城市排污系统对河流水质影响的仿真研究

以城市废水系统的集成化管理为出发点，基于国际水协会河流水质1号模型(RWOMI)，确定了适合仿真与实时控制的简化的动态河流水质模型，在MATLAB仿真环境下建立了一个以河流为核心的集成化城市废水系统的简单仿真环境。以某河流城区段为背景，采用国际水协会发布的污水负荷和组分数据作为原始输入，模拟了排水管网和污水处理厂在不同运行条件下引起的河流水质的变化情况。仿真结果表明排水管网组合下水溢流和污水处理厂排放物的处理程度对河流水质会产生直接影响，为集成化管理提供了依据。     

Scenario analysis for the role of sanitation infrastructures in integrated urban wastewater management

Traditionally, the sanitation infrastructures of most of the Urban Wastewater Systems (UWSs) have been managed individually, without considering the many relationships among the sewer systems, Wastewater Treatment Plants (WWTPs) and receiving waters. The main objective of WWTP management was to comply with the emission limits, without considering the ecological state of the receiving waters. However, the European Union approved the Water Framework Directive (WFD) in 2000 that changes the conventional practice by introducing the integrated approach concept in the hydraulic infrastructure management. The same Directive also promotes the availability and use of decision support tools for water management, specifically where water resources are becoming increasingly scarce. This paper describes the work conducted in the Besòs catchment (Catalonia, NE of Spain) in order to deal with this European legislation. A study site was selected to develop an integrated model as a support tool for the UWS management. Specifically, two sewer systems, their WWTPs and a reach of the Congost River (a tributary of the Besòs River) have been modelled. The selected software to model flow and water quality were Infoworks CS, GPS-X and Infoworks RS for the sewer systems, WWTPs and stream reach, respectively. Besides these, a specific program was developed to be used as a data transfer interface between software. Once this model integration platform was built, and taking into account the expert knowledge of the managers, several management scenarios were defined including some critical events such as industrial spills, rainfall episodes, inhibition of nitrification, WWTP shutdowns, obstruction of a sewer system conduit and episodes of minimum river flow rates as well as potential control actions such as the implementation of storage tanks or the use of bypasses between sewer systems or WWTPs. All these scenarios were modelled and simulated and the results obtained were then analysed, focusing the attention on the river water quality, with the main objective being to gain relevant knowledge to deal with the tested scenarios.     

Catchment & sewer network simulation model to benchmark control strategies within urban wastewater systems

This paper aims at developing a benchmark simulation model to evaluate control strategies for the urban catchment and sewer network. Various modules describing wastewater generation in the catchment, its subsequent transport and storage in the sewer system are presented. Global/local overflow based evaluation criteria describing the cumulative and acute effects are presented. Simulation results show that the proposed set of models is capable of generating daily, weekly and seasonal variations as well as describing the effect of rain events on wastewater characteristics. Two sets of case studies explaining possible applications of the proposed model for evaluation of: 1) Control strategies; and, 2) System modifications, are provided. The proposed framework is specifically designed to allow for easy development and comparison of multiple control possibilities and integration with existing/standard wastewater treatment models (Activated Sludge Models) to finally promote integrated assessment of urban wastewater systems.     

OpenMI-based integrated sediment transport modelling of the river Zenne,Belgium

Recent observations show that the river Zenne (Belgium) remains well below the water quality goals stated by the European Union Water Framework Directive. An interuniversity, multidisciplinary research project was therefore launched to evaluate the effects of wastewater management plans on the ecological functioning of the river. To this end, different water quantity and quality processes had to be considered and modelled, e.g., the hydrology in the river basin, hydraulics in the river and sewers, erosion and sediment transport, faecal bacteria transport and decay. This paper considers the development of an Open Modelling Interface (OpenMI) based integrated model for the purpose of simulating the river's sediment dynamics. We used the Soil and Water Assessment Tool (SWAT) to model water and sediment fluxes from rural areas. The Storm Water Management Model (SWMM) was used to simulate the hydraulics of the river, canal, and sewer systems in urban catchments. New model codes for sediment transport and stream water temperature were developed to complement SWMM. The results show that the integrated sediment transport model reproduced the sediment concentrations in the river Zenne with ‘good’ to ‘satisfactory’ accuracy. We may therefore conclude that the OpenMI has been successfully implemented to integrate water quality models into a hydraulic one. While the OpenMI run-time data communication inflicted calculation time overhead, we found that the overhead was not significant with respect to the total run-time of the integrated model.     

Integrated urban hydrologic and hydraulic modelling in Chicago,Illinois

With rapidly growing urbanization, urban flooding and water quality control are becoming a vital component of sustainable urban infrastructure. Integrated urban hydrologic and hydraulic modelling represents a potential framework for capturing system interactivity and optimizing the design, operation, and engineering of urban systems. This work describes how a widely-used hydrodynamic model, the Environmental Fluid Dynamics Code (EFDC), was made compliant with the Open Modelling Interface (OpenMI) standard. The new version of the EFDC has potential to be coupled with any OpenMI-compliant model for various studies. As an example, this paper presents an application of the OpenMI version of EFDC coupled with InfoWorks-CS for a representative highly urbanized area in the city of Chicago, Illinois, United States. The integrated modelling simulates the two-way linkage between the sewer network and the Chicago Area Waterway System (CAWS). This interaction between the river and sewer pipe systems can not be described by decoupled models. By coupling the models, it is possible to observe the interaction between the sewer system overflow discharge and the hydraulic head in the pipe network. This is particularly important since higher water levels in the pipe system increase the potential for flooding.     

CITY DRAIN © – An open source approach for simulation of integrated urban drainage systems

In the last years design procedures of urban drainage systems have shifted from end of pipe design criteria to ambient water quality approaches requiring integrated models of the system for evaluation of measures. Emphasis is put on the improvement of the receiving water quality and the overall management of river basins, which is a core element of the Water Framework Directive (WFD) as well.Typically, it is not necessary to model the whole variety of effects on the receiving water but to focus on the few dominating ones. Only pollutants and processes that have a direct and significant influence on the selected impacts need to be described quantitatively, whereas all other processes can be neglected. Hence, pragmatism is required to avoid unnecessary complexity of integrated models. This is as well true for software being used in daily engineering work, requiring simplicity in handling and a certain flexibility to be adjusted for different scenarios.CITY DRAIN © was developed to serve these needs. Therefore it was developed in the Matlab/Simulink © environment, enabling a block wise modelling of the different parts of the urban drainage system (catchment, sewer system, storage devises, receiving water, etc.). Each block represents a system element (subsystem) with different underlying modelling approaches for hydraulics and mass transport. The different subsystems can be freely arranged and connected to each other in order to describe an integrated urban drainage system. The open structure of the software allows to add own blocks and/or modify blocks (and underlying models) according to the specific needs.The application of CITY DRAIN is shown within the integrated modelling case study Vils/Reutte. Further additional applications for CITY DRAIN, including batch simulations, real time control (RTC) and model based predictive control (MBPC) are presented and discussed.     

Performance evaluation of real time control in urban wastewater systems in practice: Review and perspective

Real time control (RTC) is generally viewed as a viable method for optimising the performance of urban wastewater systems. A literature review on the performance evaluation of RTC demonstrated a lack of consensus on how to do this. Two main deficiencies were identified: omitting uncertainty analysis and applying limited evaluation periods. A general methodology to evaluate the performance of RTC in practice, that takes into account these deficiencies, is proposed. The methodology is either data or model driven and the (dis)advantages of each are discussed. In a case study for a combined sewer system with limited discharge to a WWTP, it is demonstrated that the successful application of RTC and the possibility to determine a significant effect is very much dependent on the goal. It also clearly illustrates the need for taking uncertainties into account and that careful consideration in the chosen evaluation period is required.     

Dynamic influent pollutant disturbance scenario generation using a phenomenological modelling approach

Activated Sludge Models are widely used for simulation-based evaluation of wastewater treatment plant (WWTP) performance. However, due to the high workload and cost of a measuring campaign on a full-scale WWTP, many simulation studies suffer from lack of sufficiently long influent flow rate and concentration time series representing realistic wastewater influent dynamics. In this paper, a simple phenomenological modelling approach is proposed as an alternative to generate dynamic influent pollutant disturbance scenarios. The presented set of models is constructed following the principles of parsimony (limiting the number of parameters as much as possible), transparency (using parameters with physical meaning where possible) and flexibility (easily extendable to other applications where long dynamic influent time series are needed). The proposed approach is sub-divided in four main model blocks: 1) model block for flow rate generation, 2) model block for pollutants generation (carbon, nitrogen and phosphorus), 3) model block for temperature generation and 4) model block for transport of water and pollutants. The paper is illustrated with the results obtained during the development of the dynamic influent of the Benchmark Simulation Model no. 2 (BSM2). The series of simulations show that it is possible to generate a dry weather influent describing diurnal flow rate dynamics (low rate at night, high rate during day time), weekend effects (with different flow rate during weekends, compared to weekdays), holiday effects (where the wastewater production is assumed to be different for a number of weeks) and seasonal effects (with variations in the infiltration and thus also the flow rate to the WWTP). In addition, the dry weather model can be extended with a rain and storm weather generator, where the proposed phenomenological model can also mimic the “first flush” effect from the sewer network and the influent dilution phenomena that are typically observed at full-scale WWTPs following a rain event. Finally, the extension of the sewer system can be incorporated in the influent dynamics as well: the larger the simulated sewer network, the smoother the simulated diurnal flow rate and concentration variations. In the discussion, it is pointed out how the proposed phenomenological models can be expanded to other applications, for example to represent heavy metal or organic micro-pollutant loads entering the treatment plant.     

Artificial neural networks for rapid WWTP performance evaluation: Methodology and case study

Reliable performance evaluation of wastewater treatment plants (WWTPs) can be done by simulating the plant behavior over a wide range of influent disturbances, including series of rain events with different intensity and duration, seasonal temperature variations, holiday effects, etc. Such simulation-based WWTP performance evaluations are in practice limited by the long simulation time of the mechanistic WWTP models. By moderate simplification (avoiding big losses in prediction accuracy) of the mechanistic WWTP model only a limited reduction of the simulation time can be achieved. The approach proposed in this paper combines an influent disturbance generator with a mechanistic WWTP model for generating a limited sequence of training data (4 months of dynamic data). An artificial neural network (ANN) is then trained on the available WWTP input-output data, and is subsequently used to simulate the remainder of the influent time series (20 years of dynamic data) generated with the influent disturbance generator. It is demonstrated that the ANN reduces simulation time by a factor of 36, even when including the time needed for the generation of training data and for ANN training. For repeated integrated urban wastewater system simulations that do not require repeated training of the ANN, the ANN reduces simulation time by a factor of 1300 compared to the mechanistic model. ANN prediction of effluent ammonium, BOD₅ and total suspended solids was good when compared to mechanistic WWTP model predictions, whereas prediction of effluent COD and total nitrogen concentrations was a bit less satisfactory. With correlation coefficients R² > 0.95 and prediction errors lower than 10%, the accuracy of the ANN is sufficient for applications in simulation-based WWTP design and simulation of integrated urban wastewater systems, especially when taking into account the uncertainties related to mechanistic WWTP modeling.     

Multi‐Variable Direct Self‐Organizing Fuzzy Neural Network Control for Wastewater Treatment Process

A multi‐variable direct self‐organizing fuzzy neural network control (M‐DSNNC) method is proposed for the multi‐variable control of the wastewater treatment process (WWTP). In this paper, the proposed control system is an essential multi‐variable control method for the WWTP. No exact plant model is required, which avoids the difficulty of establishing the mathematics model of WWTP. The M‐DSNNC system is comprised of a fuzzy neural network controller and a compensation controller. The fuzzy neural network is used for approximating the ideal control law under a general nonlinear system. Moreover, the neural network is designed in a self‐organizing mode to adapt the uncertainty environment. Simulation results, based on the international benchmark simulation model No.1 (BSM1), demonstrate that the control accuracy is improved under the proposed M‐DSNNC method, and the controller has a much stronger decoupling ability.     

Dynamic multi-objective optimization control for wastewater treatment process

A dynamic multi-objective optimization control (DMOOC) scheme is proposed in this paper for the wastewater treatment process (WWTP), which can dynamically optimize the set-points of dissolved oxygen concentration and nitrate level with multiple performance indexes simultaneously. To overcome the difficulty of establishing multi-objective optimization (MOO) model for the WWTP, a neural network online modeling method is proposed, requiring only the process data of the plant. Then, the constructed MOO model with constraints is solved based on the NSGA-II (non-dominated sorting genetic algorithm-II), and the optimal set-point vector is selected from the Pareto set using the defined utility function. Simulation results, based on the benchmark simulation model 1 (BSM1), demonstrate that the energy consumption can be significantly reduced applying the DMOOC than the default PID control with the fixed set-points. Moreover, a tradeoff between energy consumption and effluent quality index can be considered.

     

Development and application of an integrated ecological modelling framework to analyze the impact of wastewater discharges on the ecological water quality of rivers

Modelling is an effective tool to investigate the ecological state of water resources. In developing countries, the impact of sanitation infrastructures (e.g. wastewater treatment plants) is typically assessed considering the achievement of legal physicochemical quality standards, but ignoring the ecological water quality (EWQ) of the receiving river. In this paper, we developed a generic integrated ecological modelling framework quantifying the impact of wastewater discharges on the EWQ of the Cauca river (Colombia). The framework is flexible enough to be used in conjunction with different approaches/models and integrates a hydraulic and physicochemical water quality model with aquatic ecological models. Two types of ecological models were developed, habitat suitability models for selected macroinvertebrate groups and ecological assessment models based on a macroinvertebrate biotic index. Four pollution control scenarios were tested. It was found that the foreseen investments in sanitation infrastructure will lead to modest improvements of the EWQ, with an increase lower than six units of the ecological index BMWP-Colombia. Advanced investments, such as the collection and treatment of all wastewater produced by the cities of Cali, Yumbo and Palmira and upgrading of the treatment systems should be considered to achieve a good EWQ. The results show that the integration of ecological models in hydraulic and physicochemical water quality models (e.g. MIKE 11) has an added value for decision support in river management and water policy. The integration of models is a key aspect for the success in environmental decision making. The main limitation of this approach is the availability of physicochemical, hydraulic and biological data that are collected simultaneously. Therefore, a change in the river monitoring strategy towards collection of data which include simultaneous measurements of these variables is required.     

Influent generator for probabilistic modeling of nutrient removal wastewater treatment plants

The availability of influent wastewater time series is crucial when using models to assess the performance of a wastewater treatment plant (WWTP) under dynamic flow and loading conditions. Given the difficulty of collecting sufficient data, synthetic generation could be the only option. In this paper a hybrid of statistical (a Markov chain-gamma model for stochastic generation of rainfall and two different multivariate autoregressive models for stochastic generation of air temperature and influent time series in dry conditions) and conceptual modeling techniques is proposed for synthetic generation of influent time series. The time series of rainfall and influent in dry weather conditions are generated using two types of statistical models. These two time series serve as inputs to a conceptual sewer model for generation of influent time series. The application of the proposed influent generator to the Eindhoven WWTP shows that it is a powerful tool for realistic generation of influent time series and is well-suited for probabilistic design of WWTPs as it considers both the effect of input variability and total model uncertainty.     

一类多输入多输出网络控制系统的稳定性分析

研究了具有分布时延、有界输入的多输入多输出网络控制系统的建模和稳定性问题.基于线性时不变对象,建立了系统的数学模型.利用李雅普诺夫第2方法和线性矩阵不等式描述,分析了系统的渐近稳定性,导出了与时延相关的系统渐近稳定判据,同时得到了系统稳定运行的最大允许时延.系统渐近稳定的最大允许时延可用MatlabLMI工具箱从稳定判据获得.仿真算例表明稳定判据是可行的.     

Nonlinear multiobjective model-predictive control scheme for wastewater treatment process

A nonlinear multiobjective model-predictive control (NMMPC) scheme, consisting of self-organizing radial basis function (SORBF) neural network prediction and multiobjective gradient optimization, is proposed for wastewater treatment process (WWTP) in this paper. The proposed NMMPC comprises a SORBF neural network identifier and a multiple objectives controller via the multi-gradient method (MGM). The SORBF neural network with concurrent structure and parameter learning is developed as a model identifier for approximating on-line the states of WWTP. Then, this NMMPC optimizes the multiple objectives under different operating functions, where all the objectives are minimized simultaneously. The solution of optimal control is based on the MGM which can shorten the solution time. Moreover, the stability and control performance of the closed-loop control system are well studied. Numerical simulations reveal that the proposed control strategy gives satisfactory tracking and disturbance rejection performance for WWTP. Experimental results show the efficacy of the proposed method.     

基于隐性记忆的非平稳时变污水处理过程多目标运行优化

针对污水处理过程中运行能耗大和水质超标严重等问题,基于隐性记忆策略,提出一种适用于无规律且非平稳时变过程的多目标运行优化方法.首先,采用集成即时学习在线建模算法建立运行指标模型,实现时变工况下运行优化目标的准确描述;然后,提出基于隐性记忆的多目标优化算法,通过充分考虑复杂时变工况,挖掘埋藏在历史优化信息中的结构化知识,引导进化搜索过程,并结合平均距离聚类指标进行均匀随机局部搜索,提高算法收敛性、多样性和分布性;最后,利用基于集成即时学习的智能决策方法,获取操作变量溶解氧浓度和硝态氮浓度的优化设定值并进行跟踪控制.数据实验表明:所提出方法能够实现污水处理过程的稳定运行,在满足操作限制的情况下,提高污染物去除性能,降低运行成本.     

Minimization of combined sewer overflows by large-scale mathematical programming

There is a critical need for the development and implementation of control strategies for minimizing receiving water pollution caused by overflows from urban combined sewer systems. Automation and control of such systems has risen as a viable approach to this problem. Control is carried out through regulation of ambient and/or auxiliary storage in the system. The goal is to detain storm flows in the system long enough to prevent flows of a magnitude that exceeds treatment plant capacity, thereby reducing overflow into receiving waters. The problem is first attacked by dividing the large combined sewer system into several mildly interconnected subsystems for which control logic can be developed from application of mathematical programming algorithms consistent with the special characteristics of each subsystem. A flow-projection technique is developed for subsystems not amenable to direct solution by standard optimization techniques.     

Modeling of Energy Consumption and Effluent Quality Using Density Peaks-based Adaptive Fuzzy Neural Network

Modeling of energy consumption (EC) and effluent quality (EQ) are very essential problems that need to be solved for the multiobjective optimal control in the wastewater treatment process (WWTP). To address this issue, a density peaks-based adaptive fuzzy neural network (DP-AFNN) is proposed in this study. To obtain suitable fuzzy rules, a DP-based clustering method is applied to fit the cluster centers to process nonlinearity. The parameters of the extracted fuzzy rules are fine-tuned based on the improved Levenberg-Marquardt algorithm during the training process. Furthermore, the analysis of convergence is performed to guarantee the successful application of the DPAFNN. Finally, the proposed DP-AFNN is utilized to develop the models of EC and EQ in the WWTP. The experimental results show that the proposed DP-AFNN can achieve fast convergence speed and high prediction accuracy in comparison with some existing methods.      

Neural-optimal control algorithm for real-time regulation of in-line storage in combined sewer systems

Attempts at implementing real-time control systems as a cost-effective means of minimizing the pollution impacts of untreated combined sewer overflows have largely been unsustained due to the complexity of the real-time control problem. Optimal real-time regulation of flows and in-line storage in combined sewer systems is challenging due to the need for complex optimization models integrated with urban stormwater runoff prediction and fully dynamic routing of sewer flows within 5–15 min computational time increments. A neural-optimal control algorithm is presented that fully incorporates the complexities of dynamic, unsteady hydraulic modeling of combined sewer system flows and optimal coordinated, system-wide regulation of in-line storage. The neural-optimal control module is based on a recurrent Jordan neural network architecture that is trained using optimal policies produced by a dynamic optimal control module. The neural-optimal control algorithm is demonstrated in a simulated real-time control experiment for the King County combined sewer system, Seattle, Washington, USA. The algorithm exhibits an effective adaptive learning capability that results in near-optimal performance of the control system while satisfying the time constraints of real-time implementation.     

An emergent approach for the control of wastewater treatment plants by means of reinforcement learning techniques

One of the main problems in the automation of the control of wastewater treatment plants (WWTPs) appears when the control system does not respond as it should because of changes on influent load or flow. To tackle this difficult task, the application of Artificial Intelligence is not new, and in fact, currently Expert Systems may supervise the plant 24 h/day assisting the plant operators in their daily work. However, the knowledge of the Expert System must be elicited previously from interviews to plant operators and/or extracted from data previously stored in databases. Although this approach still has a place in the control of wastewater treatment plants, it should aim to develop autonomous systems that learn from the direct interaction with the WWTP and that can operate taking into account changing environmental circumstances. In this paper we present an approach based on an agent with learning capabilities. In this approach, the agent’s knowledge emerges from the interaction with the plant. In order to show the validity of our assertions, we have implemented such an emergent approach for the N-Ammonia removal process in a well established simulated WWTP known as Benchmark Simulation Model No.1 (BSM1).