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The accurate simulation of the dynamics of the anaerobic–anoxic–oxic (A2O) process in the biochemical reactions in wastewater treatment plants (WWTPs) is important for system prediction and optimization. Previous studies have used real-time monitoring data of WWTPs to develop data-driven predictive models, but these models cannot be used to provide mathematical analysis of A2O dynamic properties. In this study, we developed a new simulation and analysis method for determining A2O dynamics in biochemical reactions using deep learning and the Koopman operator to address the above problems. This method was validated through data from a real-world WWTP in east China and compared it with the traditional deep learning model. According to the results, the new method achieved high-accuracy prediction. Meanwhile, with the help of the Koopman operator, the new method was able to analyze the asymptotical stability and convergence behavior of the A2O process, which provides a brand-new perspective for the in-depth study of biochemical reactor dynamics. [ABSTRACT FROM AUTHOR]

With the increasing demands for higher treatment efficiency, better effluent quality, and energy conservation in Urban Wastewater Treatment Plants (WWTPs), research has already been conducted to construct an optimized control system for Anaerobic-Anoxic-Oxic (AAO) process using a data-driven approach. However, existing data-driven optimization control systems for AAO mainly focus on improving effluent water quality and reducing energy consumption, therefore they lack consideration for the stability of bioreactors. Meanwhile, safety in the optimization control process is still missing, resulting in a lack of reliability in practical applications. In this study, long short-term memory based model-predictive control (LSTM-MPC) with safety verificationis developed for the real-time control of AAO. It is used to optimize the control of aeration volume, internal recirculation, and sludge internal recycle processes for both saving energy and maintaining the stability of the bioreactor operation. To ensure the safety of the control process, this study proposes three rationality verification methods based on historical operation experience. These methods are validated through data from a real-world WWTP in eastern China. The results show that the prediction model of LSTM-MPC is capable of accurately predicting the water quality variables of the AAO system, with mean square error (MSE) close to 2.64 and Nash–Sutcliffe model efficiency coefficient (NSE) of 0.99 on the validation dataset. The combination of LSTM-MPC and rationality verification achieves a stable control trajectory with a 7% reduction in oxygen usage compared to a conventional controller, demonstrating its efficacy as a safe and reliable control strategy for WWTPs. [ABSTRACT FROM AUTHOR]

Water supply systems that use rivers with high sedimentation levels may experience issues such as reservoir siltation. The suspended sediment concentration (SSC) of rivers experiences interannual variation and high nonlinearity due to its close relationship with meteorological factors, which increase the mismatch between the river water source and urban water demand. The raw water system scheduling problem is expressed as a reservoir and pump station control problem that involves real-time SSC changes. To lower the SSC of the water intake and lower the pumping station's energy consumption, a deep reinforcement learning (DRL) model based on SSC prediction was developed. The framework consists of a DRL model, a hydraulic model for simulating the raw water system, and a neural network for predicting river SSC. The framework was tested using data from a Yellow River water withdrawal pumping station in China with an average capacity of 400,000 m3/d. The strategy created in this study can reduce the system energy consumption per unit of water withdrawal by 8.33% and the average annual water withdrawal SSC by 37.01%, when compared to manual strategy. Meanwhile, the deep reinforcement learning algorithm had good response robustness to uncertain imperfect predictive data. [ABSTRACT FROM AUTHOR]

Real‐time control (RTC) of urban drainage systems (UDS) has been proved an efficient tool in combined sewer overflow (CSO) and flooding mitigation. Recently, new RTC approaches based on reinforcement learning (RL) were developed for flooding mitigation in stormwater systems. While these studies have made contributions to enable an improved urban water management, they are insufficient to allow for deeply understanding of the effectiveness of different RLs in UDS. Meanwhile, the risk of handing over the control process to a RL agent is still unavoidable because of the fluctuations of RLs' output and the unknown consequences of implementing RLs control strategy. This study conducted four tasks to address these problems. First, five RTC systems based on five individual RLs were designed to distinguish different RLs' performance in the context of UDS. Then, an independent security system based on SWMM was provided to forecast and evaluate the consequence of RL control strategy. After that, an innovative hybrid RTC system, called Voting, was developed by coupling multiple RLs and the independent security system through a model predictive control framework to avoid the fluctuations of RLs' output. Finally, the robustness of the RL agents was validated using uncertainty analysis. All the RLs were evaluated through simulation based on a Storm Water Management Model of a UDS located in Eastern China. According to the results, (a) different RLs show promise in CSO and flooding mitigation; (b) Voting selects a relatively reliable and optimal control trajectory compared with any single RL agents; (c) the performances of RL agents have certain robustness when facing different rainfall events and imperfect input. Key Points: Different reinforcement learnings (RLs) show promise in CSO and flooding mitigationVoting selects a relatively more reliable and more optimal control trajectory compared with any single RL agentsRLs' performance is influenced by rainfall events and imperfect input, but they still have certain robustness [ABSTRACT FROM AUTHOR]

In recent studies, deep reinforcement learning (RL) methods have been used for the real‐time control of urban drainage systems (UDSs). However, the training process of an RL agent is computationally expensive since collecting the training data requires multiple simulations of a UDS model. An effective solution to this issue is to replace the original UDS model with an emulator, a simpler model which emulates the specified output of the system. However, the adaptability and interpretability of the existing UDS emulators still require further improvements. To address these challenges, a new emulator with increased adaptability and interpretability, called Koopman emulator, was developed by using the Koopman operator and a deep learning technique in this study. Then, the Koopman emulator was used to replace the UDS model in RL to establish an emulator‐based RL framework for fast and efficient training. This framework was used to train emulator‐based RL agents for flooding and overflow mitigation in a UDS located in Eastern China. Baseline emulators were also employed for comparison. All the methods were evaluated through simulations. The following results were obtained: (a) The Koopman emulator demonstrated a better emulation performance than the baseline emulators. (b) The RL agents based on the Koopman emulator achieved a similar control effect with a faster training process and more efficient data usage compared with the RL agents based on the storm water management model. (c) The uncertainty analysis showed that the RL agents based on the Koopman emulator exhibited acceptable robustness when facing different rainfall events and input perturbations. Key Points: Koopman emulator demonstrates better performance and better interpretability in nonlinear emulationThe emulator‐based reinforcement learning (RL) framework achieves similar control effect with faster training process and more efficient data usageThe RL agents based on Koopman emulator exhibited acceptable robustness when facing different rainfall events and input perturbations [ABSTRACT FROM AUTHOR]

The performance comparison studies of the autoregressive integrated moving average model (ARIMA) and the artificial neural network (ANN) were mostly carried out between the selected model structures through trial-and-error, strongly influenced by model structure uncertainty. This research aims to make up for this inadequacy. First, a surface water quality prediction case study including eight monitoring sites in China was introduced. Second, the ARIMA and ANN's performance was compared statistically between 6912 Seasonal ARIMA (SARIMA) and 110,592 feedforward ANN with different model structures, based on the mean square error (MSE) distributions depicted by boxplots. In a statistical view, the ANN models obtained a significantly lower median value and a more concentrated distribution of validation MSEs, which indicated lighter overfitting and better generalization ability. Furthermore, the optimal SARIMA models' performance is inferior to even the median of the ANN models in the case study. In contrast with the previous comparisons among selected models, the statistical comparison in this study shows lower uncertainty. [ABSTRACT FROM AUTHOR]

Neural network models have been used to predict chlorophyll-a concentration dynamics. However, as model generalization ability decreases, (i) the performance of the models gradually decreases over time; (ii) the accuracy and performance of the models need to be improved. In this study, Transfer learning (TL) is employed to optimize neural network models (including feedforward neural networks (FNN), recurrent neural networks (RNN) and long short-term memory (LTSM)) and overcome these problems. Models using TL are able to reduce the influence of mutable data distribution and enhance generalization ability. Thus, it can improve the accuracy of prediction and maintain high performance in long-term applications. Also, TL is compared with parameter norm penalties (PNP) and dropout—two other methods used to improve model generalization ability. In general, TL has a better prediction effect than PNP and dropout. All the models, including FNN with different architectures, RNN and LSTM, as well as models optimized by PNP, dropout, and TL, are applied to an estuary reservoir in eastern China to predict chlorophyll-a dynamics at 5-min intervals. According to the results of this study, (i) models with TL produce the best prediction results; (ii) the original models and the models with PNP and dropout lose their ability to predict within 3 months, while TL models retain a high prediction accuracy. [ABSTRACT FROM AUTHOR]

Due to the influence of buildings on the distribution of flood and their economic and social attributes, 3D spatial information such as the size of buildings and the flooded ratio of buildings relative to their height has an increasing impact on urban flood risk. However, existing flood risk assessment methods mainly use data in 2D and analysis methods are mostly 2D. In this study, flood variation processes were analyzed in the form of 3D dynamic visualization by coupling an urban drainage model and a flood simulation model with 3D visualization methods. By further combining with 3D building models, the 3D spatial information of buildings related to flood was obtained. In order to study the influence of 3D information on flood risk and combine with other multi-source heterogeneous data for integrated analysis, a 3D visualization assessment and analysis method for flood risk, coupled with the projection pursuit-particle swarm optimization algorithm (PP–PSO) was established (3DVAAM-PP-PSO). A case study from Chaohu City, China, was used to demonstrate the method. The results showed that the PP-PSO algorithm can process high-dimensional information and obtain the objective weight of each index. The 3D information from the influenced buildings had an impact on the evaluation results, which needed to be considered. Through the 3D visualization analysis, the overall distribution of flood risk and that around the buildings were obtained in multi-perspectives. The flood risk during different rainfall return periods were analyzed intuitively and comparatively. This study furnishes a novel method for flood risk assessment and analysis by making the most of 3D spatial information. • A 3D visualization assessment and analysis method for flood risk was established. • The 3D information from the influenced buildings had an impact on flood risk. • The assessment results were analyzed in 3D coupling with multi-source data. [ABSTRACT FROM AUTHOR]

Monitoring on urban water environment and analysis of engineering improvement measures are intricate and time-consuming tasks. In previous studies, the integration of hydrodynamic and water quality models and geographical information system (GIS) usually takes three approaches: loose coupling, tight coupling, and full coupling. However, this paper adopted a special loose coupling approach-case-based reasoning (CBR) to develop an integrated decision support system. This was characterized by invoking the case base stored in the GIS platform as the output of the model. The fused capability of model's water quality predication and strong spatial data processing analysis of GIS can be realized at the same time by integration. The functionality of the integrated system was illustrated through a case study of Chaohu, a medium-sized city in China, which includes case retrieval, result interpretation, and the visual display in the GIS platform. Results verified the feasibility and operability of the developed method. As a useful tool, the integrated decision support system makes it simpler and more convenient for decision makers to make decisions efficiently and quickly.