Real Wastewater Treatment Plant Research Articles

Intelligent aeration amount prediction control for wastewater treatment process based on recurrent neural network

The use of machine learning in artificial intelligence to solve industrial problems has been a current trend. Predictions can be made by machine learning algorithms. This greatly improves the efficiency and also the accuracy. With the development of industrialization, the pollution of water resources is becoming more and more serious. How to treat wastewater more cost-effectively to meet the discharge standards has become one of the most urgent problems in the world. In the Anaerobic-Anoxic-Aerobic (AAO) process, the key to reach the standard of sewage treatment with low cost and high efficiency lies in the aeration link. In this study, multiple machine learning methods are used for modeling, and proposed a method employing the long short-term memory (LSTM) neural network model for regression prediction addresses the need for a more accurate prediction approach. By utilizing data from the preceding 100 days, this model replaces manual, experience-based prediction methods, thereby mitigating energy consumption. This model is optimized by training and testing with real wastewater treatment plant data and continuously adjusting the parameters through multiple sets of experiments. This can make the real recorded aeration amount and the actual aeration amount infinitely close. Through the experimental comparison, it is found that the performance of the LSTM neural network model is better, with about 14% higher accuracy than the benchmark model.

Development of a sunlight adjustable parabolic trough reactor for 24-hour efficient photocatalytic wastewater treatment

Solar energy is the ideal energy source for activating photocatalysis. However, due to the solar motion, achieving 24-hour solar-powered efficient photocatalytic reactions remains a challenge. In this study, a Sunlight Adjustable Parabolic Trough Reactor (SA-PTR) integrated with flexible parabolic mirror and solar panel was developed. During the daytime, flexible parabolic mirror could be rolled up under sunny condition or expanded under cloudy condition, thus providing optimal light irradiance (1500 W·m−2) and temperature (rises yet below 55 ℃) for photocatalysis. Additionally, the solar panel behind the flexible parabolic mirror could generate solar-electricity. At night, the stored solar-electricity could meet 100 % power demands of UV lamp, enabling a 24-hour solar-powered photocatalytic process. Compared with traditional parabolic trough reactor and inclined plate collector, SA-PTR exhibited higher treatment efficiency against carcinogenic organic dyes (Rhodamine B, Methylene Blue, Methyl Orange), antibiotic (Tetracycline) and pathogenic bacteria (Escherichia coli) during day and night. Moreover, SA-PTR exhibited superior global deployability, economic advantage, safety and lifetime. Therefore, SA-PTR, as a novel solar controllable 24-hour operational photoreactor, is promising to be employed in real wastewater treatment plant.

Novel extended hybrid tool for real time control and practically support decisions to reduce GHG emissions in full scale wastewater treatment plants

In this paper, a novel methodology and extended hybrid model for the real time control, prediction and reduction of direct emissions of greenhouse gases (GHGs) from wastewater treatment plants (WWTPs) is proposed to overcome the lack of long-term data availability in several full-scale case studies. A mechanistic model (MCM) and a machine learning (ML) model are combined to real time control, predict the emissions of nitrous oxide (N2O) and carbon dioxide (CO2) as well as effluent quality (COD – chemical oxygen demand, NH4-N – ammonia, NO3-N - nitrate) in activated sludge method. For methane (CH4), using the MCM model, predictions are performed on the input data (VFA, CODs for aerobic and anaerobic compartments) to the MLM model. Additionally, scenarios were analyzed to assess and reduce the GHGs emissions related to the biological processes. A real WWTP, with a population equivalent (PE) of 125,000, was studied for the validation of the hybrid model. A global sensitivity analysis (GSA) of the MCM and a ML model were implemented to assess GHGs emission mechanisms the biological reactor. Finally, an early warning tool for the prediction of GHGs errors was implemented to assess the accuracy and the reliability of the proposed algorithm. The results could support the wastewater treatment plant operators to evaluate possible mitigation scenarios (MS) that can reduce direct GHG emissions from WWTPs by up to 21%, while maintaining the final quality standard of the treated effluent.

Micropollutant elimination by sustainable technologies: Coupling activated carbon with solar photo-Fenton as pre-oxydation step

This study evaluates the effectiveness of coupling solar photo-Fenton (SPF) with granular activated carbon (GAC) filtration to remove three selected micropollutants (benzotriazole, carbamazepine and diclofenac) from municipal wastewater treatment plant (WWTP) effluent. SPF was first studied stand-alone at natural pH with simulated and real irradiation in raceway pond reactors (RPR) in different water matrices. The scenario with the poorest removal was the use of real WWTP effluent as water matrix; the removal achieved was 25 % of benzotriazole, 57 % of carbamazepine and up to 80 % of diclofenac (6 mg L−1 of iron, 40 mg L−1 of H2O2, 15 cm depth, 0.49 kJ L−1). In the second phase, fresh and regenerated GAC were studied in different water matrices, showing that the complex matrices had a higher influence on the regenerated GAC. Thus, the breakthrough of regenerated GAC was achieved quicker than fresh GAC. The aim of the work was to extend the GAC lifetime by a pre-oxidation process; thus, SPF was applied for 5 minutes (0.05–0.08 kJ L−1) as a first step. GAC lifetime was extended between 2 and 12 times thanks to the pre-treatment by SPF, equaling the lifetime of the regenerated GAC to the fresh one in the more complex matrix.

Natural magnetite as an effective and long-lasting catalyst for CWPO of azole pesticides in a continuous up-flow fixed-bed reactor

The global occurrence of micropollutants in water bodies has raised concerns about potential negative effects on aquatic ecosystems and human health. EU regulations to mitigate such widespread pollution have already been implemented and are expected to become increasingly stringent in the next few years. Catalytic wet peroxide oxidation (CWPO) has proved to be a promising alternative for micropollutant removal from water, but most studies were performed in batch mode, often involving complex, expensive, and hardly recoverable catalysts, that are prone to deactivation. This work aims to demonstrate the feasibility of a fixed-bed reactor (FBR) packed with natural magnetite powder for the removal of a representative mixture of azole pesticides, recently listed in the EU Watch Lists. The performance of the system was evaluated by analyzing the impact of H2O2 dose (3.6–13.4 mg L−1), magnetite load (2–8 g), inlet flow rate (0.25–1 mL min−1), and initial micropollutant concentration (100–1000 µg L−1) over 300 h of continuous operation. Azole pesticide conversion values above 80% were achieved under selected operating conditions (WFe3O4 = 8 g, [H2O2]0 = 6.7 mg L−1, flow rate = 0.5 mL min−1, pH0 = 5, T = 25 °C). Notably, the catalytic system showed a high stability upon 500 h in operation, with limited iron leaching (< 0.1 mg L−1). As a proof of concept, the feasibility of the system was confirmed using a real wastewater treatment plant (WWTP) effluent spiked with the mixture of azole pesticides. These results represent a clear advance for the application of CWPO as a tertiary treatment in WWTPs and open the door for the scale-up of FBR packed with natural magnetite.

Role of Flexible Operation of a Wastewater Treatment Plant in the Reduction of Its Indirect Carbon Dioxide Emissions—A Case Study

The increasing share of renewables in electricity grids comes with a challenge of energy surpluses and deficits, which needs be handled by demand side management (DSM) and storage options. Within this approach, wastewater treatment plants (WWTPs), with flexible energy consumption and production processes and storage units, can contribute to stabilizing the grids and integrating more renewables. In this study, the operation of a real WWTP was optimized by mixed integer linear programming (MILP) to minimize its indirect carbon dioxide (CO2) emissions. The operation of the WWTP was shown to be flexible in following the CO2 emission factor of the electricity grid, which was possible with the utilization of the WWTP’s storage units and flexible co-substrate feeding. As a result, by changing only the operational behavior of the WWTP, its indirect CO2 emissions decreased by 4.8% due to the higher share of renewables in the electricity grid. The CO2 emissions were shown to decrease further up to 6.9% by adding virtual storage units.

Degradation of the cytostatic drug bicalutamide by photo-assisted catalytic wet peroxide oxidation

The presence of cytostatic drugs, such as bicalutamide (BICA), in aquatic compartments reveals the necessity of developing efficient processes for their elimination in wastewater treatment plants (WWTP). This work explores for the first time the use of natural magnetite as a low-cost catalyst in the photo assisted — catalytic wet peroxide oxidation (UV-CWPO) of BICA. For this purpose, real WWTP secondary effluents were spiked with 100 μg/L BICA and subjected to UV-CWPO. Preliminary runs comparing UV photolysis, UV/H2O2, CWPO and UV-CWPO confirmed the synergistic effect of UV radiation and the CWPO process employing magnetite. Afterwards, a parametric study was performed evaluating the influence of the pH, catalyst load, H2O2 dosage, temperature and water matrix. Under optimum operating conditions (pH 0: 6, [magnetite]: 375 mg/L and [H2O2]: 75 mg/L), 99.7 % BICA degradation and 60.7 % mineralization were reached after 30 min in WWTP effluent. UV-CWPO also proved to be effective in hospital wastewater. Catalyst activity was studied in 5 consecutive runs, showing great stability for both BICA removal and TOC elimination. Finally, phytotoxicity tests revealed a lower toxicity in the UV-CWPO effluent compared to the untreated stream. Hence, UV-CWPO proved to be a feasible to eliminate risky cytostatic drugs in relevant environmental matrices.

Comparison of benzotriazole ultraviolet stabilizers (BUVs) removal from wastewater after subsequent stages of sequencing batch reactor (SBR) treatment process

The research focused on benzotriazole ultraviolet stabilizers (BUVs) which are commonly used compounds despite being found dangerous, e.g. promoting breast cancer cell proliferation, damaging vital organs such as hearts, brains livers and kidneys. The aim of the study was to analyse the efficiency and removal rate of BUVs from wastewater depending on the quantity of tested compounds and SBR anaerobic-aerobic conditions. The study was conducted in sequencing batch reactors (SBRs - 17 L) with real flocculent activated sludge (8 L) and model wastewater (5 L) containing UV-326, UV-327, UV-328, UV-329 and UV-P from 50 to 600 μg∙L−1. The SBR were operated in 390 cycles of 7 h and 10 min over 130 days. The similarity of the technological parameters of the treatment process to those used in a real wastewater treatment plant was maintained. Efficiency removal of individual BUVs was strictly dependent on the dose of compounds introduced into wastewater and ranged from 68.2 to 97 %. Removal of UV-329 occurred with lowest efficiency (from 68.2 to 85.2 %) while UV-326 was most efficiently removed from the wastewater (from 94.1 to 97 %). UV-329 was removed from wastewater with the lowest (0.0968–0.9524 μg∙L−1∙min−1) average removal rate while UV-327 with the highest (0.16–1.3357 μg∙L−1∙min−1), irrespective of BUVs dose in the influent. Secondary release of BUVs into the wastewater occurred in SBR during the settling phase and was dependent on the type and concentration of the BUVs in the raw wastewater. This occurrence was noted for UV-326 ≥ 100; UV-327 = 600; UV-328 ≥ 200; UV-329 ≥ 50 and UV-P ≥ 100 μg∙L−1. The settling phase needs to be shortened to the required minimum. This is an important conclusion for WWTPs in regards to SBR cycle duration and technological parameters of the treatment process.

Photodegradation of thiophanate-methyl under simulated sunlight by utilization of novel composite photocatalysts

This work aimed to investigate the influence of modified titanium(IV) oxide by different nanosized particles on photocatalytic capacity to decompose the chosen organic pollutant under simulated sunlight. For that purpose, rutile-phased titanium(IV) oxide (r-TiO2) was decorated with iron vanadate (FeVO4/r-TiO2) and vanadium-substituted goethite (Fe1-xVxOOH/r-TiO2). The obtained composites were characterized by field emission scanning electron microscopy, energy-dispersive X-ray spectroscopy, X ray powder diffraction, Brunauer-Emmett-Teller, Fourier transform infrared spectroscopy - attenuated total reflec-tance and ultraviolet-visible diffuse reflectance spectroscopy techniques. Both synthesized photocatalysts showed higher photoactivity than the base r-TiO2 for the degradation of the target contaminant - thiophanate-methyl (2.5 h vs. 5 h). During the tests, parameters like the irradiation time, catalysts amount, and pesticide concentration were systematically investigated. Furthermore, photocatalysts were applied in multicycle degradation tests for examining their effectiveness during exploitation time. Monitoring of the removal rate was performed both by UV/visible spectrometry and high-performance liquid chromatography (HPLC). In order to prove completion of fungicide degradation chemical oxygen demand was measured in the course of the photocatalytic experiment. The final concentration of the observed contaminant in treated samples was under the prescribed legislative level. The fabricated materials displayed great reliability, durability and photocatalytic activity repre-senting good potentials for implementing this process in real wastewater treatment plants.

Prediction of the removal of solid suspensions and chemical oxygen demand from a pharmaceutical wastewater plant using a neural network approach

This study aimed to model the removal efficiency of chemical oxygen demand (COD) and solid suspension (SS) from a real pharmaceutical wastewater treatment plant (WWTP) using artificial neural network-multilayer perception (ANN-MLP). The ANN model was developed using experimental data which were collected during four years. The input variables of the neural network are water pH, temperature, SS, and COD. The percentages of removal of COD (CODRE) and of SS (SSRE) are considered as output variables. The Levenberg–Marquardt algorithm was utilized to train ANN. It was found that the ANN architecture has two hidden layers with 8 neurons. The results of ANN models were compared with the measured data based on the correlation coefficient (R2) and mean square error (MSE). It was noticed that the best ANN model provides good accurate results with R2 values of 0.9783 for SSRE and 0.9826 for CODRE, and a value of MSE equal to 1.695 10−3. This study may aid the users to adjust operational parameters in recovering COD and SS in the case of the process treatment of industrial effluents.

The Biofactory: Quantifying Life Cycle Sustainability Impacts of the Wastewater Circular Economy in Chile

The wastewater circular economy (WW-CE) represents a solution to improving sanitation coverage and management worldwide. However, the transition to circular wastewater treatment plants (WWTPs) requires facilitation to enhance decision-makers’ understanding of the integral sustainability impacts of the WW-CE. This research implemented a Life Cycle Sustainability Assessment (LCSA), combining Life Cycle Assessment, Social Life Cycle Assessment and Life Cycle Costing with a Multi-criteria Decision Making (MCDM) model to quantify the environmental, social, and economic impacts of different WWTPs technologies. Two real WWTPs (Plant A and Plant B) in Chile have embraced alternative WW-CE configurations, adopting the title Biofactories, and are considered as case studies in this investigation. A comparative LCSA considered the service of a 1,000,000-population equivalent, under three scenarios: wastewater discharge without treatment, conventional WWTPs, and biofactory WW-CE configurations. The results demonstrate that the transition to WW-CEs improved integral sustainability, and decreased integrated environmental, social, and economic impacts by 30% in Plant A, demonstrating better performance in terms environmental and social impacts. However, a 58% decrease in integral sustainability impacts for Plant B was achieved via the economic advantage of the thermal hydrolysis pre-treatment of sludge. The urgent need to adopt sustainable decision-making models to improve sanitation coverage and sustainability performance of the sanitation industry across the globe is discussed. The WW-CE in Chile presents an opportunity for this to be achieved.

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.

Assessment of Denitrification and Nitrification Processes during Landfill Leachate Treatment

Landfill leachate is generated from waste degradation in landfill sites, and its treatment includes biological or combined treatment with physico-chemical methods. A frequently applied technology in the biological stage of landfill leachate treatment plants is based on denitrification and nitrification. Nevertheless, with the availability of a huge number of scientific reports, the management of denitrification and nitrification (D/N) processes in the frame of real wastewater treatment plants (WWTPs) is rather difficult and always remains a critical technological problem. This study aims to perform a comparative assessment between denitrification and nitrification processes during landfill leachate treatment in three different situations in the WWTP of the Municipal Enterprise for Waste Treatment in Sofia City, Bulgaria. The comparative assessment is based on the biotransformation effectiveness of ammonium ions in the course of nitrification and of nitrates in the course of denitrification. Mixed samples (wastewater and activated sludge) were taken from the sequencing batch reactors operating in the mode of denitrification and nitrification. All physico-chemical (pH, dissolved oxygen, organics concentration, BOD5:COD, nitrogen and phosphorous) and biological (sludge volume index, sludge biotic index, quantity of aerobic heterotrophs, denitrifiers and nitrifiers, total dehydrogenase activity) indicators were investigated in the samples from the biobasins in the mode of denitrification and nitrification. The conditions for the implementation of the two processes were compared, along with the state of the activated sludge in the different reactors. The obtained results showed that denitrification was the critical process in the technology on the base of D/N. A major factor that managed the denitrification was the lower concentration of biodegradable organic matter which led to a deformation of the activated sludge structure, a decrease in the count of heterotrophic microorganisms, a decrease in the total activity of the activated sludge and an inhibition of the nitrate reductase activity. The nitrification processes were accomplished with high intensity. The results confirmed that D/N could be optimized and controlled using specific wastewater treatment technology parameters and purposely applied indicators.

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

&lt;p&gt;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%.&lt;/p&gt;&#x0D;

Simultaneous degradation of contaminants of emerging concern and disinfection by solar photoelectro-Fenton process at circumneutral pH in a solar electrochemical raceway pond reactor

Simultaneous contaminants of emerging concern (CECs) removal and wild microorganisms’ inactivation was evaluated by applying solar photoelectro-Fenton (SPEF) process in actual secondary effluent collected from a real municipal wastewater treatment plant (MWWTP). 20 L of a mixture of four CECs was used as model pollutants (200 μg/L of acetaminophen, caffeine, sulfamethazine, and sulfamethoxazole each one). The SPEF process was carried out on fully sunny days, at circumneutral pH using the complex Fe3+-EDDS, in a solar electrochemical – raceway pond reactor (SEC-RPR). Initially, the optimal conditions for CECs degradation were determined using a response surface model based on current density, iron complex concentration and Fe3+-EDDS addition time (to allow previous accumulation of H2O2) as model inputs. A current density of 24.6 mA/cm2, a Fe3+-EDDS complex concentration of 0.089 mM and 3.8 min of previous H2O2 accumulation were the resulting optimum conditions that were afterwards applied for the simultaneous degradation of the CECs synthetic mixture and wild microorganisms inactivation in actual secondary effluent. About 85% CECs removal and complete E. coli inactivation were achieved in 30 min, approximately, while E. faecalis and total coliforms could be inactivated under detection limit in 60 min and 75 min, respectively.

DNN model development of biogas production from an anaerobic wastewater treatment plant using Bayesian hyperparameter optimization

Deep neural networks have been regarded as accurate models to predict complex fermentation processes due to their capacity to learn from a high number of data sets via artificial intelligence. To enhance the performance of these models the main challenge is to select the appropriate hyperparameters, such as neural network architecture (number of hidden layers and hidden units), activation function, optimizer, learning rate, and others.This study aims to use a hybrid Bayesian optimization with tree-structured Parzen estimator (BO-TPE) to predict biogas production from real wastewater treatment plant data using deep neural network machine learning (DNN) with optimized hyperparameters. Due to the high number of missing sample measurement records, the data preprocessing process has been performed in three sequential steps: the first step is to remove columns with a high percentage of missing values. The second step concerns removing rows with a high number of missing values. The remaining missing values in the dataset were removed in the third step using the dropna function from Pandas' library. Then, on the remaining data, three normalization techniques (MinMaxScaler, RobustScaler, and StandardScaler) were used for 16 relevant features of the anaerobic digestion process (AD) and compared with Non-Normalized data. The RobustScaler technique demonstrated good prediction capabilities of biogas volume produced. The maximum predicted volume was 2236.105 Nm3/d, with the coefficient of determination (R2), the mean absolute error (MAE), and the root mean square error (RMSE) values of 0.712, 164.610, and 223.429, respectively. This value remains slightly higher than the actual biogas volume produced in the wastewater treatment plant, which was 2131 Nm3/d. Adopting StandardScaler, MinMaxScaler, and Non-Normalized data provided statistical performance indicator values of (0.690; 170.518; 231.519), (0.679; 180.575; 235.648), and (0.647; 183.403; 247.203), respectively. These findings offer an autonomous strategy to monitor the effective operational variables of a large-scale digester, ensuring the stability of the complex fermentation process as well as the long-term sustainability and economic viability of the renewable energy approach in the sector of waste treatment.

Static calibration of wastewater treatment plant models: Investigating calibration processes and objective functions

Mathematical modeling provides a reliable tool for optimal management and operation of wastewater treatment plants (WWTPs). Accurate calibration is crucial for producing reliable results, and static calibration is a critical initial step for this purpose, ensuring enough accuracy and minimizing later difficulties. The two main areas to investigate in static calibration are the appropriate calibration process and objective function. This study proposes a three-stage methodology for static calibration of model parameters. Nine different calibration objective functions considering various target variables are investigated to evaluate how different objective functions can affect the calibration process accuracy. The proposed approach is applied in a real WWTP with a carousel oxidation ditch for secondary treatment. Benchmark Simulation Model No.2 (BSM2) is modified to be used for oxidation ditch modeling. The calibration results show less than 3 % prediction error for COD, TN, and TSS, and 6.7 % mean overall error. The validation resulted in 22.5 %, 10.6 %, and 31.5 % MARE (Mean Absolute Relative Error) for effluent COD, TN, and, TSS, respectively, indicating successful calibration. Comparing different objective functions indicates that in static calibration, the objective functions that consider more targets usually yield more accurate results in a relatively acceptable time.

Dynamic Optimal Control for Wastewater Treatment Process Under Multiple Operating Conditions

Wastewater treatment process (WWTP) is operated with multiple conditions. Accurately identify these conditions and satisfy the different requirements are the keys to guarantee the optimal operation of WWTP. To solve this problem, a dynamic optimal control (DOC) strategy is designed in this paper. First, with the aid of the predicting models by adaptive fuzzy neural network for effluent nitrate and total nitrogen, different operating conditions can be identified. Corresponding to each condition, changing number of operating objectives are formulated to match the different requirements. Second, due to the changeable operating objectives can cause the expanded/contracted dimension of Pareto-optimal set, a dynamic multi-objective particle swarm optimization algorithm is developed. In this algorithm, self-adjusting mechanism of population size and global optimal solution are designed to derive the optimal solutions of control variables. Third, fuzzy controllers, matched with the different conditions, are designed to trace these optimal solutions. Finally, the effectiveness of DOC strategy is tested on a real WWTP. The results demonstrate that this proposed DOC strategy enables to achieve promising operating performance. <i>Note to Practitioners</i>&#x2014;This article aims to develop an optimal control strategy to match the multiple conditions and improve the operating performance. To achieve this goal, a dynamic optimal control (DOC) strategy, with the consideration of different operating conditions, is designed. Two key points are contained, the identification of multiple operating conditions and the satisfaction of different requirements. Due to each condition confronts with different requirements, it is important to accurately identify the conditions and construct the corresponding operating objectives. Based on the obtained operating data, changeable operating objectives are established to describe the different conditions. In addition, to satisfy the operating requirements, it is necessary to optimize the changeable objectives, but the changes in the number of objectives can cause the expanded/ contracted of Pareto-optimal set. A dynamic multi-objective particle swarm optimization algorithm, with self-adjusting mechanism of population size and global optimal solution, is designed to deal with this kind of optimization problem. The effectiveness and applicability of the proposed DOC strategy are evaluated on a pilot platform of a real WWTP, showing the improvement on the operating performance. These advantages can be valuable for transplanting this strategy to other real wastewater treatment plants to realize the optimal operation.

Antineoplastic drugs in urban wastewater: Occurrence, nanofiltration treatment and toxicity screening

Antineoplastic drugs are pharmaceuticals that have been raising concerns among the scientific community due to: (i) their increasing prescription in the fight against the disease of the twentieth century (cancer); (ii) their recalcitrance to conventional wastewater treatments; (iii) their poor environmental biodegradability; and (iv) their potential risk to any eukaryotic organism. This emerges the urgency in finding solutions to mitigate the entrance and accumulation of these hazardous chemicals in the environment. Advanced oxidation processes (AOPs) have been taken into consideration to improve the degradation of antineoplastic drugs in wastewater treatment plants (WWTPs), but the formation of by-products that are more toxic or exhibit a different toxicity profile than the parent drug is frequently reported. This work evaluates the performance of a nanofiltration pilot unit, equipped with a Desal 5DK membrane, in the treatment of real WWTP effluents contaminated (without spiking) with eleven pharmaceuticals, five of which were never studied before. Average removals of 68 ± 23% were achieved for the eleven compounds, with decreasing risks from feed to permeate for aquatic organisms from receiving waterbodies (with the exception of cyclophosphamide, for which a high risk was estimated in the permeate). Aditionally, no significative impact on the growth and germination of three different seeds (Lepidium sativum, Sinapis alba, and Sorghum saccharatum) were determined for permeate matrix in comparison to the control.

Peracetic acid as a disinfectant for wastewater reuse — Regulation (EU) 2020/741 application on a pilot-scale

Water scarcity affects already a large part of the world's population. To overcome this situation, water management is needed, and wastewater reuse must be implemented and included as a new approach. To achieve that objective water quality must comply with the parameters established in the Regulation (EU) 2020/741 of the European Parliament and the Council of the European Union and new treatment solutions have to be developed. The main goal of this pilot study was to evaluate the peracetic acid (PAA) disinfection efficiency in a real wastewater treatment plant (WWTP) in order to accomplish the wastewater reuse objective. To this end, six disinfection conditions were studied, three PAA doses (5, 10, and 15) and three contact times (5, 10, and 15) based on the commonly used disinfection operational conditions in real WWTP. Comparing the Total Suspended Solids (TSS), turbidity, Biological Oxygen Demand (BOD5) and Escherichia coli content, after and before the disinfection step, was possible to conclude that PAA ensures the Regulation (EU) 2020/741 requirements and that the disinfected effluent can be reused for several uses. All the conditions in which the PAA dose was 15 mg/L and the condition with 10 mg/L of PAA with a contact time of 15 min were the most promising, presenting the second highest water quality class achieved. The results of this study illustrate the potential of PAA as an alternative disinfectant for wastewater treatment and, bring it closer to the water reuse objective by presenting several possibilities for water uses.