Multi-Objective Optimization Based on Simulation Integrated Pareto Analysis to Achieve Low-Carbon and Economical Operation of a Wastewater Treatment Plant
1. Introduction
Reducing greenhouse gas emissions is of utmost importance in the global fight against climate change. The Paris Agreement, established in 2015, has motivated numerous countries to establish objectives for carbon peaking and carbon neutrality [1]. As a result, industries around the world have adapted their development strategies and implemented emission-reducing measures. According to statistics from major developed countries, the wastewater treatment industry is among the top ten contributors to total carbon emissions, accounting for 1% to 2% [2]. It is expected that the total greenhouse gas emissions (in terms of CO2 equivalent) from China’s wastewater treatment industry will be as high as 365 million t by 2030. It accounts for 2.95% of the country’s total emissions [3,4]. On 29 December 2023, the Chinese government released the policy entitled “Opinions on the Implementation of Promoting Synergistic Efficiency in Wastewater Treatment for Reducing Pollution and Reducing Carbon”. It announced Chinese plans to build 100 green and low-carbon benchmark plants for wastewater treatment in 2025 with high-efficiency recycling in energy and resources. Therefore, measures such as adopting new processes and improving operation levels to achieve carbon reduction in the wastewater treatment industry are of great significance in achieving carbon neutrality.
Due to the diversity of municipal wastewater treatment processes, there is a large level of complexity and uncertainty when it comes to balancing multiple objectives such as operational efficiency and low-carbon emissions. In practice, improving effluent quality and minimizing operating costs are still the primary concerns of operation managers. To attain these objectives, it is crucial to explore avenues for reducing the operation cost index (OCI) and greenhouse gas (GHG) while upholding the standards of the effluent quality index (EQI). This is a problem involving the optimization of multiple objectives in nature.
In terms of single-objective and bi-objective optimization, there have been numerous reports. Wu et al. [5] used WEST 2012® software to simulate and optimize an industrial wastewater effluent plant with complex influent compositions, which led to the reduction in OCI from 6.2 to 5.5 €/m3. Cao et al. [6] screened and analyzed the sensitivity of 61 parameters. They developed a quadratic polynomial response surface model of six key process parameters. Finally, the water quality improvement was achieved by optimizing two process parameters, dissolved oxygen (DO) and solids retention time (SRT). All of the above studies focus on the single-objective optimization problem of wastewater treatment process. However, they have not formed an integrated and comprehensive study. Vega et al. [7] combined real-time optimization and hierarchical control with nonlinear model predictive control, and evaluated the wastewater treatment process EQI and OCI through the control structure. Zhang et al. [8] proposed a multi-objective optimization and control method of BP neural network combined with a genetic algorithm, which effectively solves the problems of EQI and OCI, a pair of mutually constrained optimization objectives. Guerrero et al. [9] used both OCI and EQI as the control optimization objectives, which produced a set of optimal operating setpoints that could be approximated by a Pareto surface. These optimization studies can guide wastewater treatment plants (WWTPs) to effectively reduce OCI without consideration about GHG emissions.
Regarding the tri-objective optimization of EQI, OCI, and GHG, although there are some research reports in recent years, there are still some deficiencies that need to be improved. Lu et al. [10] assessed the effectiveness of gauging a dynamic simulation model in regulating GHG in WWTP. The evaluation puts forth a fresh approach for achieving optimal control of such plants, taking into account EQI, OCI, and GHG. It is important to mention, however, that while the proposed framework is sound in theory, it lacks practical real-world case studies. X. Flores-Alsina et al. [11] found that aerobic tank dissolved oxygen, primary sedimentation tank suspended solid (SS) removal, anaerobic digester temperature, and reflux strategy had effects on the three objectives considered (GHG, EQI, and OCI). But these objectives could not be optimized for all of them. Similar conclusions were obtained in the study of C. Sweetapple et al. [12], where none of the 315 aeration strategies set up could simultaneously achieve the co-optimization of the three objectives. The operation energy consumption, water quality, and carbon emissions of WWTP based on continuous batch reactor were studied, but no collaborative optimization was achieved [13].
Since EQI, OCI, and GHG are three mutually constrained optimization objectives, it is difficult to achieve the optimal solution of the three objectives at the same time. Hence, it is necessary to adopt a nonlinear multi-objective optimization method to determine the overall optimal solution [8]. For this purpose, the study employs Pareto optimization principles and introduces the upstream logic in the NSGA-II algorithm—the Non-dominated Sorting Genetic Algorithms, which has proven effective in optimizing control strategies for WWTPs from prior studies. Chen et al. [14] used NSGA-II to achieve multi-objective optimization of operational energy consumption, effluent quality, total volume of structures, and SS of structures based on the activated sludge method. Beraud et al. [15] coupled NSGA-II with a common wastewater treatment plant model to illustrate how the algorithm can be used to determine the feasibility of Pareto optimality. It is worth noting that researchers use mathematical models to simulate complex process conditions and obtain basic data for optimization evaluation commonly. In this way, as the high input of time and labor decreases apparently, so does the cost of trial and error. Moreover, the issues caused by uncertainty and inefficiency in relying on traditional methods to optimize operational parameters can be avoided efficiently, as well [16].
At present, there are hundreds of domestic and foreign wastewater treatment processes. Among them, the combination of anaerobic-anoxic-oxic (AAO) and membrane bioreactor (MBR) processes (named AAO-MBR) has aroused general attention, because it can achieve high and stable effluent quality, short hydraulic retention time, and low residual sludge volume [17,18]. Due to its high effluent quality, the resulting wastewater can not only be discharged directly into the environment, but also reused for non-potable water applications [19]. Considering the increasing use of AAO-MBR in upgrading projects and underground WWTPs, more than 25% of underground WWTPs use AAO-MBR as the primary process [20], the typical AAO-MBR process was selected as the research object in this study. The nonlinear multi-objective optimization method was explored to achieve low-carbon emission taking into account the effluent quality and operation cost. We established a process model with the help of GPS-X simulation and modeling software to analyze the effects of six typical operational parameters on EQI, OCI, and GHG. A non-dominated sorting method was adopted to search for the Pareto-optimal set of solutions and screen the optimal solution from it. Through the above process, the necessary trade-offs between conflicting control objectives were made, which provided support for enhancing the sustainability of the wastewater treatment system.
4. Conclusions
In this paper, three optimization objectives of WWTP were optimally weighed through a multi-objective optimization method. It demonstrated the potential to reduce GHG emissions cost-effectively while ensuring that the water quality meets the standards, and the following specific conclusions are drawn:
Starting from the idea of Pareto optimization, the GPS-X simulation and modeling software were used to screen 75 scenarios from tens of thousands of orthogonal simulation scenarios to reach the Pareto optimal level. Using the non-dominated sorting method, the optimal solutions were obtained by equal proportional weighting, which guided the attainment of the optimal combinations of operational parameters, as ATDO was kept at a very low concentration of 0.1 mg/L, MTDO was kept at 4 mg/L, IRR was 50%, ERR was 100%, a small amount of PAC (2 gAl/m3) was added, SD was kept at a low value of 1000 m3/d, and ECR was taken to be 0. This paper provides an optimal decision-making solution for this WWTP, which is conducive to the realization of sustainable development in the wastewater treatment industry.
Optimizing the performance of objectives requires a deep understanding of how it responds to operational parameters. Based on simulation results, there appears to be a significant linear relationship between ECR and the three key objectives that operators should prioritize. At the same time, the change in the C/N ratio due to the addition of ECR, thus affecting GHG emissions, is an essential direction for future research.
