and EC were found to be 91.80%, 86.81%, 72.29%, 72.20%, and 65.60%, respectively, where the values of BOD5, COD, NO3−, and PO43− in the effluent were still higher than the permissible limits of the ECR (2023). However, the RE for heavy metals was in the order of Cu (84.62%) > Fe (65.04%) > Mn (63.3%) > Zn (60.58%) > Cd (53.85%) > Ni (54.12%) > Pb (42.42%) > Cr (38%), where Cr and Cd concentrations were still higher than the permissible limit of DoE (2019). The Pearson correlation and PCA suggested that EC, TDS, TSS, DO, BOD5, and COD were the most correlating and contributing variables. This study argued that metal-ligand behaviors mainly affect the removal efficiency of the treatment plant by lowering the removal rate of heavy metals and pharmaceutical products.

Multi-Objective Optimization Based on Simulation Integrated Pareto Analysis to Achieve Low-Carbon and Economical Operation of a Wastewater Treatment Plant

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.

Deconjugation potentials of natural estrogen conjugates in sewage and wastewater treatment plant: New insights from model prediction and on-site investigations

Natural estrogen conjugates play important roles in municipal wastewater treatment plant (WWTP), but their deconjugation potentials are poorly understood. This work is the first to investigate the relationships between the enzyme activities of arylsulfatase/β-glucuronidase and deconjugation potentials of natural estrogen conjugates. This work led to three important findings. First, the enzyme activity of β-glucuronidase in sewage is far higher than that of arylsulfatase, while their corresponding activities in activated sludge were similar. Second, a model based on β-glucuronidase could successfully predict the deconjugation potentials of natural estrogen glucuronide conjugates in sewage. Third, the enzyme activity of arylsulfatase in sewage was too low to lead to evident deconjugation of sulfate conjugates, which means that the deconjugation rate of estrogen sulfates can be regarded as zero. By comparing their theoretical removal based on enzyme activity and on-site investigation, it is reasonable to conclude that reverse deconjugation of estrogen conjugates (i.e., conjugation of natural estrogens to form conjugated estrogens) likely exist in WWTP, which explains well why natural estrogen conjugates cannot be effectively removed in WWTP. Meanwhile, this work provides new insights how to improve the removal performance of WWTP on natural estrogen conjugates. SynopsisThis work is the first to show how arylsulfatase/β-glucuronidase could affect deconjugation of natural estrogen conjugates and possible way to enhance their removal in wastewater treatment plant.

Machine learning assisted combined systems of wastewater treatment plants with constructed wetlands optimal decision-making

This study proposed an efficient framework for optimizing the design and operation of combined systems of wastewater treatment plants (WWTP) and constructed wetlands (CW). The framework coupled a WWTP model with a CW model and used a multi-objective evolutionary algorithm to identify trade-offs between energy consumption, effluent quality, and construction cost. Compared to traditional design and management approaches, the framework achieved a 27 % reduction in WWTP energy consumption or a 44 % reduction in CW cost while meeting strict effluent discharge limits for Chinese WWTP. The framework also identified feasible decision variable ranges and demonstrated the impact of different optimization strategies on system performance. Furthermore, the contributions of WWTP and CW in pollutant degradation were analyzed. Overall, the proposed framework offers a highly efficient and cost-effective solution for optimizing the design and operation of a combined WWTP and CW system.

Should wastewater treatment plants' operational mode radically change to minimize GHG emissions?

The operation of municipal wastewater treatment plants (WWTPs) invariably results in significant emission of greenhouse gases (i.e., CH4, N2O, and CO2) into the atmosphere. We propose to consider a radical change in the way municipal WWTPs are operated, with the aim of minimizing GHG emissions while recycling most of the nutrient mass. The means to this end are to reduce the WWTP energy demand while maximizing the recovery of resources (phosphorus, ammonia, methane). The suggested concept involves operating the activated sludge process at a low sludge retention time (SRT < 2 d), i.e., under conditions that maximize the heterotrophic mass yield and eliminate nitrification. The ammonia concentration that remains in the water (considering N in the excess sludge and struvite production in the sludge-dewatering supernatant line) would be separated from the WWTP effluents using a unique ion-exchange material (ZnHCF), which would be regenerated using a low-volume 4 M NaCl solution. The ammonia would be then stripped at high pH and re-adsorbed by an acidic solution for reuse as fertilizer. The high bacterial yield and lack of nitrification in the aerobic step are expected to boost methane yield 3–4-fold, induce lower oxygen consumption, and most importantly, yield much lower N2O release. An approximate energy mass balance shows the concept to merit further consideration, owing to the potential significant reduction in N2O(g) emissions and recovery of resources. Empirical work followed by LCA is required to corroborate the hypothesis presented herein.

Application of a water-energy-carbon coupling index to evaluate the long-term operational stability of the anaerobic-anoxic-oxic-membrane bioreactor (A2/O-MBR) process under the influence of rainstorms

In the context of global climate change, sudden rainstorms and typhoons induce fluctuations in hydraulic shocks to wastewater treatment plants (WWTPs) in coastal areas, causing two challenges of stable effluent quality and low-carbon operation. We established a quantitative evaluation method for resistance of wastewater treatment processes to hydraulic shocks based on the water-energy-carbon nexus using operational data from a WWTP in southeast coastal China from July 2018 to December 2022. The effects of hydraulic shocks on the operational stability of the anaerobic-anoxic-oxic-membrane bioreactor (A2/O-MBR) process were analyzed following five steps. The results showed that the gray water footprint (GWF) of the process was 9.3% lower than that of the A2/O process. The energy footprint (ENF) and carbon footprint (CF) were approximately 2.1 times and 1.7 times higher than those of the A2/O process, respectively. The resistance to hydraulic shocks of the A2/O-MBR process is approximately 5.5 times higher than that of the A2/O process. In conclusion, the A2/O-MBR process exhibits higher process operational stability when subjected to hydraulic shocks, which is more conducive to the efficient and stable operation of WWTPs in rainstorm and typhoon-prone areas. The evaluation methodology provides qualitative technical support for selecting upgrading processes for WWTPs in different regions.

Evaluating the environmental and economic performance of biological and advanced biological wastewater treatment plants by life cycle assessment and life cycle costing.

The primary objective of this study is to assess and establish benchmarks for environmental and economic sustainability of biological and advanced biological wastewater treatment plants (WWTPs) with different treatment technologies and characteristics. Furthermore, the study aims to determine the beneficial role of WWTPs to reduction of eutrophication potential. Environmental and economic sustainability of ten municipal WWTPs was assessed using life cycle assessment (LCA) and life cycle costing (LCC). In the first section of the study, LCA was performed to determine the environmental performance of the WWTPs. Furthermore, net environmental benefit (NEB) approach was implemented to reveal the beneficial role of WWTPs to eutrophication potential. In the subsequent section, LCA-based LCC was conducted by integrating the results of LCA. The most significant environmental impact was determined as marine aquatic ecotoxicity, which is highly affected from the generation and transmission of electricity consumed in the WWTPs. Wastewater recovery and co-incineration of sewage sludge in cement kiln ensure significant environmental savings on ozone layer depletion, human toxicity, acidification, photochemical oxidation, and abiotic depletion (fossil fuel) potential. Considering NEB approach, the highest NEB values were found for the WWTPs with the higher organic load and nutrient concentration in the influent. The results of LCC in WWTPs varied between 0.21 and 0.53 €/m3. External (environmental) costs were evaluated higher than internal (operational) costs for all selected WWTPs. While eutrophication was the highest among environmental costs, electricity cost was the highest among operational costs for almost all WWTPs.

Genetic programming expressions for effluent quality prediction: Towards AI-driven monitoring and management of wastewater treatment plants

Continuous effluent quality prediction in wastewater treatment processes is crucial to proactively reduce the risks to the environment and human health. However, wastewater treatment is an extremely complex process controlled by several uncertain, interdependent, and sometimes poorly characterized physico-chemical-biological process parameters. In addition, there are substantial spatiotemporal variations, uncertainties, and high non-linear interactions among the water quality parameters and process variables involved in the treatment process. Such complexities hinder efficient monitoring, operation, and management of wastewater treatment plants under normal and abnormal conditions. Typical mathematical and statistical tools most often fail to capture such complex interrelationships, and therefore data-driven techniques offer an attractive solution to effectively quantify the performance of wastewater treatment plants. Although several previous studies focused on applying regression-based data-driven models (e.g., artificial neural network) to predict some wastewater treatment effluent parameters, most of these studies employed a limited number of input variables to predict only one or two parameters characterizing the effluent quality (e.g., chemical oxygen demand (COD) and/or suspended solids (SS)). Harnessing the power of Artificial Intelligence (AI), the current study proposes multi-gene genetic programming (MGGP)-based models, using a dataset obtained from an operational wastewater treatment plant, deploying membrane aerated biofilm reactor, to predict the filtrated COD, ammonia (NH4), and SS concentrations along with the carbon-to-nitrogen ratio (C/N) within the effluent. Input features included a set of process variables characterizing the influent quality (e.g., filtered COD, NH4, and SS concentrations), water physics and chemistry parameters (e.g., temperature and pH), and operation conditions (e.g., applied air pressure). The developed MGGP-based models accurately reproduced the observations of the four output variables with correlation coefficient values that ranged between 0.98 and 0.99 during training and between 0.96 and 0.99 during testing, reflecting the power of the developed models in predicting the quality of the effluent from the treatment system. Interpretability analyses were subsequently deployed to confirm the intuitive understanding of input-output interrelations and to identify the governing parameters of the treatment process. The developed MGGP-based models can facilitate the AI-driven monitoring and management of wastewater treatment plants through devising optimal rapid operation and control schemes and assisting the plants’ operators in maintaining proper performance of the plants under various normal and disruptive operational conditions.

Using Surrogate Parameters to Enhance Monitoring of Community Wastewater Management System Performance for Sustainable Operations.

Community wastewater management systems (CWMS) are small-scale wastewater treatment systems typically in regional and rural areas with less sophisticated treatment processes and often managed by local governments or communities. Research and industrial applications have demonstrated that online UV-Vis sensors have great potential for improving wastewater monitoring and treatment processes. Existing studies on the development of surrogate parameters with models from spectral data for wastewater were largely limited to lab-based. In contrast, industrial applications of these sensors have primarily targeted large wastewater treatment plants (WWTPs), leaving a gap in research for small-scale WWTPs. This paper demonstrates the suitability of using a field-based online UV-Vis sensor combined with advanced data analytics for CWMSs as an early warning for process upset to support sustainable operations. An industry case study is provided to demonstrate the development of surrogate monitoring parameters for total suspended solids (TSSs) and chemical oxygen demand (COD) using the UV-Vis spectral data from an online UV-Vis sensor. Absorbances at a wavelength of 625 nm (UV625) and absorbances at a wavelength of 265 nm (UV265) were identified as surrogate parameters to measure TSSs and COD, respectively. This study contributes to the improvement of WWTP performance with a continuous monitoring system by developing a process monitoring framework and optimization strategy.

Is Africa Ready to Use Phycoremediation to Treat Domestic Wastewater as an Alternative Natural Base Solution? A Case Study

This review outlines the potential of phycoremediation as a natural, cost-effective solution for domestic wastewater treatment in Africa, particularly focusing on its application in less densely populated and rural areas. The urgency of improving sanitation access, a key objective in both the Millennium Development Goals (2000–2015) and the Sustainable Development Goals (2015–2030), is underscored by the fact that half of Africa’s population suffers from diseases linked to inadequate water and sanitation facilities. South Africa, a focal point of this study, faces significant challenges in wastewater management. These include the limited capacity of wastewater treatment plants to handle the burgeoning wastewater volumes due to population growth, unregulated discharges causing fluctuating pollution levels, and high operational costs leading to improper sludge disposal and odor issues. Compounding these problems are frequent power outages, financial constraints impacting wastewater treatment plant operations and maintenance across Africa, and a lack of skilled personnel to manage these facilities.

Toxic effects of polycyclic aromatic hydrocarbons on the two-sludge system: Combined functional gene, resistance gene and microbial community assessment

Polycyclic aromatic hydrocarbons (PAHs) were a class of persistent organic matter, and contained PAHs wastewater was discharged into wastewater treatment plants (WWTPs) finally, which could greatly affect their operational efficiency. The toxic effects of PAHs on the two-sludge system were assessed from functional genes, resistance genes, and microbial community. Three types of PAHs including Naphthalene (NAP), Anthracene (ANT), and Phenanthrene (PHE) were individually introduced into three separate two-sludge systems. Results showed that three types of PAHs did not adversely affect on nitrification performance, but exhibited a significant toxic effect on denitrification and phosphorus (PO43--P) removal. The effluent NH4+-N was almost 0 mg/L in all three systems, the highest total nitrogen (TN) removal rate was achieved at 90.88% in the PHE system, whereas the lowest rate was 72.46% in the ANT system. Concurrently, the PO43--P removal rate decreased from 93.10% to 51.22% in the ANT system. Functional genes, such as NORB and NIRS, and resistance genes such as PAH-RHD GNF/R and NAH-F/NH-R, were enriched in the PHE and NAP system, contributing to improved pollutant removal. However, the NIRK and PAH-RHD GPF/R were more abundant in the ANT system, resulting in varied pollutant removal capabilities. The composition of the microbial community was also influenced by PAH exposure with some specific functional bacteria being identified across the different systems. Among the PAHs tested, ANT was found to exert the highest toxicity towards the two-sludge system, surpassing the toxic effects of NAP and PHE. This study highlights the importance of considering PAH toxicity in the context of WWTPs operations and the potential need for tailored strategies to mitigate their impact on biological processes.

Integrated supervisory fuzzy control framework for biological wastewater treatment plants operation and their effect on GHG emissions

In this research, the effect on the greenhouse gases (GHG) emissions from biological wastewater treatment plants (WWTP) operation by implementing an integrated supervisory control framework on the bench-mark simulation model 2 gas (BSM2G) platform is studied. Ammonia based aeration control is considered for improving the efficiency of the nitrification process and also nitrate control is considered to enhance the performance of the overall plant. Different control strategies are proposed by considering PI control, Model Predictive control and Fuzzy controllers. A notable change is observed in GHG emissions due to bio-treatment because of the impact of control strategies on N2O emissions during denitrification with respect to the nitration where dissolved oxygen (DO) control alone is considered. In addition, when the nitrate loop is considered, a better change is observed in the overall plant performance. On comparison with other approaches, the proposed control strategies resulted in a reduction in the GHG emissions with marginal effect on the other performance indexes effluent quality index (EQI) and operational cost index (OCI).

Performance of full-scale rural wastewater treatment plants in the reduction of antibiotic-resistant bacteria and antibiotic resistance genes from small-city effluents

The main objective of this study was to evaluate the performance of full-scale rural wastewater treatment plants (WWTPs) in the reduction of antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs) from small-city effluents. Twenty full-scale WWTPs in rural Chile with different biological treatment technologies (vermifiltration (VF), activated sludge (AS) and biodisc (BD)) and disinfection treatments (chlorination (Cl) and UV irradiation (UV)) were monitored and studied in two campaigns: Campaign 1 (20 WWTPs) and Campaign 2 (6 WWTPs). In both campaigns, the rural WWTPs improved the water quality of the effluents very significantly (90%, 70%, and 40% reductions in TU, COD, and NH4+-N, respectively) and reduced ARB and ARG loads by 2–4 log units. All three biological treatments contributed to the final quality of the effluents, especially in terms of microbiological parameters, with statistically indistinguishable efficiencies between them. These results show the importance of rural WWTPs in improving the water quality of urban effluents while reducing microbiological risk and the spread of antibiotic resistance into the environment. The study demonstrates the utility of a non-centralized, self-managed wastewater treatment scheme that can be implemented in many sparsely populated areas around the world.