Dynamical Analysis and Robust Control Synthesis for Water Treatment Processes

Nowadays, water demand and water scarcity are very urgent issues due to population growth, drought and poor water quality all over the world. Therefore, water treatment plants are playing a vital role for good living condition of human. Water area needs more concentration study to increase water productivity and decrease water cost. This dissertation presents the analysis and control of water treatment plants using robust control techniques. The applied control algorithms include H∞, gain scheduled and observer-based loop-shaping control technique. They are modern control algorithms and very powerful in robust controlling of systems with uncertainties and disturbances. The water treatment plants include a desalination system and a wastewater process. Since fresh water scarcity is getting more serious, the desalination plants are to produce drinking water and wastewater treatment plants give the reusable water. The desalination system is a RO one used to produce drinking water from seawater and brackish water. The nonlinear behaviors of this system is carefully analyzed before the linearization. Due to the uncertainty caused by concentration polarization, the system is linearized using linear state-space parametric uncertainty framework. The system also suffer from many disturbances which water hammer is one of the most influential one. The mixed robust H∞ and μ-synthesis control algorithm is applied to control the RO system coping with large uncertainties, disturbances and noises. The wastewater treatment process is an activated sludge process. This biological process use microorganisms to convert organic and certain inorganic matter from wastewater into cell mass. The process is very complex with many coupled biological and chemical reactions. Due to the large variation in the influent flow, the system is modelized and linearized as a linear parametric varying system using affine parameter-dependent representation. Since the influent flow is quickly variable and easily to be measured, the robust gain scheduled robust controller is applied to deal with the large uncertainty caused by the scheduled parameter. This control algorithm often gives better performances than those of general robust H∞ one. In the wastewater treatment plant, there exist an anaerobic digestion, which is controlled by the observer-based loop-shaping algorithm. The simulations show that all the controllers can effectively deal with large uncertainties, disturbances and noises in water treatment plants. They help improve the system performances and safeties, save energy and reduce product water costs. The studies contribute some potential control approaches for water treatment plants, which is currently a very active research area in the world.Contents ······················································································· iv List of Tables ··············································································· viii List of Figures ··············································································· ix Chapter 1. Introduction ···································································· 1 1.1 Reverse osmosis process ···································································· 2 1.2 Activated sludge process ···································································· 6 1.3 Robust H∞ and gain scheduling control ··················································· 10 Chapter 2. Robust H∞ controller ······················································· 13 2.1 Introduction ·················································································· 13 2.2 Uncertainty modelling ······································································ 13 2.2.1 Unstructured uncertainties ···························································· 14 2.2.2 Parametric uncertainties ······························································· 15 2.2.3 Structured uncertainties ································································ 16 2.2.4 Linear fractional transformation ······················································ 16 2.3 Stability criterion ············································································ 17 2.3.1 Small gain theorem ····································································· 17 2.3.2 Structured singular value (muy) synthesis brief definition ·························· 19 2.4 Robustness analysis and controller design ··············································· 20 2.4.1 Forming generalised plant and N-delta structure ····································· 20 2.4.2 Robustness analysis ···································································· 24 2.5 Reduced controller ·········································································· 26 2.5.1 Truncation ··············································································· 27 2.5.2 Residualization ········································································· 29 2.5.3 Balanced realization···································································· 29 2.5.4 Optimal Hankel norm approximation ················································ 31 Chapter 3. Robust gain scheduling controller ······································· 37 3.1 Introduction ·················································································· 37 3.2 Linear parameter varying (LPV) system ·················································· 39 3.3 Matrix Polytope ·············································································· 40 3.4 Polytope and affine parameter-dependent representation ······························· 41 3.4.1 Polytope representation ································································ 41 3.4.2 Affine parameter-dependent representation ········································· 42 3.5 Quadratic stability of LPV systems and quadratic (robust) H∞ performance ········· 43 3.6 Robust gain scheduling ····································································· 44 3.6.1 LPV system linearization ······························································ 44 3.6.2 Polytope-based gain scheduling ······················································ 45 3.6.3 LFT-based gain scheduling ··························································· 48 Chapter 4. Mixed robust H∞ and μ-synthesis controller applied for a reverse osmosis desalination system ····························································· 52 4.1 RO principles ················································································ 52 4.1.1 Osmosis and reverse osmosis ························································· 52 4.1.2 Dead-end filtration and cross-flow filtration ········································ 53 4.2 Membranes ··················································································· 54 4.2.1 Structure and material ································································· 54 4.2.2 Hollow fine fiber membrane module ················································ 55 4.2.3 Spiral wound membrane module ····················································· 57 4.3 Nonlinear RO modelling and analysis ···················································· 58 4.3.1 RO system introduction ······························································· 58 4.3.2 Modelling ··············································································· 60 4.3.3 Nonlinear analysis ······································································ 62 4.3.4 Concentration polarization ···························································· 64 4.4 Water hammer phenomenon ······························································· 66 4.4.1 Water hammer, column separation and vaporous cavitation ······················ 66 4.4.2 Water hammer analysis and simulation ·············································· 69 4.4.3 Prevention of water hammer effect··················································· 78 4.5 RO linearization ············································································· 79 4.5.1 Nominal linearization ·································································· 79 4.5.2 Uncertainty modeling ·································································· 81 4.5.3 Parametric uncertainty linearization ················································· 83 4.6 Robust H∞ controller design for RO system ·············································· 85 4.6.1 Control of uncertain RO system ······················································ 85 4.6.2 Robustness analysis and H∞ controller design ······································ 86 4.7 Simulation result and discussion··························································· 90 4.8 Conclusion ··················································································· 95 Chapter 5. Robust gain scheduling control of activated sludge process ······· 96 5.1 Introduction about activated sludge process ············································· 96 5.1.1 State variables ·········································································· 98 5.1.2 ASM1 processes ······································································ 100 5.1.3 The control problem of activated sludge process ································· 102 5.2 System modelling ········································································· 104 5.3 Model linearization ········································································ 107 5.4 Robust gain-schedule controller design for activated sludge process ··············· 109 5.5 Simulation result and discussion························································· 115 5.6 Conclusion ················································································· 120 Chapter 6. Observer-based loop-shaping control of anaerobic digestion ···· 121 6.1 Introduction ················································································ 121 6.1.1 Control problem in anaerobic digestion ··········································· 122 6.2 System modelling ········································································· 123 6.3 Controller design ·········································································· 124 6.3.1 H∞ loop-shaping controller ························································· 125 6.3.2 Coprime factor uncertainty ·························································· 126 6.3.3 Control synthesis ····································································· 127 6.4 Simulation result ··········································································· 131 6.5 Conclusion ················································································· 133 Chapter 7. Conclusion ··································································· 134 References ·················································································· 136 Appendices ················································································· 144Docto

State-space approach to nonlinear predictive generalized minimum variance control

A Nonlinear Predictive Generalized Minimum Variance (NPGMV) control algorithm is introduced for the control of nonlinear discrete-time multivariable systems. The plant model is represented by the combination of a very general nonlinear operator and also a linear subsystem which can be open-loop unstable and is represented in state-space model form. The multi-step predictive control cost index to be minimised involves both weighted error and control signal costing terms. The solution for the control law is derived in the time-domain using a general operator representation of the process. The controller includes an internal model of the nonlinear process but because of the assumed structure of the system the state observer is only required to be linear. In the asymptotic case, where the plant is linear, the controller reduces to a state-space version of the well known GPC controller

Model Predictive Control Strategies Performance Evaluation over a Pipeline Transportation System

In several industries using pipelines to transport different products from one point to another is a common and indispensable process, especially at oil/hydrocarbon industries. Thus, optimizing the way this process is carried out must be an issue that cannot be stopped. Therefore, the performance of the control strategy implemented is one way of reaching such optimal operating zones. This study proposes using Model Predictive Control strategies for solving some issues related to the proper operation of pipelines. It is proposed a model based on physics and thermodynamic laws, using MATLAB® as the development environment. This model involves four pumping stations separated by three pipeline sections. Three MPC strategies are developed and implemented. Accordingly, the results indicate that a centralized controller with an antiwindup back-calculation method has the best results among the three configurations used

Identification of Water Hammering for Centrifugal Pump Drive Systems

Water hammering is a significant problem in pumping systems. It damages the pipelines of the pump drastically and needs to identify with an intelligent method. Various conventional methods such as the method of characteristics and wave attenuation methods are available to identify water hammering problems, and the predictive control method is one of the finest and time-saving methods that can identify the anomalies in the system at an early stage such that the device can be saved from total damage and reduce energy loss. In this research, a machine learning (ML) algorithm has used for a predictive control method for the identification of water hammering problems in a pumping system with the help of simulations and experimental-based works. A linear regression algorithm has been used in this work to predict water hammering problems. The efficiency of the algorithm is almost 90% compared to other ML algorithms. Through a Vib Sensor app-based device at different pressures and flow rates, the velocity of the pumping system, a fluctuation between healthy and faulty conditions, and acceleration value at different times have been collected for experimental analysis. A fault created to analyze a water hammering problem in a pumping system by the sudden closing and opening of the valve. When the valve suddenly closed, the kinetic energy in the system changed to elastic resilience, which created a series of positive and negative wave vibrations in the pipe. The present work concentrates on the water hammering problem of centrifugal pumping AC drive systems. The problem is mainly a pressure surge that occurs in the fluid, due to sudden or forced stops of valves or changes in the direction and momentum of the fluid. Various experimental results based on ML tool and fast Fourier transformation (FFT) analysis are obtained with a Vib Sensor testbed set-up to prove that linear regression analysis is the less time-consuming algorithm for fault detection, irrespective of data size

Contributions à la commande prédictive des systèmes de lois de conservation

La Commande prédictive ou Commande Optimale à Horizon Glissant (COHG) devient de plus en plus populaire dans de nombreuses applications pratiques en raison de ses avantages importants tels que la stabilisation et la prise en compte des contraintes. Elle a été bien étudiée pour des systèmes en dimension finie même dans le cas non linéaire. Cependant, son extension aux systèmes en dimension infinie n'a pas retenu beaucoup d'attention de la part des chercheurs. Ce travail de thèse apporte des contributions à l'application de cette approche aux systèmes de lois de conservation. Nous présentons tout d'abord une preuve de stabilité complète de la COHG pour certaines classes de systèmes en dimension infinie. Ce résultat est ensuite utilisé pour les systèmes hyperboliques 2x2 commandés aux frontières et appliqué à un problème de contrôle de canal d'irrigation. Nous proposons aussi l'extension de cette stratégie au cas de réseaux de systèmes hyperboliques 2x2 en cascade avec une application à un ensemble de canaux d'irrigation connectés. Nous étudions également les avantages de la COHG dans le contexte des systèmes non linéaires et semi-linéaires notamment vis-à-vis des chocs. Toutes les analyses théoriques sont validées par simulation afin d'illustrer l'efficacité de l'approche proposée.The predictive control or Receding Horizon Optimal Control (RHOC) is becoming increasingly popular in many practical applications due to its significant advantages such as the stabilization and constraints handling. It has been well studied for finite dimensional systems even in the nonlinear case. However, its extension to infinite dimensional systems has not received much attention from researchers. This thesis proposes contributions on the application of this approach to systems of conservation laws. We present a complete proof of stability of RHOC for some classes of infinite dimensional systems. This result is then used for 2x2 hyperbolic systems with boundary control, and applied to an irrigation canal. We also propose the extension of this strategy to networks of cascaded 2x2 hyperbolic systems with an application to a set of connected irrigation canals. Furthermore, we study the benefits of RHOC in the context of nonlinear and semi-linear systems in particular with respect to the problem of shocks. All theoretical analyzes are validated by simulation in order to illustrate the effectiveness of the proposed approach.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF

Cooperative Estimation and Control of Large-scale Process Networks

학위논문 (박사)-- 서울대학교 대학원 : 화학생물공학부, 2017. 2. 이종민.본 연구는 대규모 공정 네트워크의 협동 추정 및 제어에 관한 연구이며 기존 대규모 공정의 추정 및 제어 알고리즘의 단점을 보완한 새로운 알고리즘을 제안한다. 또한 대규모 시스템의 한 가지 예로 주로 대규모 상수관망을 대상으로 하여 모델링 및 추정을 통해 이상 진단 및 감지 알고리즘을 개발한다. 상수관망에서 누수, 파열 등의 이상이 발생할 경우 시스템의 크기 및 복잡성으로 인해 이를 감지 및 진단하는 것이 매우 어렵다. 또한 상수관망의 이상으로 인해 발생한 압력 전파 모델이 존재하지 않기 때문에 시스템 모델 없이 효과적으로 이상감지 및 진단하는 기법을 제안하고자 하였다. 기존에 화학공정에서 이상감지를 위해 많이 쓰이는 통계적 기법인 cumulative sum(CUSUM)과 특이점을 빠르고 정확하게 감지할 수 있는 discrete wavelet transform(DWT)을 통합한 새로운 감지 알고리즘을 제안하였고, 이상감지 결과를 이용하여 대규모 상수관망에서 간단한 최적화 해법으로 이상의 위치를 진단하는 알고리즘을 제안하였다. 실제 상수관망의 이상 실험 데이터를 사용하여 제안된 알고리즘을 검증하였고 진단 오차가 30 m 이내로 기존 기술 대비 이상 진단 오차를 현저히 줄임을 확인하였다. 상수관망의 압력 전파 모델이 존재한다면 상태추정(state estimation)을 이용하여 데이터 기반의 알고리즘에 비해 쉽고 빠르게 이상감지 및 진단을 할 수 있기 때문에 상수관망의 이상으로 인한 압력 전파 모델을 개발하고자 하였다. 모델링을 위해 consensus 알고리즘이라는, 네트워크에서의 노드 간의 상태(state)를 나타내는 알고리즘을 이용하였고 consensus 알고리즘을 상수관망에 맞게 수정하여 복잡한 압력 전파 모델을 선형의 간단한 형태로 나타낼 수 있었다. 이를 실제 실험 데이터와 비교하여 제안한 consensus 알고리즘 기반 모델이 실제 압력 전파 다이나믹스를 15% 이내의 오차로 나타낼 수 있음을 보였다. 다음으로 위에서 개발한 모델을 이용하여 대규모 네트워크 시스템에서의 상태추정 기법을 적용한 새로운 이상 감지 및 진단 알고리즘을 제안하였다. 기존의 칼만필터(Kalman filter) 등의 상태추정 방법은 상수관망과 같은 대규모 시스템에 적용될 경우 시스템의 규모가 매우 크기 때문에 계산량 등의 문제가 발생한다. 이를 해결하기 위해 전체 시스템을 여러 개의 서브시스템으로 나눈 decentralized 추정 방법이 연구가 되었다. 그러나 이는 서브시스템 간 상호작용을 고려하지 않기 때문에 이를 보완하기 위해 distributed 추정이 연구되었지만 이 방식은 전체 시스템의 크기가 커짐에 따라서 서브시스템의 estimator의 크기 또한 커지는, 즉 scalability가 없다는 단점이 있다. 본 연구에서 이러한 기존의 방법들을 보완한 새로운 cooperative estimation 알고리즘을 제안하였다. Cooperative state estimation을 상수관망 뿐만 아니라 대규모 화학공정에도 적용하여 decentralized 그리고 distributed 방식의 단점을 보완하면서 centralized estimation과 유사한 성능을 가짐을 보였다. 마지막으로, cooperative estimation 개발에 사용한 핵심 아이디어를 제어 알고리즘에 똑같이 적용하여 cooperative model predictive control(cooperative MPC)을 제안하였다. Cooperative MPC 또한 대규모 공정 네트워크의 제어에 있어 기존의 decentralized 또는 distributed MPC의 단점을 보완하는 새로운 알고리즘으로, 대규모 화학공정에 적용하여 centralized MPC와 유사한 성능을 보임을 증명하였다. 본 박사 논문에서 제시한 대규모 공정의 추정 및 제어를 위한 cooperative KF 그리고 cooperative MPC를 사용하여 기존의 centralized의 계산량 문제, decentralized의 상호작용 문제, 그리고 distributed의 scalability 문제를 해결한 새로운 추정 및 제어가 가능하다.State estimation and control of large-scale process network systems are considered as difficult problems because they consist of numerous subsystems and interactions between subsystems make the entire network dynamics complicated. Chemical processes and pipe networks are representative large-scale networks. In this thesis, we propose a novel cooperative estimation and control algorithms of large-scale process networks. In water pipe networks, a fault such as pipe leak or burst often happens and it is difficult to detect and diagnose. For fault detection and location of water pipe networks, state estimation can be an effective tool. However, a mathematical model describing dynamics of leak in water pipe networks does not exist. Before we develop a mathematical model of water pipe network, we propose a novel methodology to detect and locate leak in water pipe networks. Conventional detection methods include a cumulative sum (CUSUM) and a wavelet transform (WT). However, the CUSUM has a problem of slow response and the WT is sensitive to signal transitions. We integrate two algorithms to effectively detect sudden pressure changes of water pipe networks. The developed leak detection and location system is validated with real field data obtained from artificial leaks by opening hydrant valves in small-scale and medium-scale pipe networks and natural leak occurred in large-scale pipe network. The developed algorithm is model-free approach to detection and location of leak in water pipe networks. We propose consensus algorithm based mathematical model of leak dynamics. Modeling the flow dynamics of leaks in water pipe networks is an extremely difficult problem due to the complex entangled network structure and hydraulic phenomenon. We propose a fundamental model for negative pressure wave dynamics of leaks in water pipe networks based on a consensus algorithm and water hammer theory. The resulting model is a simple and linearly interconnected model in the network even though the dynamics of water pipe networks has a considerable complexity. The model is then validated using experimental data obtained from a real water pipe network. A comparative study demonstrates that the proposed model can describe the real system with high qualitative and quantitative accuracy and that it can be used to develop a model-based leak detection and location algorithm based on the state estimation approach. Using the developed model, we develop a fault detection and location algorithm based on state estimation in water pipe networks. The detection algorithm is based on cooperative H∞-estimation for large-scale interconnected linear systems. To show applicability of the proposed model, we apply distributed and cooperative estimation with H∞-performance to the developed model. The estimation result demonstrates the consensus algorithm based pipe network model can be potentially used for leak detection and location with state estimation method. H∞-based design provides guaranteed performance with respect to model and measurement disturbances. Also, we propose cooperative Kalman filter of large-scale network systems. Basic concepts are based on cooperative H∞-estimation used for detection and location. The proposed cooperative Kalman filter can show fully decentralized or fully distributed state estimation performance depending on parameter selection. It is demonstrated using large-scale chemical process network. We finally propose a cooperative model predictive control of large-scale process networks based on the same concepts and ideas used to develop cooperative state estimation. Important properties of stability, optimality, local controllability, and scalability are also proved. When the developed cooperative MPC is applied to chemical process network composed of three process units, it shows performance between decentralized and distributed manners. We also show that the proposed cooperative MPC is the same with centralized MPC under certain condition.1. Introduction 1 1.1 Background and Motivation 1 1.2 Preliminaries 2 1.2.1 Network topology 3 1.2.2 Consensus algorithm 4 1.2.3 State Estimation for large-scale networks 6 1.2.4 Control for large-scale networks 9 1.3 Contribution 17 1.4 Outline 18 2. Model-free Approach to Fault Detection and Location of Water Pipe Networks 20 2.1 Introduction 20 2.2 Detection Algorithm 24 2.2.1 Noise filtering of raw pressure data 24 2.2.2 Cumulative sum for global detection 25 2.2.3 Discrete wavelet transform for local time correction 26 2.3 Location Algorithm 29 2.3.1 Negative pressure wave 29 2.3.2 Node matrix 31 2.3.3 Objective function 32 2.4 Integrated System 33 2.5 Experiments and Validations 34 2.5.1 Small-scale pipe network with artificial faults 34 2.5.2 Medium-scale pipe network with artificial faults 43 2.5.3 Large-scale pipe network with natural faults 46 2.6 Limitations for applicability to complex networks 52 2.7 Conclusions 53 3. Consensus Algorithm for Process Networks 54 3.1 Introduction 54 3.2 Consensus Algorithm based Process Network Model 59 3.2.1 Consensus in networks 59 3.3 Application to Water Pipe Networks 60 3.3.1 Flow dynamics based on consensus algorithm 61 3.3.2 Water hammer theory 62 3.3.3 Dynamics at leak point 64 3.3.4 Complete model 65 3.3.5 Experiment 66 3.3.6 Validation 69 3.4 Conclusions 74 4. Cooperative State Estimation of Large-scale Process Networks 77 4.1 Introduction 77 4.2 System Model and Repartition 79 4.2.1 System model 79 4.2.2 Repartition of system model 82 4.3 Cooperative State Estimation Based on Kalman Filter 84 4.3.1 Standard Kalman filter 84 4.3.2 Cooperative Kalman filter 87 4.4 Application I: Water Pipe Networks for Fault Detection and Location 98 4.5 Application II: Chemical Process Networks with Recycles 104 4.5.1 Network model 104 4.5.2 Simulation results 109 4.6 Conclusions 109 5. Cooperative Model Predictive Control of Large-scale Process Networks 112 5.1 Introduction 113 5.2 System Model and Repartition 115 5.3 Cooperative Model Predictive Control 116 5.3.1 Centralized MPC 116 5.3.2 Cooperative MPC 120 5.4 Application to Chemical Process Networks with Recycles 121 5.5 Conclusions 121 6. Concluding Remarks 123 6.1 Concluding Remarks 123 6.2 Future Directions 126 Bibliography 127 초록 138Docto

40 Years Theory and Model at Wageningen UR

"Theorie en model" zo luidde de titel van de inaugurele rede van CT de Wit (1968). Reden genoeg voor een (theoretische) terugblik op zijn wer

Automation and Control Architecture for Hybrid Pipeline Robots

The aim of this research project, towards the automation of the Hybrid Pipeline Robot (HPR), is the development of a control architecture and strategy, based on reconfiguration of the control strategy for speed-controlled pipeline operations and self-recovering action, while performing energy and time management. The HPR is a turbine powered pipeline device where the flow energy is converted to mechanical energy for traction of the crawler vehicle. Thus, the device is flow dependent, compromising the autonomy, and the range of tasks it can perform. The control strategy proposes pipeline operations supervised by a speed control, while optimizing the energy, solved as a multi-objective optimization problem. The states of robot cruising and self recovering, are controlled by solving a neuro-dynamic programming algorithm for energy and time optimization, The robust operation of the robot includes a self-recovering state either after completion of the mission, or as a result of failures leading to the loss of the robot inside the pipeline, and to guaranteeing the HPR autonomy and operations even under adverse pipeline conditions Two of the proposed models, system identification and tracking system, based on Artificial Neural Networks, have been simulated with trial data. Despite the satisfactory results, it is necessary to measure a full set of robot’s parameters for simulating the complete control strategy. To solve the problem, an instrumentation system, consisting on a set of probes and a signal conditioning board, was designed and developed, customized for the HPR’s mechanical and environmental constraints. As a result, the contribution of this research project to the Hybrid Pipeline Robot is to add the capabilities of energy management, for improving the vehicle autonomy, increasing the distances the device can travel inside the pipelines; the speed control for broadening the range of operations; and the self-recovery capability for improving the reliability of the device in pipeline operations, lowering the risk of potential loss of the robot inside the pipeline, causing the degradation of pipeline performance. All that means the pipeline robot can target new market sectors that before were prohibitive