Plantwide control structure selection methodology for the benchmark vinyl acetate monomer plant

Abstract In this paper, the regulatory control structure selection (RCSS) methodology of Psaltis, Kookos, and Kravaris (2013, Computers & Chemical Engineering, 52, 240–248) is applied on the benchmark case study of the vinyl acetate (VAc) monomer plant. The VAc monomer plant consists of 10 unit operations with high level of interactions. The mathematical model of the process involves 246 state variables, 26 potential manipulated variables and 46 measurements. It is the first time that the proposed RCSS methodology is applied to a plantwide case study and this was made possible due to the improvements in the classical “back-off” methodology proposed by Psaltis et al. (2013). The control structure obtained for VAc plant is implemented on the nonlinear plant model using decentralized PI controllers and the simulation results illustrate its satisfactory performance.

[1]  William L. Luyben,et al.  Plantwide Process Control , 1998 .

[2]  I. Kookos Real-time regulatory control structure selection based on economics , 2005 .

[3]  William L. Luyben,et al.  Design and Control of a Modified Vinyl Acetate Monomer Process , 2011 .

[4]  Charles F. Moore Selection of Controlled and Manipulated Variables , 1992 .

[5]  Luis A. Ricardez-Sandoval,et al.  Simultaneous Design and Control: A New Approach and Comparisons with Existing Methodologies , 2010 .

[6]  J. Perkins,et al.  A systematic method for optimum sensor selection in inferential control systems , 1999 .

[7]  B. Young,et al.  PLANTWIDE CONTROL STUDY OF A VINYL ACETATE MONOMER PROCESS DESIGN , 2005 .

[8]  C. Floudas,et al.  Structural analysis and synthesis of feasible control systems ― theory and applications , 1989 .

[9]  Luis A. Ricardez-Sandoval,et al.  Simultaneous process synthesis and control design under uncertainty: A worst-case performance approach , 2013 .

[10]  T. McAvoy,et al.  Plantwide Control System Design: Methodology and Application to a Vinyl Acetate Process , 2003 .

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

[12]  Costas Kravaris,et al.  Plant-wide control structure selection methodology based on economics , 2013, Comput. Chem. Eng..

[13]  Thomas J. McAvoy,et al.  A Nonlinear Dynamic Model of a Vinyl Acetate Process , 2003 .

[14]  George Stephanopoulos,et al.  Synthesis of control systems for chemical plants A challenge for creativity , 1983 .

[15]  Sigurd Skogestad,et al.  Optimal measurement combinations as controlled variables , 2009 .

[16]  Michael Baldea,et al.  Control of integrated process networks - A multi-time scale perspective , 2007, Comput. Chem. Eng..

[17]  J. D. Perkins,et al.  Selection of process control structure based on linear dynamic economics , 1993 .

[18]  Costin Sorin Bildea,et al.  Effect of catalytic reactor design on plantwide control strategy: Application to VAM plant , 2008 .

[19]  Efstratios N. Pistikopoulos,et al.  Optimal design of dynamic systems under uncertainty , 1996 .

[20]  Hiroya Seki,et al.  Plantwide control system design of the benchmark vinyl acetate monomer production plant , 2010, Comput. Chem. Eng..

[21]  D. A. R. Zumoffen,et al.  Decentralized plantwide control strategy for large-scale processes. Case study: Pulp mill benchmark problem , 2013, Comput. Chem. Eng..

[22]  Peter L. Lee,et al.  On dissipativity, passivity and dynamic operability of nonlinear processes , 2008 .

[23]  Richard E. Rosenthal,et al.  GAMS -- A User's Guide , 2004 .

[24]  M. Luyben,et al.  An industrial design/control study for the vinyl acetate monomer process , 1998 .