Improvements on multiloop control design via net load evaluation

Abstract The plant-wide control problem is a very important topic in process control. A particular control structure design will define (restrict) the future operability degree for the plant under study. Classical control policies (decentralized or full) are not always the best solution. In this context a systematic and generalized strategy to solve the multivariable plant-wide control problem is proposed here. The methodology called minimum square deviation (MSD) considers several points such as the optimal controlled variables (CVs) selection based on the sum of square deviation (SSD) and controller structure design supported by net load evaluation (NLE) analysis. The overall problem is combinatorial and is solved by accounting several steady-state tools and new indexes minimizing the heuristic load. Four well-known case studies are presented and other approaches taken from the literature are accounted for the sake of comparison. A robust stability test, μ -tools, is also performed for concluding about the control policies.

[1]  Lihua Xie,et al.  RNGA based control system configuration for multivariable processes , 2009 .

[2]  Cheng-Ching Yu,et al.  The relative gain for non-square multivariable systems , 1990 .

[3]  Ian Postlethwaite,et al.  Multivariable Feedback Control: Analysis and Design , 1996 .

[4]  William L. Luyben,et al.  Improved multiloop single-input/single-output (SISO) controllers for multivariable processes , 1988 .

[5]  M. Morari,et al.  Closed-loop properties from steady-state gain information , 1985 .

[6]  Sigurd Skogestad,et al.  Control structure selection for an evaporation process , 2001 .

[7]  Francis J. Doyle,et al.  A pulp mill benchmark problem for control: problem description , 2004 .

[8]  Sigurd Skogestad,et al.  Selection of Controlled Variables: Maximum Gain Rule and Combination of Measurements , 2008 .

[9]  Gene H. Golub,et al.  Matrix computations (3rd ed.) , 1996 .

[10]  Wenjian Cai,et al.  Multivariable Process Control: Decentralized, Decoupling, or Sparse? , 2010 .

[11]  T. McAvoy,et al.  Optimal selection of measurements and manipulated variables for production control , 2010 .

[12]  Sigurd Skogestad,et al.  Perfect Steady-State Indirect Control , 2005 .

[13]  David Zumoffen,et al.  Systematic Control Approach for the Petlyuk Distillation Column , 2011 .

[14]  Hsiao-Ping Huang,et al.  Multivariable Control with Generalized Decoupling for Disturbance Rejection , 2009 .

[15]  Jan M. Maciejowski,et al.  Predictive control : with constraints , 2002 .

[16]  Gene H. Golub,et al.  Matrix computations , 1983 .

[17]  M. Morari,et al.  Implications of large RGA elements on control performance , 1987 .

[18]  Alex Simpkins,et al.  System Identification: Theory for the User, 2nd Edition (Ljung, L.; 1999) [On the Shelf] , 2012, IEEE Robotics & Automation Magazine.

[19]  Jinsong Zhao,et al.  Effect of Manipulated Variables Selection on the Controllability of Chemical Processes , 2011 .

[20]  Carlos E. Garcia,et al.  Internal model control. 2. Design procedure for multivariable systems , 1985 .

[21]  David Zumoffen,et al.  Optimal Sensor Location for Chemical Process Accounting the Best Control Configuration , 2009 .

[22]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[23]  E. Bristol.  On a new measure of interaction for multivariable process control , 1966 .

[24]  David Zumoffen,et al.  A Systematic Approach for the Design of Optimal Monitoring Systems for Large Scale Processes , 2010 .

[25]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[26]  A. Khaki-Sedigh,et al.  Control configuration selection for multivariable plants , 2009 .

[27]  David Zumoffen,et al.  Plant-wide control strategy applied to the Tennessee Eastman process at two operating points , 2011, Comput. Chem. Eng..

[28]  William L. Luyben,et al.  Simple method for tuning SISO controllers in multivariable systems , 1986 .

[29]  Yi Cao,et al.  Branch and bound method for multiobjective pairing selection , 2010, Autom..

[30]  Cheng-Ching Yu,et al.  Relative disturbance gain array , 1992 .

[31]  P. S. Buckley,et al.  Techniques of Process Control , 1965 .

[32]  Y. Arkun,et al.  A new approach to defining a dynamic relative gain , 2001 .

[33]  Cheng-Ching Yu,et al.  Synthesis of controller structures for robust load performance , 1994 .

[34]  Theodore J. Williams.  Techniques of process control, Page S. Buckley, Wiley, New York (1964), 303 pages, $ 15.00 , 1965 .

[35]  Ernst Dieter Gilles,et al.  Plant-wide process control for quality assurance , 1991 .

[36]  Yi Cao,et al.  Bidirectional Branch and Bound for Controlled Variable Selection Part III: Local Average Loss Minimization , 2010, IEEE Transactions on Industrial Informatics.

[37]  David Zumoffen,et al.  A New Systematic Approach to Fi nd Plantwide Control Structures , 2009 .