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Wenjian Cai - One of the best experts on this subject based on the ideXlab platform.

  • multivariable process control decentralized decoupling or sparse
    Industrial & Engineering Chemistry Research, 2010
    Co-Authors: Yuling Shen, Wenjian Cai
    Abstract:

    In this article, a systematic approach is proposed to design PI-/PID-based multivariable control systems in which the designs and analyses of decentralized, decoupling, and sparse control schemes are all treated under a unified framework. First, based on the Relative normalized Gain Array (RNGA), a best loop pairing is obtained using the RGA (Relative Gain Array)−Nederlinski index (NI)−RNGA rules. The index matrix is then calculated, and the control structure is determined according to a selection criterion. Finally, the selected loop controllers are independently designed based on equivalent transfer functions. The effectiveness of the proposed design approach is verified by analysis of several multivariable industrial processes, demonstrating that the selected control structure results in better overall system performance.

  • decentralized control system design for multivariable processesa novel method based on effective Relative Gain Array
    Industrial & Engineering Chemistry Research, 2006
    Co-Authors: Qiang Xiong, Wenjian Cai
    Abstract:

    In this paper, a novel method for design of a decentralized control system for multivariable processes is proposed. On the basis of a new interaction measure, effective Relative Gain Array (ERGA), in terms of energy transmission ratio, loop interactions are quantified by two elements, i.e., Relative Gain and Relative critical frequency. The interaction effects for a particular loop from all other closed loops are analyzed through both steady-state Gain and critical frequency variations. Consequently, appropriate detuning factors for decentralized controllers under different interaction conditions can be derived based on the effective Relative Gain, Relative Gain, and Relative critical frequency. The design method can be effectively used for both normal processes as well as process-loop transfer functions containing unstable zeros resulted from other closed loops. This design method is simple, straightforward, and effective and can be easily understood and implemented by field engineers. Several multivaria...

Luis A Ricardezsandoval - One of the best experts on this subject based on the ideXlab platform.

  • controllability and flexibility analysis of co2 post combustion capture using piperazine and mea
    International Journal of Greenhouse Gas Control, 2016
    Co-Authors: Jozsef Gaspar, Luis A Ricardezsandoval, John Bagterp Jorgensen, Philip Loldrup Fosbol
    Abstract:

    In this study, we developed a decentralized control scheme and investigate the performance of the piperazine (PZ) and monoethanolamine (MEA) CO2 capture process for industrially-relevant operation scenarios. The base for the design of the control schemes is Relative Gain Array (RGA) analysis combined with open-loop dynamic sensitivity analysis. This study suggests that controllers with smaller time integrals and larger Gains are required to maintain the PZ plant within reasonable short closed-loop settling times when compared to MEA. It also shows that the offset from the designated set-points in the presence of disturbances in the flue gas flow and heat duty is larger using PZ compared to MEA. The settling time for the PZ plant is generally larger than for MEA. However, the PZ plant rejects the disturbances faster and with less variability in the load of the power plant. Furthermore, this study indicates that the proposed PI-based control structure can handle large changes in the load provided that the manipulated variables, i.e. lean solvent flow or reboiler duty, do not reach their saturation limit. Additionally, we observed that shortage in the steam supply (reboiler duty) may represent a critical operational bottleneck, especially when PZ is being used. The MEA plant controllers drive the system towards drying out/flooding while the CO2 capture rate performance of the PZ plant reduces drastically in the presence of constraints in the availability of steam. These findings suggest the need for advanced control structures, e.g. MPC, which can explicitly account for constraints in the process variables.

  • dynamic modelling and control of mea absorption processes for co2 capture from power plants
    Fuel, 2014
    Co-Authors: Thanita Nittaya, Eric Croiset, Peter L Douglas, Luis A Ricardezsandoval
    Abstract:

    Abstract This paper presents a mechanistic dynamic model of a post-combustion CO 2 capture plant using the MEA absorption process. Insight regarding the process dynamics due to various changes in the plant’s operating conditions is presented and three decentralized control structures are proposed. The first control scheme was designed using the RGA (Relative Gain Array) analysis whereas the other two control schemes were designed based on heuristics. The performance of the proposed control schemes was evaluated under different scenarios, e.g. changes in flue gas flow rates, set point tracking, and valve stiction. In this study, a control scheme obtained from the heuristic approach resulted in shorter closed-loop settling times than the RGA-based control scheme.

  • a decentralized control structure for a co2 compression capture and purification process an uncertain Relative Gain Array approach
    IFAC Proceedings Volumes, 2011
    Co-Authors: Atchariya Chansomwong, Kourosh Zanganeh, Ahmed Shafeen, P L Douglas, Eric Croiset, Luis A Ricardezsandoval
    Abstract:

    Abstract This paper presents a study on decentralized control structures that can be proposed to control a CO 2 compression, capture, and purification process for fossil fuel power plants based on oxyfuel combustion. A dynamic model that describes the transient behavior of this process is currently not available. Thus, the present work applied the Relative Gain Array (RGA) analysis to identify the most promising control strategies for this process. The process Gains were estimated using a steady-state process model developed in Aspen Plus and validated with data obtained from the CanmetENERGY. The RGA analysis performed for the base case operation of this process was compared to an uncertain RGA analysis that takes into account the uncertainty on the process Gains for control structure selection. The result obtained with the uncertain RGA demonstrates that the controller pairings suggested by nominal RGA can lead to control configurations with loops that may become unstable.

Ali Abbas - One of the best experts on this subject based on the ideXlab platform.

  • dynamic modelling identification and preliminary control analysis of an amine based post combustion co2 capture pilot plant
    Journal of Cleaner Production, 2016
    Co-Authors: Norhuda Abdul Manaf, Ashleigh Cousins, Paul Feron, Ali Abbas
    Abstract:

    Abstract Solvent-based post combustion CO 2 capture (PCC) is considered as a mature technology for dealing with CO 2 emissions from fossil-fired power plants. In this study, a mathematical black box model is developed to analyse the dynamic responses of a PCC pilot plant. The model identification reported the dynamics of variables of the key units in the plant, the absorber, rich/lean heat exchanger and desorber. Pilot plant dynamic data were used to develop a data-driven model for each unit operation. Individual models were integrated to produce a simplified 4 × 3 PCC process model of the PCC plant. The fastest dynamic with a time constant ranging from 2 to 3 min featured in the relationship between power plant flue gas flow rate and CO 2 concentration in the absorber off gas. Whereas, the slowest response with a process time constant between 9 and 27 min occurred in CO 2 concentration at the top of the stripper due to changes in reboiler heat duty. Preliminary control analysis using Relative Gain Array (RGA) analysis suggested that carbon capture efficiency, CC (%), and energy performance, EP (MJ per kg of CO 2 captured), can be controlled by manipulating the lean solvent flow rate and reboiler heat duty, respectively. The proposed control structure was tested and tracked CC and EP random set point step changes in the range between 7–25 h and 4–5 h respectively. This study contributes to understanding transient variable behaviours in PCC plants; concurrent with current industrial requirements for controllability and flexible operation of PCC plants, in response to the dynamics of power plant load, electricity and carbon prices.

Mohammed Chadli - One of the best experts on this subject based on the ideXlab platform.

  • book reviews multivariable control systems an engineering approach
    Automatica, 2005
    Co-Authors: Pedro Albertos, Antonio Sala, Mohammed Chadli
    Abstract:

    1 Introduction to Multivariable Control 1.1 Introduction 1.2 Process and Instrumentation 1.3 Process Variables 1.4 The Process Behaviour 1.5 Control Aims 1.6 Modes of Operation 1.7 The Need for Feedback 1.8 Model-free vs. Model-based Control 1.9 The Importance of Considering Modelling Errors 1.10 Multivariable Systems 1.11 Implementation and Structural Issues 1.12 Summary of the Chapters 2 Linear System Representation: Models and Equivalence 2.1 Introduction: Objectives of Modelling 2.2 Types of Models. 2.3 First-principle Models: Components 2.4 Internal Representation: State Variables 2.5 Linear Models and Linearisation 2.6 Input/Output Representations 2.6.1 Polynomial Representation 2.6.2 Transfer Matrix 2.7 Systems and Subsystems: Interconnection 2.7.1 Series, Parallel and Feedback Connection 2.7.2 Generalised Interconnection 2.8 Discretised Models. 2.9 Equivalence of Representations 2.10 Disturbance Models 2.10.1 Deterministic Signals 2.10.2 Randomness in the Signals 2.10.3 Discrete Stochastic Processes 2.11 Key Issues in Modelling 2.12 Case Study: The Paper Machine Headbox 2.12.1 Simpli.ed Models 2.12.2 Elaborated Models 3 Linear Systems Analysis 3.1 Introduction 3.2 Linear System Time-response 3.3 Stability Conditions 3.3.1 Relative Degree of Stability 3.4 Discretisation 3.5 Gain 3.5.1 Static Gain 3.5.2 Instantaneous Gain 3.5.3 Directional Gain 3.6 Frequency response 3.7 System Internal Structure 3.7.1 Reachability (State Controllability) 3.7.2 Observability 3.7.3 Output Reachability 3.7.4 Remarks on Reachability and Observability 3.7.5 Canonical Forms 3.8 Block System Structure (Kalman Form) 3.8.1 Minimal Realisation 3.8.2 Balanced Realisation. 3.8.3 Poles and Zeros 3.9 Input/Output Properties 3.9.1 Input/Output Controllability 3.10 Model Reduction 3.10.1 Time Scale Decomposition 3.10.2 Balanced Reduction 3.11 Key Issues in MIMO Systems Analysis 3.12 Case Study: Simple Distillation Column 4 Solutions to the Control Problem 4.1 The Control Design Problem 4.2 Control Goals 4.3 Variables Selection 4.4 Control Structures 4.5 Feedback Control 4.5.1 Closed-loop Stability Analysis 4.5.2 Interactions 4.5.3 Generalised Plant 4.5.4 Performance Analysis Contents xv 4.6 Feedforward Control 4.6.1 Manual Control 4.6.2 Open-loop Inversion and Trajectory Tracking 4.6.3 Feedforward Rejection of Measurable Disturbances 4.7 Two Degree of Freedom Controller 4.8 Hierarchical Control 4.9 Key Issues in Control Design. 4.10 Case Study: Ceramic Kiln 5 Decentralised and Decoupled Control 5.1 Introduction 5.1.1 Plant Decomposition, Grouping of Variables 5.2 Multi-loop Control, Pairing Selection 5.2.1 The Relative Gain Array Methodology 5.2.2 Integrity (Fault Tolerance) 5.2.3 Diagonal Dominance (Stability Analysis) 5.3 Decoupling 5.3.1 Feedforward Decoupling 5.3.2 Feedback Decoupling 5.3.3 SVD Decoupling 5.4 Enhancing SISO Loops with MIMO Techniques: Cascade Control 5.4.1 Case I: Extra Measurements 5.4.2 Case II: Extra Actuators 5.5 Other Possibilities 5.5.1 Indirect and Inferential Control 5.5.2 Override, Selectors 5.5.3 Split-range Control 5.5.4 Gradual Control, Local Feedback 5.6 Sequential-Hierarchical Design and Tuning 5.6.1 Combined Strategies for Complex Plants 5.7 Key Conclusions 5.8 Case Studies 5.8.1 Steam Boiler 5.8.2 Mixing Process 6 Fundamentals of Centralised Closed-loop Control 6.1 State Feedback 6.1.1 Stabilisation and Pole-placement 6.1.2 State Feedback PI Control 6.2 Output Feedback 6.2.1 Model-based Recurrent Observer 6.2.2 Current Observer 6.2.3 Reduced-order Observer 6.2.4 Separation Principle 6.3 Rejection of Deterministic Unmeasurable Disturbances 6.3.1 Augmented Plan

  • multivariable control systems an engineering approach
    2003
    Co-Authors: Pedro Albertos, Antonio Sala, Mohammed Chadli
    Abstract:

    1 Introduction to Multivariable Control 1.1 Introduction 1.2 Process and Instrumentation 1.3 Process Variables 1.4 The Process Behaviour 1.5 Control Aims 1.6 Modes of Operation 1.7 The Need for Feedback 1.8 Model-free vs. Model-based Control 1.9 The Importance of Considering Modelling Errors 1.10 Multivariable Systems 1.11 Implementation and Structural Issues 1.12 Summary of the Chapters 2 Linear System Representation: Models and Equivalence 2.1 Introduction: Objectives of Modelling 2.2 Types of Models. 2.3 First-principle Models: Components 2.4 Internal Representation: State Variables 2.5 Linear Models and Linearisation 2.6 Input/Output Representations 2.6.1 Polynomial Representation 2.6.2 Transfer Matrix 2.7 Systems and Subsystems: Interconnection 2.7.1 Series, Parallel and Feedback Connection 2.7.2 Generalised Interconnection 2.8 Discretised Models. 2.9 Equivalence of Representations 2.10 Disturbance Models 2.10.1 Deterministic Signals 2.10.2 Randomness in the Signals 2.10.3 Discrete Stochastic Processes 2.11 Key Issues in Modelling 2.12 Case Study: The Paper Machine Headbox 2.12.1 Simpli.ed Models 2.12.2 Elaborated Models 3 Linear Systems Analysis 3.1 Introduction 3.2 Linear System Time-response 3.3 Stability Conditions 3.3.1 Relative Degree of Stability 3.4 Discretisation 3.5 Gain 3.5.1 Static Gain 3.5.2 Instantaneous Gain 3.5.3 Directional Gain 3.6 Frequency response 3.7 System Internal Structure 3.7.1 Reachability (State Controllability) 3.7.2 Observability 3.7.3 Output Reachability 3.7.4 Remarks on Reachability and Observability 3.7.5 Canonical Forms 3.8 Block System Structure (Kalman Form) 3.8.1 Minimal Realisation 3.8.2 Balanced Realisation. 3.8.3 Poles and Zeros 3.9 Input/Output Properties 3.9.1 Input/Output Controllability 3.10 Model Reduction 3.10.1 Time Scale Decomposition 3.10.2 Balanced Reduction 3.11 Key Issues in MIMO Systems Analysis 3.12 Case Study: Simple Distillation Column 4 Solutions to the Control Problem 4.1 The Control Design Problem 4.2 Control Goals 4.3 Variables Selection 4.4 Control Structures 4.5 Feedback Control 4.5.1 Closed-loop Stability Analysis 4.5.2 Interactions 4.5.3 Generalised Plant 4.5.4 Performance Analysis Contents xv 4.6 Feedforward Control 4.6.1 Manual Control 4.6.2 Open-loop Inversion and Trajectory Tracking 4.6.3 Feedforward Rejection of Measurable Disturbances 4.7 Two Degree of Freedom Controller 4.8 Hierarchical Control 4.9 Key Issues in Control Design. 4.10 Case Study: Ceramic Kiln 5 Decentralised and Decoupled Control 5.1 Introduction 5.1.1 Plant Decomposition, Grouping of Variables 5.2 Multi-loop Control, Pairing Selection 5.2.1 The Relative Gain Array Methodology 5.2.2 Integrity (Fault Tolerance) 5.2.3 Diagonal Dominance (Stability Analysis) 5.3 Decoupling 5.3.1 Feedforward Decoupling 5.3.2 Feedback Decoupling 5.3.3 SVD Decoupling 5.4 Enhancing SISO Loops with MIMO Techniques: Cascade Control 5.4.1 Case I: Extra Measurements 5.4.2 Case II: Extra Actuators 5.5 Other Possibilities 5.5.1 Indirect and Inferential Control 5.5.2 Override, Selectors 5.5.3 Split-range Control 5.5.4 Gradual Control, Local Feedback 5.6 Sequential-Hierarchical Design and Tuning 5.6.1 Combined Strategies for Complex Plants 5.7 Key Conclusions 5.8 Case Studies 5.8.1 Steam Boiler 5.8.2 Mixing Process 6 Fundamentals of Centralised Closed-loop Control 6.1 State Feedback 6.1.1 Stabilisation and Pole-placement 6.1.2 State Feedback PI Control 6.2 Output Feedback 6.2.1 Model-based Recurrent Observer 6.2.2 Current Observer 6.2.3 Reduced-order Observer 6.2.4 Separation Principle 6.3 Rejection of Deterministic Unmeasurable Disturbances 6.3.1 Augmented Plan

Brandon Hencey - One of the best experts on this subject based on the ideXlab platform.

  • decentralized feedback structures of a vapor compression cycle system
    IEEE Transactions on Control Systems and Technology, 2010
    Co-Authors: Neera Jain, Bin Li, M Keir, Brandon Hencey
    Abstract:

    In vapor compression cycle systems, it is desirable to effectively control the thermodynamic cycle by controlling the thermodynamic states of the refrigerant. By controlling the thermodynamic states with an inner loop, supervisory algorithms can manage critical functions and objectives such as maintaining superheat and maximizing the coefficient of performance. In practice, it is generally preferred to tune multiple single-input-single-output (SISO) control inner loops rather than a single multiple-input-multiple-output control inner loop. This paper presents a process by which a simplified feedback control structure, amenable to a decoupled SISO control loop design, may be identified. In particular, the many possible candidate input-output (I/O) pairs for decentralized control are sorted via a decoupling metric, called the Relative Gain Array number. From a reduced set of promising candidate I/O pairs, engineering insight is applied to arrive at the most effective pairings successfully verified on an experimental air-conditioning-and-refrigeration test stand.