Case studies in dynamic modelling of large-scale wastewater treatment plants

Several large-scale wastewater treatment plants have been modelled successfully using an extensive modelling library in the General Purpose Simulator, GPS-X. The mathematical models included all important processes on the selected wastewater treatment plants, i.e. primary sedimentation, activated sludge biokinetics and final clarification.The models have been used in different areas, such as: a)analysis of operational scenarios; b) determenation of sustained and peak capacity of plants(rated plant capacity) and c) investigation of plant expansion scenarios. Out of more than twenty documented cases four, involving simulation studies on large-scale wastewater plants are discussed.     

Quality control of wastewater treatment: A new approach

This paper presents a new approach to quality control of wastewater treatment. The first part formulates basic principles of statistical process control (SPC) and Taguchi Method. Then it is shown that the classical SPC technique used in industry, cannot be to applied to wastewater treatment plants without adaptation and that the Taguchi Method is inapplicable in this case. This is followed by an example from literature, which demonstrates the problems of applying the SPC method to wastewater treatment. The third part of the paper presents a case study where the performance of a greywater treatment plant is examined. The performance is analyzed by means of cross-correlation between input and output parameters. A new approach to SPC of wastewater treatment, either “Dynamic SPC” or “linear regression SPC”, is presented, and a permeability coefficient is developed (the ratio of the output and input energies). Both are proposed as monitoring tools for wastewater treatment systems.     

Modeling,simulation and adaptive process control of biotechnological processes in decentralized anaerobic treatment of domestic wastewater

Control in anaerobic wastewater treatment plants is difficult to achieve but necessary due to a high sensitivity to disturbances and process complexity. With the help of different mathematical tools, control strategies can be developed. Particularly, a well-defined mathematical model can be highly effective for design, assessment and optimization of treatment plants. However, applications directly in the control system of a treatment plant are hard to achieve due to model complexity and usually require specialized software and the engagement of experts in the subject. The objective of the present study was the development of less empirical methods for assessment and control of a decentralized anaerobic plant for the treatment of domestic wastewater. A lab-scale plant, which consisted of a two-stage anaerobic digestion process followed by an anaerobic ammonium oxidation (ANAMMOX) reactor for nitrogen removal, was used as object of study. Ordinary differential equation models were implemented to simulate the processes that took place in the treatment plant. With the help of the implemented models, control tools were developed. These tools include a standalone application for monitoring of the two-stage anaerobic digestion process and an ammonium estimator for the ANAMMOX reactor by means of artificial neural networks (ANNs). The procedures followed aimed to reduce the amount of experimental work required so they can be easily transferred from laboratory to full-scale conditions. (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Statistical identification and optimal control of thermal power plants

Statistical system identification and its use for the optimal control of thermal power plants are discussed. The analysis of the plant dynamics and derivation of the state-space representation are performed by fitting a multivariate AR model to the plant data obtained by an experiment. The basic concept of the power plant control and the motivation that necessitated the statistical approach are explained in the introduction. Practical procedure for the implementation of the method is described in detail with examples obtained from actual plants. The main items discussed are the selection of system variables by means of relative power contribution analysis, determination of the state equation and adjustment of the optimal feedback gain by digital simulation technique. Finally, excellent performance of the proposed control system is demonstrated by the operating records of 500 MW and 600 MW supercritical plants.     

Reduction of overestimation in interval arithmetic simulation of biological wastewater treatment processes

A novel interval arithmetic simulation approach is introduced in order to evaluate the performance of biological wastewater treatment processes. Such processes are typically modeled as dynamical systems where the reaction kinetics appears as additive nonlinearity in state. In the calculation of guaranteed bounds of state variables uncertain parameters and uncertain initial conditions are considered. The recursive evaluation of such systems of nonlinear state equations yields overestimation of the state variables that is accumulating over the simulation time. To cope with this wrapping effect, innovative splitting and merging criteria based on a recursive uncertain linear transformation of the state variables are discussed. Additionally, re-approximation strategies for regions in the state space calculated by interval arithmetic techniques using disjoint subintervals improve the simulation quality significantly if these regions are described by several overlapping subintervals. This simulation approach is used to find a practical compromise between computational effort and simulation quality. It is pointed out how these splitting and merging algorithms can be combined with other methods that aim at the reduction of overestimation by applying consistency techniques. Simulation results are presented for a simplified reduced-order model of the reduction of organic matter in the activated sludge process of biological wastewater treatment.     

Non-linear model parameter estimation–estimating a feasible parameter set with respect to model use

This article deals with non-linear model parameter estimation from experimental data. As for non-linear models a rigorous identifiability analysis is difficult to perform, parameter estimation is performed in such a way that uncertainty in the estimated parameter values is represented by the range of model use results when the model is used for a certain purpose. Using this approach, the article presents a simulation study where the objective is to discover whether the estimation of model parameters can be improved, so that a small enough range of model use results is obtained. The results of the study indicate that from plant measurements available for the estimation of model parameters, it is possible to extract data that are important for the estimation of model parameters relative to a certain model use. If these data are improved by a proper measurement campaign (e.g. proper choice of measured variables, better accuracy, higher measurement frequency) it is to be expected that a valid model for a certain model use will be obtained. The simulation study is performed for an activated sludge model from wastewater treatment, while the estimation of model parameters is done by Monte Carlo simulation.     

Robust control system design for polytopic stable LPV systems

We investigate robust control system design for polytopic stablelinear parameter-varying (LPV) plants using prior and non-real-timeknowledge of the parameter. A gain-scheduled framework and robustmodel matching (RMM) strategy are combined to develop controllers.First, a self-scheduled H-infinity method is applied to designa nominal controller using a known parameter. Then a robustcompensator is added in order to reduce the influence of parameterperturbation due to the real parameter's deviation from thenominal parameter. Thus, a RMM design method that is a practicalapproach to the design of attachable robust compensators forthe linear time-invariant plant, is extended in applicationto the LPV plant. Finally, robust stability of the overall systemfor possible parameter trajectories is confirmed. A design exampleand simulation results are presented in order to demonstratethe proposed method.     

Adaptation of the plant location model for regional environmental facilities and cost allocation strategy

This paper is divided into two parts. In the first part of the paper, the plant location model is adapted for the special case of siting wastewater treatment facilities when the wastewater sources and treatment facilities are arranged in a chain or linear configuration. In this problem, flows or shipments may be merged in common pipes that provide economies of scale in transport. In order to apply the plant location model, an appropriate definition of the additional cost incurred when a waste source joins a regional facility is required. In addition, sequential priority constraints are developed in the siting model in order to make possible proper accounting of transport costs. The new siting model can be conveniently solved by linear programming.In the second part of the paper, a dual of the plant location model is explored as a cost allocation method for the fixed charge facility siting problem. The constraint sets of the dual model can be shown to imply the core conditions of the related cost game; hence, a set of the dual variables from the dual problem can be regarded as rational cost allocations. The analysis places both facility siting and cost allocation in a common framework.     

Feedback methods for inverse simulation of dynamic models for engineering systems applications

Inverse simulation is a form of inverse modelling in which computer simulation methods are used to find the time histories of input variables that, for a given model, match a set of required output responses. Conventional inverse simulation methods for dynamic models are computationally intensive and can present difficulties for high-speed applications. This article includes a review of established methods of inverse simulation, giving some emphasis on iterative techniques that were first developed for aeronautical applications. It goes on to discuss the application of a different approach that is based on feedback principles. This feedback method is suitable for a wide range of linear and non-linear dynamic models and involves two distinct stages. The first stage involves design of a feedback loop around the given simulation model, and in the second stage, that closed-loop system is used for inversion of the model. Issues of robustness within closed-loop systems used in inverse simulation are not significant as there are no plant uncertainties or external disturbances. Thus, the process is simpler than that required for the development of a control system of equivalent complexity. Engineering applications of this feedback approach to inverse simulation are described through case studies that put particular emphasis on non-linear and multi-input multi-output models.     

Input-output dynamic model for optimal environmental pollution control

This paper describes the allocation of a wastewater treatment fund within a region based on a dynamic input-output model. Considering the complexity of the input-output process, many indeterminate factors must be included in the model. For example, with the aging of machines, an unexpected loss will be caused by the retention of raw materials during an operation; this can be realistically considered as a random variable, because of the sufficiently large amount of historical data. By contrast, actions such as a temporary transfer or inexperienced operators can only be regard as uncertain variables, because of a lack of historical data. First, the pollution control model is formulated in an uncertain environment by including both human uncertainty and objective randomness. Second, an optimal control model subject to an uncertain random singular system is established; this model can be transformed into an equivalent optimization problem. To solve such a problem, recurrence equations are presented based on Bellman’s principle, and these were successfully applied to address the optimal control problem in two special cases. Moreover, two algorithms are formulated for solving the pollution control problem. Finally, the optimal distribution strategies of the pollution control fund used to control the emissions of COD and NH₃-H, which are two indicators of wastewater in China, were obtained through the proposed algorithms.     

Optimal investment in the reclamation of eutrophic water bodies

Eutrophication, i.e., the abnormal growth of phytoplankton, is considered in this note, which focuses on the optimal treatment of eutrophic water bodies. The issue is addressed by the use of a nonlinear model where phytoplankton and the number of wastewater treatment plants in operation are the state variables. The decision maker is a governmental agency which has to define the time pattern of investment in new plants so as to minimize the present value of environmental and treatment costs. The optimal solution is shown to have the following features. First, the optimal size for the wastewater treatment system is attained in minimum time. Subsequently, investment replaces wornout treatment plants, and phytoplankton adjusts asymptotically to its optimal equilibrium value.This work was supported by Centro Teoria dei Sistemi—CNR, Milano and by Fondazione ENI E. Mattei.     

Emergy evaluations for constructed wetland and conventional wastewater treatments

Based on emergy synthesis, this study presents a comparative study on constructed wetland (CW) and conventional wastewater treatments with three representative cases in Beijing. Accounting the environmental and economic inputs and treated wastewater output based on emergy, different characteristics of two kinds of wastewater treatments are revealed. The results show that CWs are environment-benign, less energy-intensive despite the relatively low ecological waste removal efficiency (EWRE), and less cost in construction, operation and maintenance compared with the conventional wastewater treatment plants. In addition, manifested by the emergy analysis, the cyclic activated sludge system (CASS) has the merit of higher ecological waste elimination efficiency.     

Simulation-based optimisation of wastewater flow and composition for on-site treatment in the food and beverage industry utilising reference nets in combination with genetic algorithms

This study introduces the concept of computer modelling and simulation of complex bioprocesses and systems using an approach that combines the reference net formalism with machine learning and optimisation techniques. Reference nets are an extension of high level Petri Nets, which can be used as a central visualisation and modelling tool. The net-in-net paradigm used by reference nets makes it possible to model complex processes, such as those found in the food and beverage industry. A plugin/interface system based on the java programming language allows implementation of advanced mathematical modelling techniques at specific points in entire system simulations. Separate optimisation tools can also run and modify existing reference net models for fast solutions to efficiency problems. We present an example system that simulates a specific section of a beer brewery using the reference net formalism, which is optimised using a genetic algorithm. We show in detail how the different software packages can be combined for a simulation based optimisation approach. The optimisation technique specifically addresses the wastewater pollution load in regard to its chemical oxygen demand. A beer brewery was chosen as an example for this study due to the constantly increasing requirements to lower energy and water consumption in this industry. One possibility to lower the energy and water demands is to effectively treat wastewater produced by the brewery, which can introduce cost savings by providing recycled water and biogas. Most approaches to wastewater treatment are end-of-pipe solutions that do not consider the brewery as a whole. A brewery contains many processes that can be running concurrently and interacting with one another (e.g. brewing, clean-in-place and bottling) with each process producing varying amounts of wastewater with different pollution loads. Optimisation of the scheduling of the different processes with respect to the wastewater production will allow for more effective wastewater treatment, and therefore cost and energy savings. (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

INDUSTRIAL WASTEWATER CONTROL IN CHINESE CITIES: DETERMINANTS OF SUCCESS IN ENVIRONMENTAL POLICY

The research reported in this paper has three objectives: (1) to examine trends in Chinese industrial wastewater pollution and to describe policies developed to control this pollution; (2) to analyze relationships between the degree of industrial wastewater pollution control and the economic, demographic, and organizational characteristics of major Chinese cities; and (3) to identify factors that determine the success of the government's efforts to control industrial wastewater pollution. The study specifies and econometrically estimates a system of pollution control equations based on a unique data set for 82 Chinese cities from 1985 to 1990. Results demonstrate that the structure of enterprise ownership and investment in environmental management capacity are associated with increases in the effectiveness of wastewater pollution control. The Discharge Permit System, one of China's main strategies for urban environmental protection, appears to have been moderately successful in increasing wastewater treatment, but it has not encouraged enterprise managers to adopt less-polluting production technology.     

A multiobjective optimization model for processing and distributing biosolids to reuse fields

Advanced wastewater treatment plants remove deleterious nutrients, chemicals, and microorganisms from wastewater and produce biosolids products to be reused at farms and other sites. These biosolids are carefully regulated by environmental restrictions but still may be malodorous to the local populations. In this paper, we develop a multiobjective optimization model to simultaneously minimize the biosolids odours as well as processing and distribution costs. The model employs a linear odour function and bilinear costs; the latter being approximated via Schur's decomposition and special ordered set (SOS) type 2 variables resulting in a mixed integer linear multiobjective optimization problem. Such a model can be used proactively by these plants to produce the least malodorous product at minimal costs. We demonstrate use of the model with a case study for the Blue Plains advanced wastewater treatment plant run by the DC Water and Sewer Authority in Washington, DC.     

车站咽喉通过能力可视化计算集成系统的研究

在对国内外咽喉道岔通过能力计算方法比较与分析基础上 ,论文提出了车站咽喉通过能力可视化计算集成系统的系统结构和功能设计 ,应用图论和网络优化技术对车站咽喉布置、咽喉及车站作业过程进行抽象建模 ,建立了咽喉作业占用安排网络优化和咽喉作业能力的复合递阶决策模型 .该研究综合运用上述模型、算法、计算机信息处理技术和图形技术 ,可视化模拟咽喉作业情况 ,实现了核心基础数据计算机模拟采集、作业优化安排和最大可实现能力的查定及优化 ,并将结果以图形方式输出 .通过实例验证表明 ,计算结果合理可信 ,可用于车站咽喉能力的评价检验 .     

Modelling and hierarchical learning control of a dryer

This paper deals with modelling and hierarchical learning control of an industrial phosphate dryer. The model is derived from heat and mass balances. It consists of hyperbolic nonlinear partial differential equations. The control and the output variables appear at the boundary conditions. The method of characteristics that is based on two independent variables is used for numerical simulation purposes. The control objectives are to minimize fuel consumption and to keep the moisture content of dried phosphate close to a desired value, despite external perturbations acting on the drying process. The fuel flow and the dried product moisture content have been selected as control variables. A hierarchical learning system, operating in a random environment that corresponds to the dryer, is used for control purposes. During its operation the learning system collects the pertinent information about the variables that describe the process to be controlled and generates a control action. The obtained results show the good performance characteristics of the considered controller.     

Online identification of Takagi–Sugeno fuzzy models based on self-adaptive hierarchical particle swarm optimization algorithm

This paper presents an approach for online learning of Takagi–Sugeno (T-S) fuzzy models. A novel learning algorithm based on a Hierarchical Particle Swarm Optimization (HPSO) is introduced to automatically extract all fuzzy logic system (FLS)’s parameters of a T–S fuzzy model. During online operation, both the consequent parameters of the T–S fuzzy model and the PSO inertia weight are continually updated when new data becomes available. By applying this concept to the learning algorithm, a new type T–S fuzzy modeling approach is constructed where the proposed HPSO algorithm includes an adaptive procedure and becomes a self-adaptive HPSO (S-AHPSO) algorithm usable in real-time processes. To improve the computational time of the proposed HPSO, particles positions are initialized by using an efficient unsupervised fuzzy clustering algorithm (UFCA). The UFCA combines the K-nearest neighbour and fuzzy C-means methods into a fuzzy modeling method for partitioning of the input–output data and identifying the antecedent parameters of the fuzzy system, enhancing the HPSO’s tuning. The approach is applied to identify the dynamical behavior of the dissolved oxygen concentration in an activated sludge reactor within a wastewater treatment plant. The results show that the proposed approach can identify nonlinear systems satisfactorily, and reveal superior performance of the proposed methods when compared with other state of the art methods. Moreover, the methodologies proposed in this paper can be involved in wider applications in a number of fields such as model predictive control, direct controller design, unsupervised clustering, motion detection, and robotics.     

A control problem related to wastewater treatment

The management of a wastewater treatment system is studied as a pointwise control problem with state constraints. A theoretical analysis of the problem is developed, a numerical algorithm is proposed and numerical results are given.     

A Plant Virus Disease Model with Periodic Environment and Pulse Roguing

Regular roguing is an effective method to control plant virus diseases. In this paper, a compartmental mathematical model is established to represent the dynamics of plant disease in a periodic environment, including impulsive roguing control strategy. The basic reproductive number and its relation to the persistence of the disease is discussed via using next infection operation. Numerical simulations are performed to demonstrate the theoretical findings, and to illustrate the effect of control measures. Our results show that, (i) when the infection rate is high, it may be impossible to eradicate the disease by simply roguing the infectious plant, so how to effectively identify the latent plant is a key issue in disease control, (ii) increasing the replanting rate is bad for disease control, (iii) the published autonomous research model with continuous roguing may overestimate the infectious risk inherent to impulsive control.