Optimization of integrated urban wastewater systems using multi-objective evolution strategies

Integrating simulation models with a view to optimal control of urban wastewater systems

A fast sampling based evolutionary algorithm for million-dimensional multiobjective optimization

Wastewater treatment plants (WWTPs) are major contributors to the release of volatile organic chemicals (VOCs), many of which pose significant risks to human health through both non-carcinogenic and carcinogenic pathways. These chemicals, along with plastic-derived compounds, pesticides, and pharmaceuticals and personal care products (PPCPs), have emerged as critical environmental pollutants. Their widespread release through urban wastewater systems, combined with their hydrophilic nature and limited removal efficiency in conventional WWTPs, allows these pollutants to persist throughout the water cycle, often contaminating drinking water supplies. Despite increasing global awareness of the environmental and health risks associated with these contaminants, data on their occurrence, transport, and fate in Mexico's wastewater systems are still limited. To address this knowledge gap, the present study analyzed 54 VOCs in wastewater samples collected from 17 WWTPs across different provinces of Mexico. Among these, 38 VOCs were detected at significant levels, with the highest concentrations recorded for Toluene (21.39 µg/L), 1,1,2,2-Tetrachloroethane (28.02 µg/L), followed by p-Isopropyltoluene (27.24 µg/L), and Trichloromethane (17.56 µg/L). Additionally, pesticides and related chemicals such as 2-Chlorotoluene, Naphthalene, 1,2-Dichlorobenzene, and n-Butylbenzene were prevalent, underscoring the extensive use of these compounds in agricultural practices. These chemicals not only bioaccumulate in soil but can also leach into groundwater systems, exacerbating contamination risks and increasing their persistence in the environment. Furthermore, many of the detected compounds, such as Toluene, its derivatives, and Trichloromethane, are known endocrine disruptors (EDCs) capable of causing hormonal imbalances, drug resistance, and reduced primary productivity in ecosystems. Their bioaccumulation in organisms and persistence in water further exacerbate their environmental impact, making them critical candidates for regulatory scrutiny. Therefore, this study underscores the urgent need for enhanced regulatory monitoring and management strategies targeting VOCs and EDCs in Mexico’s wastewater systems. By providing valuable insights into the prevalence and distribution of these hazardous pollutants, the findings highlight the importance of incorporating pesticides and PPCPs into comprehensive monitoring frameworks. Such efforts are essential for mitigating the environmental and health impacts of these contaminants and ensuring the sustainable management of water resources. The results also offer a foundation for developing targeted interventions aimed at reducing pollutant loads in wastewater and preventing their long-term accumulation in aquatic ecosystems. 

The importance of including fossil carbon in greenhouse gas emission assessments from wastewater treatment plants (WWTPs) is highlighted in the 2019 Intergovernmental Panel for Climate Change (IPCC) guidelines revision and underpinned by an increasing number of experimental studies. The present study introduces a model-based approach to estimate fossil carbon flows within Sweden's urban wastewater system, employing data on chemical and polymeric material flows as a starting point. Our findings show that fossil carbon constitutes approximately 12–17 % of the total carbon emissions to sewer systems. This result aligns with experimental data, which shows fossil carbon contributions to WWTP influents ranging from 4 to 28 %. Our analysis further indicates that microplastics contribute about 13 % of the fossil carbon influx to Swedish WWTPs, while organic chemicals account for the remaining 83 %.

Fate and transformation of silver nanoparticles in urban wastewater systems

Optimizing the operation of a multireservoir system is challenging due to the high dimension of the decision variables that lead to a large and complex search space. A spectral optimization model (SOM), which transforms the decision variables from time domain to frequency domain, is proposed to reduce the dimensionality. The SOM couples a spectral dimensionality‐reduction method called Karhunen‐Loeve (KL) expansion within the routine of Nondominated Sorting Genetic Algorithm (NSGA‐II). The KL expansion is used to represent the decision variables as a series of terms that are deterministic orthogonal functions with undetermined coefficients. The KL expansion can be truncated into fewer significant terms, and consequently, fewer coefficients by a predetermined number. During optimization, operators of the NSGA‐II (e.g., crossover) are conducted only on the coefficients of the KL expansion rather than the large number of decision variables, significantly reducing the search space. The SOM is applied to the short‐term operation of a 10‐reservoir system in the Columbia River of the United States. Two scenarios are considered herein, the first with 140 decision variables and the second with 3360 decision variables. The hypervolume index is used to evaluate the optimization performance in terms of convergence and diversity. The evaluation of optimization performance is conducted for both conventional optimization model (i.e., NSGA‐II without KL) and the SOM with different number of KL terms. The results show that the number of decision variables can be greatly reduced in the SOM to achieve a similar or better performance compared to the conventional optimization model. For the scenario with 140 decision variables, the optimal performance of the SOM model is found with six KL terms. For the scenario with 3360 decision variables, the optimal performance of the SOM model is obtained with 11 KL terms.

Pharmaceuticals and particularly antibiotics can harm sensitive aquatic species. Their occurrence in urban wastewater systems is the consequence of five successive processes: (i) ingestion of the substance, (ii) accumulation in the urine, (iii) excretion, (iv) degradation in the sewer system and (v) transport to the wastewater treatment plant (WTP). These processes were included in an integrated model that can be used to assess the dynamics of pharmaceuticals at a WTP inlet. First, information on sales data, posology, pharmacokinetics and toilet flushing frequency were combined to create a source model of pharmaceuticals entering a sewer system. This production function was then coupled with a transport/degradation model to simulate concentrations of pharmaceuticals at a WTP inlet. In an example application, the full model was applied to simulate the concentration of the antibiotic ciprofloxacin on an hourly time scale. In this application, the model was calibrated and validated for a case study at a WTP in Lausanne, Switzerland. Validation of the integrated approach was successful despite the high variability evident in the model results. This modeling approach has potential use in pollution management and epidemiology related to wastewater.

Large-scale high-dimensional many-objective optimization problems (LaMaOPs) are prevalent in fields such as autonomous driving, cloud resource scheduling, and smart grids. LaMaOPs involve a large number of decision variables and multiple conflicting objectives that need to be optimized simultaneously. The challenges posed by the curse of dimensionality due to the vast number of decision variables, and the conflict between convergence and diversity caused by the numerous objective variables, make traditional optimization methods inadequate. To address these issues, this paper proposes a two-population cooperative evolutionary algorithm based on large-scale decision variable analysis (DVA-TPCEA). This algorithm integrates quantitative analysis methods for decision variables to deeply examine their impact on each objective and introduces a contribution-based objective detection method. Additionally, a dual-population cooperative evolution mechanism is employed, with targeted optimization strategies designed for convergence and diversity populations, achieving synergistic and complementary optimization between the two populations. To validate the algorithm’s effectiveness in practical applications, a large-scale container resource scheduling strategy based on the DVA-TPCEA algorithm is also proposed. The experimental results indicate that the proposed algorithm demonstrates significant advantages in both general datasets DTLZ, WFG, and LSMOP, and practical models.

Many engineering optimization problems are typically multi-objective in their natures and multidisciplinary with a large number of decision variables. Furthermore, Pareto dominance loses its effectiveness in such situations. Thus, developing a robust optimization algorithm undoubtedly becomes a true challenge. This paper proposes a multi-objective orthogonal opposition-based crow search algorithm (M2O-CSA) for solving large-scale multi-objective optimization problems (LSMOPs). In the M2O-CSA, a multi-orthogonal opposition strategy is employed to mitigate the conflicts among the convergence and distribution of solutions. First, two individuals are randomly chosen to undergo the crossover stage and then orthogonal array is presented to obtain nine individuals. Then individuals are used in the opposition stage to improve the diversity of solutions. The effectiveness of the proposed M2O-CSA is investigated by implementing it on different dimensions of multi-objective optimization problems (MOPs). The Pareto front solutions of these MOPs have various characteristics such as convex, non-convex and discrete. It is also applied to solve multi-objective design applications with distinctive features such as four bar truss (FBT) design, welded beam (WB) deign, disk brake (DB) design, and speed reduced (SR) design, where they involve different characteristics. In this context, a new decision making tool based on multi-objective optimization on the basis of ratio analysis (MOORA) technique is employed to help the designer for extracting the operating point as the best compromise or satisfactory solution to execute the candidate engineering design. Simulation results affirm that the proposed M2O-CSA works efficiently and effectively.

Multi-objective evolutionary algorithms (MOEAs) of the state of the art are created with the only purpose of dealing with the number of objective functions in a multi-objective optimization problem (MOP) and treat the decision variables of a MOP as a whole. However, when dealing with MOPs with a large number of decision variables (more than 100) their efficacy decreases as the number of decision variables of the MOP increases. On the other hand, problem decomposition, in terms of decision variables, has been found to be extremely efficient and effective for solving large scale optimization problems. Nevertheless, most of the currently available approaches for large scale optimization rely on models based on cooperative coevolution or linkage learning methods that use multiple subpopulations or preliminary analysis, respectively, which is computationally expensive (in terms of function evaluations) when used within MOEAs. In this work, we study the effect of what we call operational decomposition, which is a novel framework based on coevolutionary concepts to apply MOEAs’s crossover operator without adding any extra cost. We investigate the improvements that NSGA-III can achieve when combined with our proposed coevolutionary operators. This new scheme is capable of improving efficiency of a MOEA when dealing with large scale MOPs having from 200 up to 1200 decision variables.

Multicriteria Control System Design Using an Intelligent Evolution Strategy

Design of a sustainable city has changed the traditional centralized urban wastewater system towards a decentralized or clustering one. Note that there is considerable spatial variability of the factors that affect urban drainage performance including urban catchment characteristics. The potential options are numerous for planning the layout of an urban wastewater system, which are associated with different costs and local environmental impacts. There is thus a need to develop an approach to find the optimal spatial layout for collecting, treating, reusing and discharging the municipal wastewater of a city. In this study, a spatial multi-objective optimization model, called Urban wastewateR system Layout model (URL), was developed. It is solved by a genetic algorithm embedding Monte Carlo sampling and a series of graph algorithms. This model was illustrated by a case study in a newly developing urban area in Beijing, China. Five optimized system layouts were recommended to the local municipality for further detailed design.

This study assesses the environmental impacts of four measures proposed for upgrading of the urban wastewater system of Eindhoven and the Dommel River in The Netherlands, against the base case, "do-nothing" option. The measures aim to reduce the overall environmental impact of the Eindhoven urban wastewater system (UWS) by targeting river dissolved oxygen depletion and ammonia peaks, reducing combined sewer overflows, and enhancing nutrient removal. The measures are evaluated using a life cycle analysis with the boundaries including the receiving river section by means of an integrated model of the UWS. An uncertainty analysis of the estimated impacts has been performed to support the outcomes. The study also uses the economic concept of shadow prices to assign relative weights of socio-economic importance to the estimated life cycle impacts. This novel integration of tools complements the assessments of this UWS with the inclusion of long-term global environmental impacts and the investigation of trade-offs between different environmental impacts through a single monetary unit. The results support the selection of deeper clarifiers as the most environmentally beneficial measure for upgrade.

Centrifugal compressors with high aerodynamic performance are widely used in turbochargers, aero-engines and petrochemical engineering. The impeller is the core component and plays a key role in determining the compressor performance. This paper reports the optimisation of the aerodynamic performance of an industrial centrifugal impeller by a multi-objective evolutionary strategy. Firstly the 3-D modeling method for parameterisation of impeller’s geometry was described. Secondly the traditional NSGA-II method was modified to improve its ability and efficiency. Employed CFD code was first validated using the experimental data of an existing impeller. The optimisation was applied to the industrial centrifugal impeller through a two-step optimization process to allow for significant variations of the impeller geometry and speedy finding of the optimum. The optimisation was completed within 53 hours on a workstation with two 24-core processors (Xeon(R) E5-2670 v3 2.3GHz). The results indicated that the isentropic efficiency of the impeller increased by 5.3 percents and the total pressure ratio by 20.5 percents at design condition.

Many real-world multi-objective optimization problems have hundreds or even thousands of decision variables, which contrast with the current practice of multi-objective metaheuristics whose performance is typically assessed using benchmark problems with a relatively low number of decision variables (normally, no more than 30). In this paper, we propose a cooperative coevolution framework that is capable of optimizing large scale (in decision variable space) multi-objective optimization problems. We adopt a benchmark that is scalable in the number of decision variables (the ZDT test suite) and compare our proposed algorithm with respect to two state-of-the-art multi-objective evolutionary algorithms (GDE3 and NSGA-II) when using a large number of decision variables (from 200 up to 5000). The results clearly indicate that our proposed approach is effective as well as efficient for solving large scale multi-objective optimization problems.