Biological Wastewater Treatment Plants Research Articles

Determination of benzophenone derivatives in wastewater by GC–MS/MS combined with in‐port derivatization

AbstractThe analysis of UV filters (UVFs) in water has become increasingly important due to their adverse effects on aquatic organisms and humans. This study describes a method for the determination of benzophenone derivatives UVF in wastewater samples. The selected UVFs are 2‐hydroxy‐4‐methoxybenzophenone (BP‐3), 2,4‐dihydroxybenzophenone (BP‐1), 4‐hydroxybenzophenone (4HB), 2,2′‐dihydroxy‐4‐methoxybenzophenone (DHMB) and lastly, 4,4′‐dihydroxybenzophenone (4DHB). The method includes solid‐phase extraction (SPE) of analytes from wastewater followed by on‐line derivatization with bis(trimethylsilyl)trifluoroacetamide (BSTFA) and analysis with GC‐MS/MS. Method validation studies resulted in good recoveries (86–112%), relative standard deviation RSD = 0.8 and 7.3%, the limits of detection LODs = 1.00–10.8 ng/L, and the limits of quantification LOQs = 3.00–32.3 ng/L. The method was successfully applied to domestic wastewater samples collected from influent and effluent of touristic hotels’ biological wastewater treatment plants. BP‐3 (24–1765 ng/L), BP‐1 (8–703 ng/L), 4HB (26–96 ng/L), and 4DHB (20–22 ng/L) were the common benzophenone derivatives in the influent wastewater while effluent contained mainly BP‐1 (8–32 ng/L), 4HB (12–57 ng/L) and 4DHB (20–102 ng/L). These results indicate that BP‐3 and BP‐1 are biodegraded in the treatment processes. However, 4HB and 4DHB are resistant to degradation and they are the main benzophenone metabolites discharged to receiving media.

Reduction of greenhouse gas emissions from closed activated sludge- to aerobic granular sludge-based biosystems via gas circulation

Greenhouse gas (GHG) emissions from biological treatment units are challenging wastewater treatment plants (WWTPs) due to their wide applications and global warming. This study aimed to reduce GHG emissions (especially N2O) using a gas circulation strategy in a closed sequencing-batch reactor when the biological unit varies from activated sludge (AS) to aerobic granular sludge (AGS). Results show that gas circulation lowers pH to 6.3 ± 0.2, facilitating regular granules but elevating total N2O production. From AS to AGS, N2O emission factor increased (0.07–0.86 %) due to decreasing ammonia-oxidizing rates while the emissions of CO2 (0.3 ± 0.1 kg-CO2/kg-chemical oxygen demand) and CH4 remained in the closed biosystem. The gas circulation decreased N2O emission factor by 63 ± 15 % after granulation higher than 44 ± 34 % before granulation, which is implemented by heterotrophic denitrification. This study provides a feasible strategy to enhance heterotrophic N2O elimination in the biological WWTPs.

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.

Inference of the Paramentes of a Bioreactor via Approximate Bayesian Evidence

In the context of the recent water crisis, the development and improvement of technologies for efficiently treating wastewater becomes increasingly necessary. Aligned with this, the activated sludge process is widely used in biological wastewater treatment plants to remove carbon and nutrients from wastewater. Although significant advancements have been achieved in recent years in modelling these processes to improve their performance and energy efficiency, the models are still complex and difficult to calibrate using data from real plants. In this work, we use Bayesian inference frameworks to estimate the parameters of the activated sludge process. We employ Gaussian processes to estimate the solution to the differential equations of the activated sludge process model and we fit this estimation to manufactured data of the observations of the wastewater. We find that the use of the Bayesian inference framework outperforms the classical approach in scenarios where measurements are more strongly affected by noise.

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).

Hybrid supervised hierarchical control of a biological wastewater treatment plant.

In wastewater treatment intensification, hierarchical control structures are developed to improve the plant's performance. This paper proposes two novel hybrid supervised hierarchical control structures for specifying the dissolved oxygen concentration in the last aerobic reactor of the wastewater treatment plant (WWTP) based on the nitrification rate and the ammonia level in this reactor. These structures combine the optimum disturbance rejection PI control (OPI), adaptive neuro-fuzzy inference system (ANFIS), and genetic algorithms (GA) to reduce energy consumption and operational costs, improve effluent quality, and reduce the number and percentage of times the established maximum concentration of pollutants in the effluent of the WWTP is violated. The proposed control strategy is implemented and evaluated using benchmark simulation model no. 1 (BSM1). The OPI-ANFIS-GA configuration significantly enhances effluent quality in dry, rainy, and stormy weather conditions, reducing total nitrogen violations by 50.17%, 63.35%, and 47.35%, respectively. Then, 6.79% and 7.12% of aeration energy and 1.44% and 1.46% of operational costs are reduced in dry and rain weather conditions. The OPI-ANFIS configuration enhanced significant energy savings and a cost reduction in storm weather conditions. Both configurations led to a 49.89% decrease in total suspended sludge (TSS) during stormy weather conditions. The proposed controller significantly improves the performance of the WWTP in all weather scenarios compared to the default controller and similar controllers found in the literature.

Bacterial community and filamentous population of industrial wastewater treatment plants in Belgium.

The discharge of industrial water requires the removal of its pollutants, where biological wastewater treatment plants (WWTPs) are the most used systems. Biological WWTPs make use of activated sludge (AS), where bacteria are responsible for the removal of pollutants. However, our knowledge of the microbial communities of industrial plants is limited. Understanding the microbial population is essential to provide solutions to industrial problems such as bulking. The aim of this study was to identify at a high taxonomic resolution the bacterial population of 29 industrial WWTPs using 16S rRNA amplicon sequencing. Our results revealed that the main functional groups were dominated by Thauera and Zoogloea within denitrifiers, Dechloromonas in phosphate-accumulating organisms, and Defluviicoccus in glycogen-accumulating organisms. The activated sludge characterization indicated that 59% of the industrial plants suffered from bulking sludge, with DSVI values of up to 448 mL g-1. From the bulking cases, 72% corresponded to filamentous bulking with Thiothrix as the most abundant filament; meanwhile, the other 28% corresponded to viscous bulking sludge in which Zoogloea was the most abundant genus. Furthermore, the bacterial population did not share a core of taxa across all industrial plants. However, 20 genera were present in at least 50% of the plants comprising the general core, including Thauera, Ca. Competibacter, and several undescribed microorganisms. Moreover, statistical analysis revealed that wastewater salinity strongly affected the microbial richness of the industrial plants. The bacterial population across industrial plants differed considerably from each other, resulting in unique microbial communities that are attributed to the specificity of their wastewaters. KEY POINTS: • The general core taxa of industrial plants were mostly made up of undescribed bacterial genera. • Filamentous bacteria constituted on average 4.1% read abundance of the industrial WWTPs. • Viscous bulking remains a significant type of bulking within industrial WWTPs.

DEVELOPMENT OF CONTROL AND FORECASTING SYSTEM FOR THE EFFECTIVE FUNCTIONING OF BIOLOGICAL WASTEWATER TREATMENT PLANTS IN THE CITY OF VITEBSK BASED ON NEURAL NETWORKS

The relevance of implementing modern approaches to improving the efficiency of sewage treatment facilities has been analyzed. Functional modeling of biological wastewater treatment has been performed using the IDEF0 methodology. It has allowed the identification of the nomenclature of input and control factors, mechanisms, and the results of corre-sponding technological processes. A block diagram of information flows in the context of control of wastewater treatment facilities is constructed. Correlation analysis of the relationships between wastewater quality parameters has been con-ducted, and expert opinions have justified the further use of neural networks for modeling the processes of purification of aqueous solutions. Neural model has been built on the Deductor analytical platform for forecasting the functioning of biological wastewater treatment plants.

Forecasting the dynamics of a complex microbial community using integrated meta-omics

Predicting the behaviour of complex microbial communities is challenging. However, this is essential for complex biotechnological processes such as those in biological wastewater treatment plants (BWWTPs), which require sustainable operation. Here we summarize 14 months of longitudinal meta-omics data from a BWWTP anaerobic tank into 17 temporal signals, explaining 91.1% of the temporal variance, and link those signals to ecological events within the community. We forecast the signals over the subsequent five years and use 21 extra samples collected at defined time intervals for testing and validation. Our forecasts are correct for six signals and hint on phenomena such as predation cycles. Using all the 17 forecasts and the environmental variables, we predict gene abundance and expression, with a coefficient of determination ≥0.87 for the subsequent three years. Our study demonstrates the ability to forecast the dynamics of open microbial ecosystems using interactions between community cycles and environmental parameters.

Application of machine learning algorithms and feature selection methods for better prediction of sludge production in a real advanced biological wastewater treatment plant

Although the management of sewage sludge is an important and challenging task of wastewater treatment, there is a scarcity of studies on the prediction of waste sludge. To overcome this deficiency, the present work aims to develop an appropriate model providing accurate and fast prediction of sewage sludge. With this aim, different machine learning (ML) algorithms were tested by data obtained from a real advanced biological wastewater treatment plant located in Kocaeli, Turkey. In modelling studies, a data set from January 2022 to December 2022 composed of 208 daily measurements was considered. The flow rate of the plant (Q), polyelectrolyte dosage (PD) and removed amounts of total suspended solids (TSS), chemical oxygen demand (COD), biological oxygen demand (BOD), total phosphorous (TP), total nitrogen (TN) were assigned as input parameters to predict sludge production (SP). The precision of the models was evaluated in terms of Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and correlation coefficient (R2). Among the various tested models Kernel Ridge Regression provided the best accuracy with R2 value of 0.94 and MAE value of 3.25. Mutual information-based feature selection (MIFS) and correlation-based feature selection (CFS) algorithms were also used in the study in order to enhance the model performance. Thus, higher prediction accuracies were achieved using the selected subset of features. Furthermore, importance contribution of features were calculated and visualized by SHapley Additive exPlanations (SHAP) technique. The overall results of the work indicate the feasibility of ML models for describing the dynamic and complex nature of SP. The process operators may benefit from this modelling approach since it enables accurate and fast estimation of sewage sludge by using fewer and easily measurable parameters.

Fenton oxidation treatment of benzalkonium chlorides to prevent antibiotic resistance development in non-acclimated activated sludge system

Improper discharge of benzalkonium chlorides (BAC) to sewage and wastewater treatment plants can cause acute intoxication and chronic low-dose exposure, which can lead to changes in microbial communities and can promote the development of antibiotic resistance. The aim of this study was to develop a feasible treatment strategy based on the homogeneous Fenton oxidation to minimize the negative impact of BAC-containing wastewater on activated sludge systems. The effect of initial iron and oxidant concentrations was analyzed. After an initial Fenton oxidation stage (180 min), the solution needed to be stored at ambient temperature and in the dark for some time to remove residual oxidizing agents and promote further oxidation. The 0.1 % w/v-BAC solution treated at best operating conditions, i.e., homogeneous Fenton for 180 min at 30 °C, pH0 = 3, stoichiometric oxidant dose, [Fe+2]0:[H2O2]0 = 0.011, and stored during 4 days, led to BAC conversion of 99.9 %, no residual H2O2, no sludge, and DOC conversion of 40.8 %. An increase in metabolic activity was observed when non-acclimated activated sludge was exposed to the treated solution. Treated solution showed biodegradation without increasing resistance to Ampicillin, Cephalexin, or Ciprofloxacin. Evaluation of the effect of water matrix (tap or distilled) showed that the composition of tap water enhanced the oxidation extent, resulting in a 15 % increase in mineralization and a further reduction of COD. This study provides insights into the treatment of wastewater containing BAC and its safe discharge to sewage systems or biological wastewater treatment plants.

Microplastics in advanced biological wastewater treatment plant of Kocaeli, Turkey: point source of microplastics reaching Marmara Sea

Microplastics in advanced biological wastewater treatment plant of Kocaeli, Turkey: point source of microplastics reaching Marmara Sea

The water footprint assessment for advanced biological wastewater treatment plant

The water footprint assessment for advanced biological wastewater treatment plant

High-Performance Catalytic Wet Oxidation of Excess Activated Sludge Derived from Pharmaceutical Wastewater Treatment Process over a Cu/γ-Al2O3 Catalyst

The performance of catalytic wet oxidation of excess activated sludge derived from pharmaceutical wastewater treatment over a Cu/γ-Al2O3 catalyst was investigated. The experiments were performed with a stainless steel autoclave reactor by using the prepared Cu/γ-Al2O3 catalyst. The effects of reaction conditions were examined, including additional catalyst amount, reaction temperature, time, and initial oxygen pressure. Results demonstrated that the catalyst, fabricated via co-precipitating method, has excellent catalytic performance. Through the study on condition optimization, the highest removal rates of volatile suspended solids (VSS, 93.6%) and total chemical oxygen demand (TCOD, 76.5%) were acquired with the reaction temperature 260 °C, time 60 min, initial oxygen pressure 1.0 MPa, and the prepared catalyst 5.0 g/L. The volatile fatty acids (VFAs) produced from the wet oxidation of sludge included acetic (mainly), propanoic, isobutyric, and isovaleric acids, which have great potential for the utilization as organic carbon sources in biological wastewater treatment plant. These results demonstrated that the proposed method, catalytic wet oxidation over Cu/γ-Al2O3 catalyst, is effective for treating excess activated sludge and resource utilization of organic carbon in the sludge.

Bulking of activated sludge in the biological treatment of municipal and industrial wastewater, due to the massive development of filamentous bacteria Type 021N

A research of the causes and consequences of bulking of activated sludge was conducted at existing biological wastewater treatment plants (municipal and local at an industrial enterprise). The filamentous bacterium that was caused the bulking of the activated sludge in the studied treatment plants was identified as Type 021N. It was established that the studied filamentous bulking causes a significant deterioration of the technological characteristics of activated sludge flocs and it is accompanied by suppression of the nitrification process. To suppress the bulking of activated sludge two methods of suppressing the inflow of hydrogen sulfide into biological treatment plants were successfully used: blowing from wastewater at municipal treatment plants and inhibiting its formation at local treatment plants in the settling and laminar flow zones.

Achieving partial nitrification by harnessing basic hydrolysis of sulphide salts amid high dissolved oxygen conditions

Attempts are going on to incorporate sulphur into the carbon and nitrogen removal processes at a biological wastewater treatment plant by external dosing or its intrinsic presence. A possible form of dosing sulphide at the field scale is the external dosing as the sodium salt of sulphide which under basic hydrolysis increases the pH. We demonstrated that the sodium salt dosage of sulphide can also be useful in achieving partial nitrification (PN). The results showed that 15 mgS/L of the sodium salt of sulphide was sufficient to establish PN in the low-strength synthetic and real wastewater under adequate dissolved oxygen concentrations (1.3 ± 0.4 mg/L during discharge phase of SBR cycle to 5.3 ± 0.2 mg/L during aeration phase of SBR cycle). PN was achieved only during the operational phases when the pH rise due to the basic hydrolysis of sodium sulphide was not controlled. The dosage of sulphide as its sodium salt has dual advantages as an inhibitor to nitrite oxidising bacteria (NOB). The activity of NOBs was 10–20 mgN/gVSS/d during the operational phases when PN was effective. The long-term operation showed a nitrite accumulation ratio of 70 ± 19% with an effluent ammonia concentration of 19 ± 4 mgN/L and nitrite concentrations of 18 ± 4 mgN/L. At the genus level, the NOB communities Nitrospira and Nitrobacter diminished in the long-term phases of stable PN with real wastewater. NOB communities evolved again when sulphide dosage was stopped indicating this strategy needs to be adopted continuously to achieve PN not time-based. The study demonstrated a strategy to achieve the feasibility of sulfide-driven PN systems and has a potential application in nitrogen removal from domestic wastewater.

Assessment of the Role of Photovoltaic Systems in Reducing the Carbon Footprint of Wastewater Treatment Plants

<p>Wastewater treatment plants (WWTPs) consume large amounts of energy and thus cause an increase in carbon footprint. For this reason, it has become important not only to meet the discharge criteria in treatment plants, but also to reduce the carbon footprint resulting from treatment processes and energy use. In this study, the effect of supplying the energy required by a real domestic biological wastewater treatment plant from a photovoltaic (PV) system on the reduction of its carbon footprint was investigated. For this purpose, the annual energy consumption profile of the plant was prepared, and direct emissions from treatment processes and indirect emissions from electricity consumption were calculated for 2020 and 2021. Indirect emissions contribute 54% and 69% to the total carbon footprint of the plant for 2020 and 2021, respectively, while direct emissions contribute 46% and 31%. With the partial transition of the plant to a PV system in 2021, annual electricity consumption decreased by 401,000 kWh/year and the carbon footprint decreased by 21% to 819 tCO2e. In this way, the plant also achieved 40% economic savings. If the plant meets all the energy it needs from the PV system, it will reduce its carbon footprint by 45%.</p>

Additional Treatment of Nitrogen and Phosphorus Using Natural Materials in Small-Scale Domestic Wastewater Treatment Unit

The conventional wastewater treatment methods only remove up to 80% of total nitrogen (N) or phosphorus (P) from wastewater, so additional facilities are needed. This article describes a newly created other wastewater treatment unit (NCU) that increases the effectiveness of P and N removal of the small-scale biological wastewater treatment plant. This work aims to evaluate the capacity of simultaneous elimination from wastewater nitrogen (NH4-N, NO3-N) and phosphorus (PO4-P) by adsorption. NCU was filled with the sorbent material zeolite (clinoptilolite) and OCS (Fe, Mn, Ca oxides coated sand). After treatment in the main plant, wastewater flows through the NCU without using electric power. A compact system consisting of a main treatment plant and the NCU worked for 4 months, as the harmonized European Standard EN 12566-7 recommended. The NCU unit reduced PO4-P, NH4-N, and NO3-N concentrations in the effluent (74–98%, 52–99%, and 50–98%, respectively). In general, the small-scale system treated wastewater did not contain more than 1 mg/L phosphorus concentration and not more than 10 mg/L nitrogen concentration. This study demonstrates that treatment in NCU is an ecological and environmentally friendly method suitable for decentralized wastewater treatment.

Machine learning approaches for data-driven process monitoring of biological wastewater treatment plant: A review of research works on benchmark simulation model No.1(BSM1).

In the past decade, machine learning techniques have seen wide industrial applications for design of data-based process monitoring systems with an aim to improve industrial productivity. An efficient process monitoring system for wastewater treatment process (WWTP) ensures increased efficiency and effluents meeting stringent emission norms. Benchmark simulation model No. 1 (BSM1) provides a simulation platform to researchers for developing efficient data-based process monitoring, quality monitoring, and process control systems for WWTPs. The present article presents a review of all research works reporting applications of various machine learning techniques for sensor and process fault detection of BSM1. The review focuses on process monitoring of biological wastewater treatment process, which uses a series of aerobic and anaerobic reactions followed by secondary settling process. Detailed information on various parameters monitored, different machine learning techniques explored, and results obtained by different researchers are presented in tabular and graphical format. In the review, it was observed that principal component analysis(PCA) and its variants account for the maximum number of research works for process monitoring in WWTPs and there are very few applications of recently developed deep learning techniques. Following the review and analysis, various future scopes of research (such as techniques yet to be explored or improvement of results for a particular fault) are also presented. These information will assist prospective researchers working on BSM1 to take forward the research.

Application of Immobilized Biocatalysts in the Biotransformation of Non-Steroidal Anti-Inflammatory Drugs

Among the micropollutants identified in the environment, non-steroidal anti-inflammatory drugs (NSAIDs) dominate more and more often. This is due to both the high consumption and low efficiency of biological wastewater treatment plants, where the initial transformation of NSAIDs most often takes place. The solution to the problem may be using preparations supporting activated sludge in sewage treatment plants in the biodegradation of NSAIDs. Therefore, the research aimed to develop a biopreparation stimulating the activated sludge of the sewage treatment plant to decompose paracetamol and selected NSAIDs. This biopreparation is based on strains of Stenotrophomonas maltophilia KB2, Planococcus sp. S5, Bacillus thuringiensis B1(2015b), and Pseudomonas moorei KB4 immobilized on a plant sponge. As a result of the tests, it was shown that the optimal species composition of the proposed preparation includes all tested strains immobilized on a carrier with a mass of 1.2 g/L. The system optimization showed that the optimal amount of strains on the carrier was 17 mg/g of the carrier, 15 mg/g of the carrier, 18 mg/g of the carrier, and 20 mg/g of the carrier for KB4, B1(2015b), KB2, and S5, respectively. The presence of phenol stimulated the degradation of the tested drugs, and this effect deepened with increasing phenol concentration. At the same time, the degradation rate of the mixture of NSAIDs in the presence of phenol did not depend on the amount of biomass. The lack of inhibition in the presence of an additional co-contaminant, i.e., phenol, indicates that the preparation constructed in this way has a chance of being used in sewage treatment plant systems, where introduced strains are exposed to various aromatic compounds.