Optimizing municipal wastewater treatment plants using an improved multi-objective optimization method

Digitalization of phosphorous removal process in biological wastewater treatment systems: Challenges, and way forward

Phosphorus in wastewater poses a significant environmental threat, leading to water pollution and eutrophication. However, it plays a crucial role in the water-energy-resource recovery-environment (WERE) nexus. Recovering Phosphorus from wastewater can close the phosphorus loop, supporting circular economy principles by reusing it as fertilizer or in industrial applications. Despite the recognized importance of phosphorus recovery, there is a lack of analysis of the cyber-physical framework concerning the WERE nexus. Advanced methods like automatic control, optimal process technologies, artificial intelligence (AI), and life cycle assessment (LCA) have emerged to enhance wastewater treatment plants (WWTPs) operations focusing on improving effluent quality, energy efficiency, resource recovery, and reducing greenhouse gas (GHG) emissions. Providing insights into implementing modeling and simulation platforms, control, and optimization systems for Phosphorus recovery in WERE (P-WERE) in WWTPs is extremely important in WWTPs. This review highlights the valuable applications of AI algorithms, such as machine learning, deep learning, and explainable AI, for predicting phosphorus (P) dynamics in WWTPs. It emphasizes the importance of using AI to analyze microbial communities and optimize WWTPs for different various objectives. Additionally, it discusses the benefits of integrating mechanistic and data-driven models into plant-wide frameworks, which can enhance GHG simulation and enable simultaneous nitrogen (N) and Phosphorus (P) removal. The review underscores the significance of prioritizing recovery actions to redirect Phosphorus from effluent to reusable products for future considerations.

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.

Multiobjective Operation Optimization of Wastewater Treatment Process Based on Reinforcement Self-Learning and Knowledge Guidance

This article proposes a multiobjective operation optimization method based on reinforcement self-learning and knowledge guidance for quality assurance and consumption reduction of wastewater treatment process (WWTP) with nonstationary time-varying dynamics. First, operation optimization models are developed by online sequential random vector functional-link (OS-RVFL) neural network, which can realize online sequential learning of model parameters. Then, a knowledge base is established to store typical optimization cases for knowledge guiding the subsequent optimizations. Based on it, a reinforcement self-learning-based multiobjective particle swarm optimization (RSL-MOPSO) algorithm is proposed to perform optimization calculation. In this algorithm, reinforcement self-learning is used for interaction learning between environment and action in optimization, and the particle motion trend of algorithm is adjusted according to the feedback information of the optimization process. The effects of wastewater state parameters on particles are recorded and reused to improve the solution quality and calculation efficiency of optimization. Moreover, to make good use of the information of the previous optimizations and balance the coordination between global search in the early stage and local search in the later stage, a selective information feedback mechanism is further proposed to ensure the diversity and convergence of the algorithm. Finally, prediction-based intelligent decision making is performed to select the final optimization solution as the final setpoints for the lower-level controllers from the Pareto frontier with considering specific technical requirements. Data experiments show that the proposed method can effectively reduce energy consumption and ensure effluent quality.

Application of Fuzzy Multi-Objective Programming to Regional Sewer System Planning

Planning of sewer systems typically involves limitations and problems, regardless of whether traditional planning methods or optimization models are used. Such problems include non-quantifiability, fuzzy objectives, and uncertainties in decision-making variables which are commonly applied in the planning of any process. Particularly, uncertainties have prevented the inclusion of these variables in models. Consequently, the theoretical optional solution of the mathematical models is not the true optimum solution to practical problems. In this study, to solve the above problems for regional sewer system planning, multi-objective programming (MOP), nonlinear programming, mixed-integer programming, and compromise fuzzy programming were used. The objectives of this study were two-fold: (1) determination of the necessary decision-making variables or parameters, such as the optimum number of plants, piping layout, size of the plant, and extent of treatment; (2) establishment of a framework and methodology for optimal planning for designing a regional sewer system, matching demanded targets with the lowest cost, which would achieve the aim of lower space and energy requirements as well as consumption and high treatment efficiency for the purpose of meeting effluent standards. The findings of this study revealed that individual regional sewage treatment plants could be merged to form a centralized system. Land acquisition was difficult; thus, reducing the number of plants was required. Therefore, the compromise-fuzzy-based MOP method could effectively be used to build a regional sewer system plan, and the amount of in-plant establishment reached its maximized value with a minimized cost.

Developing a practical model for the optimal operation of wastewater treatment plant considering influent characteristics

Wastewater treatment plants (WWTPs) play an important role in protecting the quality of water sources. The optimum operation of WWTPs in response to continuous changes in the characteristics of the influent of the WWTP is very important, and it can improve the quality of the effluent of the WWTP. In this study, an approach for optimal operation of the WWTP has been presented considering the quantitative and qualitative variables of influent. In the proposed method, first, the simulation model of WWTP is developed and calibrated using the recorded data of its influent and effluent characteristics as well as operation conditions. Then, the influent is classified into clusters quantitatively and qualitatively k-means clustering method. In the final step, after determining the effective operation parameters, the AMOEA-MAP optimization algorithm is used to determine the optimal values of operation parameters for each cluster of influents based on its quantitative and qualitative characteristics including flow rate, COD, ammonium, and temperature. The proposed approach was implemented on a WWTP in the South of Tehran, the capital of Iran. Dissolved oxygen (DO) in the aeration tank, waste-activated sludge flow rate (Q_WAS) and the ratio of the supernatant flow rate of the sludge dewatering unit to the effluent flow rate (Q_d/Q_e) were considered as operation parameters affecting the performance of the system in removing pollutants and their optimal values were obtained as DO, 0.25-1.7 mg/l, Q_WAS, 875-2000 m³/day, and Q_d/Q_e, 10-14%. Using this method, i.e., changing system operation conditions based on influent characteristics, has improved the performance of a system in reducing COD, ammonium, and nitrate in the effluent by 11-41, 17-20 and 15-34, respectively.

Efficiency evaluation of sewage treatment plants in Delhi, India, using tolerance-based data envelope analysis

Correct and effective performance evaluation of wastewater treatment plants is a tough task because of the complex biological, physico-chemical, and biochemical processes and associated variables affecting their performance. Conventionally, the efficiency of sewage treatment plants (STPs) are obtained using some index relating pollutant removal efficiency with energy used or costs. These indicators consider only one variable at a time. This leads to incorrect assessment of efficiency, which in turn could adversely affect decision-making of the regulatory authorities. The data envelope analysis (DEA) method utilizes a Linear programming technique which can handle multiple input/output variables without requiring the cost function. This makes it an appropriate tool for assessing the relative efficiency of treatment plants. The present study assess the efficiency of 30 STPs in Delhi, India, using the tolerance-based DEA model utilizing the variable return of scale (VRS). The uncertainty was incorporated into the model using the tolerance measure. The model is solved using the "Add on" option in spreadsheet toolbox of excel solver. Results reveal that out of the 30 plants considered for the study, 6 are performing well (20%). Further, it was observed that a slight change in the input data leads to instability of the efficiency results. Lastly, the ranking is used to determine the treatment plant with best efficiency under all scenarios for the larger period of the year. Such studies will help in chalking out the best management practices that could be adopted by other regulatory authorities.

Relationship between manual air valve positioning, water quality and energy usage in activated sludge processes

Diffused aeration is the most implemented method for oxygen transfer in municipal activated sludge systems and governs the economics of the entire treatment process. Empirical observations are typically used to regulate airflow distribution through the adjustment of manual valves. However, due to the associated degrees of freedom, the identification of a combination of manual valves that optimizes all performance criteria is a complex task. For the first time a multi-criteria optimization algorithm was used to minimize effluent constituents and energy use by parametrizing manual valves positions. Data from a full-scale facility in conjunction with specific model assumptions were used to develop a base-case facility consisting of a detailed air supply model, a bio-kinetic model and a clarification model. Compared to the base-case condition, trade-offs analysis showed potential energy savings of up to 13.6% and improvement of effluent quality for NH₄⁺ (up to 68.5%) and NO_x (up to 81.6%). Based on two different tariff structures of a local power utility, maximum costs savings of 12800 USD mo^-1 to 19000 USD mo^-1 were estimated compared to baseline condition.

A decade of nitrous oxide (N2O) monitoring in full-scale wastewater treatment processes: A critical review

Direct nitrous oxide (N₂O) emissions during the biological nitrogen removal (BNR) processes can significantly increase the carbon footprint of wastewater treatment plant (WWTP) operations. Recent onsite measurement of N₂O emissions at WWTPs have been used as an alternative to the controversial theoretical methods for the N₂O calculation. The full-scale N₂O monitoring campaigns help to expand our knowledge on the N₂O production pathways and the triggering operational conditions of processes. The accurate N₂O monitoring could help to find better process control solutions to mitigate N₂O emissions of wastewater treatment systems. However, quantifying the emissions and understanding the long-term behaviour of N₂O fluxes in WWTPs remains challenging and costly. A review of the recent full-scale N₂O monitoring campaigns is conducted. The analysis covers the quantification and mitigation of emissions for different process groups, focusing on techniques that have been applied for the identification of dominant N₂O pathways and triggering operational conditions, techniques using operational data and N₂O data to identify mitigation measures and mechanistic modelling. The analysis of various studies showed that there are still difficulties in the comparison of N₂O emissions and the development of emission factor (EF) databases; the N₂O fluxes reported in literature vary significantly even among groups of similar processes. The results indicated that the duration of the monitoring campaigns can impact the EF range. Most N₂O monitoring campaigns lasting less than one month, have reported N₂O EFs less than 0.3% of the N-load, whereas studies lasting over a year have a median EF equal to 1.7% of the N-load. The findings of the current study indicate that complex feature extraction and multivariate data mining methods can efficiently convert wastewater operational and N₂O data into information, determine complex relationships within the available datasets and boost the long-term understanding of the N₂O fluxes behaviour. The acquisition of reliable full-scale N₂O monitoring data is significant for the calibration and validation of the mechanistic models -describing the N₂O emission generation in WWTPs. They can be combined with the multivariate tools to further enhance the interpretation of the complicated full-scale N₂O emission patterns. Finally, a gap between the identification of effective N₂O mitigation strategies and their actual implementation within the monitoring and control of WWTPs has been identified. This study concludes that there is a further need for i) long-term N₂O monitoring studies, ii) development of data-driven methodological approaches for the analysis of WWTP operational and N₂O data, and iii) better understanding of the trade-offs among N₂O emissions, energy consumption and system performance to support the optimization of the WWTPs operation.

Scale and process design for sewage treatment plants in airports using multi-objective optimization model with uncertain influent concentration

The treatment of airport sewage has posed many novel challenges because of its huge impact on the surrounding environment. This paper proposes a multi-objective decision model to optimize the scale design and process selection of sewage treatment plants in airports. In this model, we consider the conflict among the process cost, environmental protection, and benefits of recycled water. In addition, the uncertainty in influent concentration and passenger throughput is also incorporated. Airport sewage treatment has its own unique features, such as the concentration of airport sewage is higher than that of ordinary urban sewage, the change in passenger throughput impacts the volume of the airport sewage treatment, and the utilization rate of the entire sewage treatment plant must be higher than or equal to 70%. Only in this case can the airport sewage treatment plant pass the acceptance test. The Tianfu International Airport, the largest civil transportation hub airport project in southwestern China, is used to prove the efficiency of the proposed model. Finally, some significant insights are suggested for the design of wastewater treatment plants in airports.

Modeling and performance improvement of an anaerobic-anoxic/nitrifying-induced crystallization process via the multi-objective optimization method

The trade-off between energy savings and emission reductions of an activated sludge process is a multi-objective problem relating to several potentially conflicting objectives. Therefore, the optimal modification of an anaerobic-anoxic/nitrifying/induced crystallization (A2N-IC) process by multi-objective optimization method was studied in this work. The multi-objective optimization model comprised three evaluative indices, (effluent quality (EQ), operation cost (OC), and total volume (TV) of structures), and 14 process parameters (decision variables) solving by non-dominated sorting genetic algorithm II (NSGA-II) in MATLAB. The trade-off relationships among EQ, OC, and TV were investigated under 30 days of dynamic influent with different constraint conditions. A series of Pareto solutions were obtained, and one Pareto solution was selected for further analysis. Results showed improved effluent concentrations of chemical oxygen demand (COD), total nitrogen (TN), ammonia-nitrogen (NH₄⁺-N), and total phosphorous (TP) under the optimized strategy compared to the original strategy, where the average effluent concentrations decreased by 2.22, 0.47, 0.13, and 0.02 mg/L, respectively. The values of EQ and OC decreased from 0.015 kg/day and 0.15 ¥/m³ to 0.0023 kg/day and 0.12 ¥/m³, respectively, while the TV increased from 0.31 to 0.33 m³. These results indicated that the multi-objective optimization method is useful for modifying activated sludge processes.

Artificial neural network - Genetic algorithm to optimize wheat germ fermentation condition: Application to the production of two anti-tumor benzoquinones

Methoxy-ρ-benzoquinone (MBQ) and 2, 6-dimethoxy-ρ-benzoquinone (DMBQ) are two potential anticancer compounds in fermented wheat germ. In present study, modeling and optimization of added macronutrients, microelements, vitamins for producing MBQ and DMBQ was investigated using artificial neural network (ANN) combined with genetic algorithm (GA). A configuration of 16-11-1 ANN model with Levenberg-Marquardt training algorithm was applied for modeling the complicated nonlinear interactions among 16 nutrients in fermentation process. Under the guidance of optimized scheme, the total contents of MBQ and DMBQ was improved by 117% compared with that in the control group. Further, by evaluating the relative importance of each nutrient in terms of the two benzoquinones' yield, macronutrients and microelements were found to have a greater influence than most of vitamins. It was also observed that a number of interactions between nutrients affected the yield of MBQ and DMBQ remarkably.

Optimization of the moving-bed biofilm sequencing batch reactor (MBSBR) to control aeration time by kinetic computational modeling: Simulated sugar-industry wastewater treatment

A novel approach was applied for optimization of a moving-bed biofilm sequencing batch reactor (MBSBR) to treat sugar-industry wastewater (BOD5=500-2500 and COD=750-3750 mg/L) at 2-4 h of cycle time (CT). Although the experimental data showed that MBSBR reached high BOD5 and COD removal performances, it failed to achieve the standard limits at the mentioned CTs. Thus, optimization of the reactor was rendered by kinetic computational modeling and using statistical error indicator normalized root mean square error (NRMSE). The results of NRMSE revealed that Stover-Kincannon (error=6.40%) and Grau (error=6.15%) models provide better fits to the experimental data and may be used for CT optimization in the reactor. The models predicted required CTs of 4.5, 6.5, 7 and 7.5 h for effluent standardization of 500, 1000, 1500 and 2500 mg/L influent BOD5 concentrations, respectively. Similar pattern of the experimental data also confirmed these findings.

Performance and modeling of a moving bed biofilm process: nickel and chromium heavy metal removal from industrial wastewater

The process of a lab-scale moving bed biofilm reactor (MBBR) using simulated sugar-manufacturing wastewater as feed was investigated.

The application of multi-objective optimization method for activated sludge process: a review

The activated sludge process (ASP) is the most generally applied biological wastewater treatment approach. Depending on the design and specific application, activated sludge wastewater treatment plants (WWTPs) can achieve biological nitrogen (N) and phosphorus (P) removal, besides the removal of organic carbon substances. However, the effluent N and P limits are getting tighter because of increased emphasis on environmental protection, and the needs for energy conservation as well as the operational reliability. Therefore, the balance between treatment performance and cost becomes a critical issue for the operations of WWTPs, which necessitates a multi-objective optimization (MOO). Recent studies in this field have shown promise in utilizing MOO to address the multiple conflicting criteria (i.e. effluent quality, operation cost, operation stability), including studying the ASP models that are primarily responsible for the process, and developing the method of MOO in the wastewater treatment process, which facilitates better optimization of process performance. Based on a better understanding of the application of MOO for ASP, a comprehensive review is conducted to offer a clear vision of the advances, and potential areas for future research are also proposed in the field.