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Wastewater treatment plants (WWTPs) are large-scale and nonlinear processes with tightly integrated operating units. The application of online optimization-based control strategies, such as model predictive control (MPC), to WWTPs generally faces high computational complexity. This paper proposes an event-triggered approach to address this issue. The model predictive controller updates information and solves the optimization problem only when the corresponding triggered logic is satisfied. The triggered logic sets the maximum allowable deviation for the tracking variables. Moreover, to ensure system performance, the design of the event-triggered logic incorporates the effluent quality. By obtaining the optimal sequence for the effluent quality within the receding horizon of the MPC, the cumulative deviation between the predicted and desired effluent quality is analyzed to evaluate the performance within that horizon. Based on these two conditions, the need for adjusting control actions is determined. Even if the maximum allowable range for the tracking variables in the triggered logic design is set unreasonably, the consideration of effluent quality factors in the triggered conditions ensures good performance. Simulation results demonstrate an average reduction in computational effort of 25.49% under different weather conditions while simultaneously ensuring minimal impact on the effluent quality and total cost index and compliance with effluent discharge regulations. Furthermore, this method can be combined with other approaches to guarantee effluent quality while further reducing computation time and complexity.

Modular wastewater treatment units in large residential complexes in India’s crowded cities often lack stringent monitoring due to cost constraints and limited technical manpower. Although these plants must meet effluent standards, testing often requires sending samples to external labs, causing delays and added costs. As a result, they are rarely monitored, risking improper effluent discharge. Quick, cost-effective assessments of effluent quality could significantly improve plant operation and maintenance. Addressing the special challenges faced by such wastewater treatment systems, artificial intelligence (AI)-based soft sensors and virtual instruments have been developed to forecast effluent quality with the help of a water quality parameter that is inexpensively, easily, and immediately measurable with a hand-held device. In this study, advanced artificial neural network (ANN)-based soft sensors were developed to enhance the monitoring and management of effluent quality in five modular wastewater treatment plants in Bangalore. The models serve as virtual instruments for the measurement of total suspended solids (TSS), biochemical oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP), using the wastewater turbidity as the input parameter. By using these AI models, operators can better anticipate and manage water quality, ultimately contributing to more efficient and effective wastewater treatment operations. This innovative approach represents a significant advancement in wastewater treatment technology providing a practical and efficient solution to streamline monitoring and enhance overall plant performance.

Granular activated sludge has gained increasing interest due to its potential in treating wastewater in a compact and efficient way. It is well-established that activated sludge can form granules under certain environmental conditions such as batch-wise operation with feast-famine feeding, high hydrodynamic shear forces, and short settling time which select for dense microbial aggregates. Aerobic granules with stable structure and functionality have been obtained with a range of different wastewaters seeded with different sources of sludge at different operational conditions, but the microbial communities developed differed substantially. In spite of this, granule instability occurs. In this review, the available literature on the mechanisms involved in granulation and how it affects the effluent quality is assessed with special attention given to the microbial interactions involved. To be able to optimize the process further, more knowledge is needed regarding the influence of microbial communities and their metabolism on granule stability and functionality. Studies performed at conditions similar to full-scale such as fluctuation in organic loading rate, hydrodynamic conditions, temperature, incoming particles, and feed water microorganisms need further investigations.

Physical and biological treatment technology are considered a highly feasible and economic way to treat slaughterhouse wastewater. To achieve the desired effluent quality for disposal or reuse, various technological options were reviewed. However, most practical operations are accompanied by several advantages and disadvantages. Nevertheless, due to the presence of biodegradable organic matter in slaughterhouse waste, anaerobic digestion technology is commonly applied for economic gain. In this paper, the common technologies used for slaughterhouse wastewater treatment and their suitability were reviewed. The advantages and disadvantages of the different processes were evaluated. Physical treatments (dissolved air floatation (DAF), coagulation–flocculation and sedimentation, electrocoagulation process and membrane technology) were found to be more effective but required a large space to operate and intensive capital investment. However, some biological treatments such as anaerobic, facultative lagoons, activated sludge process and trickling filters were also effective but required longer start-up periods. This review further explores the various strategies being used in the treatment of other wastewater for the production of valuable by-products through anaerobic digestion.

Herein, the influence of various contents of polyethylene glycol (PEG) on the dispersion of TiO[sub.2] nanoparticles and the comprehensive properties of PVDF/TiO[sub.2] composite membranes via the steric hindrance interaction was systematically explored. Hydrophilic PEG was employed as a dispersing surfactant of TiO[sub.2] nanoparticles in the pre-dispersion process and as a pore-forming additive in the following membrane preparation process. The slight overlap shown in the TEM image and low TSI value (<1) of the composite casting solution indicated the effective dispersion and stabilization under the steric interaction with a PEG content of 6 wt.%. Properties such as the surface pore size, the development of finger-like structures, permeability, hydrophilicity and Zeta potential were obviously enhanced. The improved antifouling performance between the membrane surface and foulants was corroborated by less negative free energy of adhesion (about −42.87 mJ/m[sup.2]), a higher interaction energy barrier (0.65 KT) and low flux declination during the filtration process. The high critical flux and low fouling rate both in winter and summer as well as the long-term running operation in A/O-MBR firmly supported the elevated antifouling performance, which implies a promising application in the municipal sewage treatment field.

Constructed wetland systems (CWs) are technologies based on natural processes for pollutant removal and have been more and more accepted in the treatment of domestic and industrial wastewater. This study selected and reviewed articles published in the last six years involving the use of different CW conceptions and their association with other technologies to treat different effluents and evaluated the quality of the effluents for reuse. From a total of 81 articles reviewed, 41 presented quantitative data on the quality of the treated effluent in relation to the requirements of the reuse regulations in different countries of the world. CWs can be used to treat gray water and runoff water, as well as domestic and industrial effluents with the purpose of reusing them. While studies on the removal of new chemical and biological substances have increased, challenges are associated with the optimization of CWs to improve the removal of pathogens and new contaminants that have appeared more recently. The potential for the improved removal of those pollutants lies in the association of CWs with conventional and advanced technologies in new configurations. We concluded that studies related to the reuse of effluents using CWs are in constant evolution, with experiments at different scales. The perspectives are promising since CWs are an economic, environmentally friendly, and efficient technology to help in the mitigation of water scarcity problems imposed by climate changes.

Maintaining effluent quality in wastewater treatment plants (WWTPs) comes with significant challenges under variable weather conditions, where sudden changes in flow rate and increased pollutant loads can affect treatment performance. Traditional physical sensors became both expensive and susceptible to failure under extreme conditions. In this study, we evaluate the performance of soft sensors based on artificial intelligence (AI) to predict the components underlying the calculation of the effluent quality index (EQI). We thus focus our study on three ML models: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Transformer. Using the Benchmark Simulation Model no. 2 (BSM2) as the WWTP, we were able to obtain datasets for training the ML models and to evaluate their performance in dry weather scenarios, rainy episodes, and storm events. To improve the classification of networks according to the type of weather, we developed a Random Forest (RF)-based meta-classifier. The results indicate that for dry weather conditions the Transformer network achieved the best performance, while for rain episodes and storm scenarios the GRU was able to capture sudden variations with the highest accuracy. LSTM performed normally in stable conditions but struggled with rapid fluctuations. These results support the decision to integrate AI-based predictive models in WWTPs, highlighting the top performances of both a recurrent network (GRU) and a feed-forward network (Transformer) in obtaining effluent quality predictions under different weather conditions.

Effluent quality prediction is critical for optimizing Wastewater Treatment Plant (WWTP) operations, ensuring regulatory compliance, and promoting environmental sustainability. This study evaluates the performance of five supervised learning models—AdaBoost, Backpropagation Neural Networks (BP-NN), Support Vector Machine (SVR), XGBoost, and Gradient Boosting (GB)—using data from a WWTP in Zhuhai, China. The Effluent Quality Index (EQI), integrating multiple pollutant concentrations and environmental impacts, was used as the target variable. The models were trained and tested on 84 monthly datasets, with their performances compared using R 2 , Mean Absolute Percentage Error (MAPE), and Mean Bias Error (MBE). XGBoost achieved the best balance between accuracy and robustness, with the lowest MAPE(6.11%) and a high R 2 (0.813), while SVR excelled in fitting accuracy(R 2  = 0.826) but showed limitations in error control. Although we employed GridSearchCV with cross-validation to optimize hyperparameters and ensure a fair model comparison, the study is limited by the reliance on data from a single WWTP and the relatively small dataset size (84 records). Nevertheless, the findings provide valuable insights into selecting effective machine learning models for effluent quality prediction, supporting data-driven decision-making in wastewater management and advancing intelligent process optimization in WWTP.

The effluent treatment from the packaging industry, particularly color removal, is strongly influenced by process interferences. High concentrations of dyes often make water reuse unfeasible. In this context, the present work aims to study the clarification of the dye used in the packaging industry by the photocatalytic process. Niobium was used as a catalyst, which was characterized by different techniques. Before verifying the catalytic activity in the industrial effluent, tests were performed with synthetic dye solutions. As a characterization result, it was possible to identify typical characteristics of the semiconductor. The results with the synthetic effluent indicated that the photocatalytic reaction was adequate for the decolorization of the solution. The optimized conditions indicated pH conditions without adjustments (4.2) and a catalyst concentration of 1.0 g L[sup.−1] , obtaining a decolorization of 98%. Tests with industrial effluent revealed that the optimum conditions were also obtained with an unadjusted pH (6.2) and catalyst concentration of 6.0 g L[sup.−1] , obtaining, however, 42% discoloration. This result highlights the influence of the organic load and other interfering factors such as additives. However, the process is promising in the clarification of the effluent, which possibly, with a 42% reduction in color, can be reused in the process generating water sustainability. A curve adjustment was proposed to determine the best conditions obtained for both synthetic and industrial effluents.

In Brazil, domestic effluents represent the primary source of pressure on water resources. Water pollution can be controlled by defining, applying, and enforcing the effluent standards for wastewater discharge. Discussions are ongoing in Minas Gerais State regarding the possibility of setting a discharge standard for ammonia nitrogen in municipal wastewater, which is currently not required. However, providing technical support for decision-making is challenging because of the difficulties in accessing monitoring data from sewage treatment plants. This study aimed to analyze the monitoring data from 49 sewage treatment plants operating in Minas Gerais to offer guidance for decision making. High concentrations of ammonia nitrogen in the effluents of the treatment plants were found, reinforcing the need for better control and the adoption of more advanced technologies. Furthermore, it was observed an increase in concentrations downstream of the discharges in the receiving water bodies. Adopting a progressive and adaptable discharge standard can be a solution for better control of treatment systems.