Feasibility of rigorous multi-objective optimization of wastewater management and treatment plants

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.

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.

Optimal control towards sustainable wastewater treatment plants based on multi-agent reinforcement learning

Wastewater treatment plants (WWTPs) are designed to eliminate pollutants and alleviate environmental pollution resulting from human activities. However, the construction and operation of WWTPs consume resources, emit greenhouse gases (GHGs) and produce residual sludge, thus require further optimization. WWTPs are complex to control and optimize because of high non-linearity and variation. This study used a novel technique, multi-agent deep reinforcement learning (MADRL), to simultaneously optimize dissolved oxygen (DO) and chemical dosage in a WWTP. The reward function was specially designed from life cycle perspective to achieve sustainable optimization. Five scenarios were considered: baseline, three different effluent quality and cost-oriented scenarios. The result shows that optimization based on LCA has lower environmental impacts compared to baseline scenario, as cost, energy consumption and greenhouse gas emissions reduce to 0.890 CNY/m³-ww, 0.530 kWh/m³-ww, 2.491 kg CO²-eq/m³-ww respectively. The cost-oriented control strategy exhibits comparable overall performance to the LCA-driven strategy since it sacrifices environmental benefits but has lower cost as 0.873 CNY/m³-ww. It is worth mentioning that the retrofitting of WWTPs based on resources should be implemented with the consideration of impact transfer. Specifically, LCA-SW scenario decreases 10 kg PO⁴-eq in eutrophication potential compared to the baseline within 10 days, while significantly increases other indicators. The major contributors of each indicator are identified for future study and improvement. Last, the authors discussed that novel dynamic control strategies required advanced sensors or a large amount of data, so the selection of control strategies should also consider economic and ecological conditions. In a nutshell, there are still limitations of this work and future studies are required.

Uncertainty and sensitivity analysis for reducing greenhouse gas emissions from wastewater treatment plants

This paper presents the sensitivity and uncertainty analysis of a plant-wide mathematical model for wastewater treatment plants (WWTPs). The mathematical model assesses direct and indirect (due to the energy consumption) greenhouse gases (GHG) emissions from a WWTP employing a whole-plant approach. The model includes: (i) the kinetic/mass-balance based model regarding nitrogen; (ii) two-step nitrification process; (iii) N₂O formation both during nitrification and denitrification (as dissolved and off-gas concentration). Important model factors have been selected by using the Extended-Fourier Amplitude Sensitivity Testing (FAST) global sensitivity analysis method. A scenario analysis has been performed in order to evaluate the uncertainty related to all selected important model factors (scenario 1), important model factors related to the influent features (scenario 2) and important model factors related to the operational conditions (scenario 3). The main objective of this paper was to analyse the key factors and sources of uncertainty at a plant-wide scale influencing the most relevant model outputs: direct and indirect (DIR,CO_2eq and IND,CO_2eq, respectively), effluent quality index (EQI), chemical oxygen demand (COD) and total nitrogen (TN) effluent concentration (COD_OUT and TN_OUT, respectively). Sensitivity analysis shows that model factors related to the influent wastewater and primary effluent COD fractionation exhibit a significant impact on direct, indirect and EQI model factors. Uncertainty analysis reveals that outflow TN_OUT has the highest uncertainty in terms of relative uncertainty band for scenario 1 and scenario 2. Therefore, uncertainty of influential model factors and influent fractionation factors has a relevant role on total nitrogen prediction. Results of the uncertainty analysis show that the uncertainty of model prediction decreases after fixing stoichiometric/kinetic model factors.

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.

Application and Evaluation of Energy Conservation Technologies in Wastewater Treatment Plants

High energy consumption is an important issue affecting the operation and development of wastewater treatment plants (WWTPs). This paper seeks energy-saving opportunities from three aspects: energy application, process optimization, and performance evaluation. Moreover, effective energy-saving can be achieved from the perspective of energy supply and recovery by using green energy technologies, including wastewater and sludge energy recovery technologies. System optimization and control is used to reduce unnecessary energy consumption in operation. Reasonable indexes and methods can help researchers evaluate the application value of energy-saving technology. Some demonstration WWTPs even can achieve energy self-sufficiency by using these energy conservation technologies. Besides, this paper introduces the challenges faced by the wastewater treatment industry and some emerging energy-saving technologies. The work can give engineers some suggestions about reducing energy consumption from comprehensive perspectives.

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.

A Holistic Sustainability Framework for Waste Management in European Cities: Concept Development

Waste management represents a challenge for public authorities due to many reasons such as increased waste generation following urban population growth, economic burdens imposed on the municipal budget, and nuisances inevitably caused to the environment and local inhabitants. To optimize the system from a sustainability perspective, moving the transition towards a more circular economy, a better understanding of the different stages of waste management is necessary. A review of recently developed sustainability frameworks for waste management showed that no single framework captures all the instruments needed to ultimately provide a solid basis for comprehensive analyses of the potential burdens associated with urban waste management. Bearing this limitation in mind, the objective of this research is to propose a conceptual and comprehensive sustainability framework to support decision-making in waste management of European cities. The framework comprises a combination of methods capable of identifying future strategies and scenarios, to assess different types of impacts based on a life cycle perspective, and considers the value of waste streams, the actors involved, and possible constraints of implementing scenarios. The social, economic, environmental, technical and political domains are covered, and special attention is paid to impacts affecting foremost the local population.

Sensitivity analysis of temporal parameters in a dynamic LCA framework

Including the temporal dimension in the Life Cycle Assessment (LCA) method is a very recent research subject. A complete framework including dynamic Life Cycle Inventory (LCI) and dynamic Life Cycle Impact Assessment (LCIA) was proposed with the possibility to calculate temporal deployment of climate change and ecotoxicity/toxicity indicators. However, the influence of different temporal parameters involved in the new dynamic method was not still evaluated. In the new framework, LCI and LCIA results are obtained as discrete values in function of time (vectors and matrices). The objective of this study is to evaluate the influence of the temporal profile of the dynamic LCI and calculation time span (or time horizon in conventional LCA) on the final LCA results. Additionally, the influence of the time step used for the impact dynamic model resolution was analysed. The range of variation of the different time steps was from 0.5day to 1year. The graphical representation of the dynamic LCA results shown important features such as the period in time and the intensity of the worst or relevant impact values. The use of a fixed time horizon as in conventional LCA does not allow the proper consideration of essential information especially for time periods encompassing the life time of the studied system. Regarding the different time step sizes used for the dynamic LCI definition, they did not have important influence on the dynamic climate change results. At the contrary, the dynamic ecotoxicity and human toxicity impacts were strongly affected by this parameter. Similarly, the time step for impact dynamic model resolution had no influence on climate change calculation (step size up to 1year was supported), while the toxicity model resolution requires adaptive time step definition with maximum size of 0.5day.