Automated synthesis of control configurations for process networks based on structural coupling

Abstract In this paper, a method to systematically synthesize control configurations with favorable structural coupling is developed, using relative degree as a measure of such coupling. Initially, an integer optimization problem is formulated to identify optimal distributions of inputs and outputs (decentralized control configurations) that minimize the overall structural coupling in the network. Then, a hierarchical clustering procedure, which allows identifying groups of inputs and outputs that are strongly connected topologically (block decentralized control configurations), is proposed. The application of the method is illustrated through an example process network.

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

[2]  Panagiotis D. Christofides,et al.  Distributed model predictive control of nonlinear process systems , 2009 .

[3]  Michael Baldea,et al.  Dynamics and Control of Process Networks with Large Energy Recycle , 2009 .

[4]  Marc M. J. van de Wal,et al.  A review of methods for input/output selection , 2001, Autom..

[5]  B. Erik Ydstie,et al.  Passivity based control via the second law , 2002 .

[6]  Katalin M. Hangos,et al.  Decentralised controller structure design and retrofit of process systems based on graph theory , 2011, Int. J. Syst. Sci..

[7]  Ronald L. Rivest,et al.  Introduction to Algorithms , 1990 .

[8]  Michael Baldea,et al.  Dynamics and control of integrated networks with purge streams , 2006 .

[9]  Sigurd Skogestad.  Plantwide control: the search for the self-optimizing control structure , 2000 .

[10]  Riccardo Scattolini,et al.  Architectures for distributed and hierarchical Model Predictive Control - A review , 2009 .

[11]  George Stephanopoulos,et al.  Synthesis of control systems for chemical plants , 1996 .

[12]  Gade Pandu Rangaiah,et al.  Plantwide control : recent developments and applications , 2012 .

[13]  J. Fraser Forbes,et al.  Block Relative Gain: Properties and Pairing Rules , 2003 .

[14]  William L. Luyben,et al.  Plantwide control design procedure , 1997 .

[15]  Sigurd Skogestad,et al.  Self-optimizing control: the missing link between steady-state optimization and control , 2000 .

[16]  C. Georgakis,et al.  Plantwide regulatory control design procedure using a tiered framework , 1993 .

[17]  Prodromos Daoutidis,et al.  Structural evaluation of control configurations for multivariable nonlinear processes , 1992 .

[18]  Panagiotis D. Christofides,et al.  Fault‐tolerant control of nonlinear process systems subject to sensor faults , 2007 .

[19]  James B. Rawlings,et al.  Coordinating multiple optimization-based controllers: New opportunities and challenges , 2008 .

[20]  P. Daoutidis,et al.  Nonlinear Dynamics and Control of Process Systems with Recycle , 2000 .

[21]  Bin Xu,et al.  Process flow diagram of an ammonia plant as a complex network , 2005 .

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

[23]  Costas Kravaris,et al.  Plantwide control structure selection methodology for the benchmark vinyl acetate monomer plant , 2014, Comput. Chem. Eng..

[24]  J. D. Perkins,et al.  Process control structure selection based on economics , 2000 .

[25]  Mark Newman,et al.  Detecting community structure in networks , 2004 .

[26]  Panagiotis D. Christofides,et al.  Selection of control configurations for economic model predictive control systems , 2014 .

[27]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[28]  Kenneth Kunen,et al.  Handbook of Set-Theoretic Topology , 1988 .

[29]  I. Mezic,et al.  Graph decomposition methods for uncertainty propagation in complex, nonlinear interconnected dynamical systems , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[30]  Vasilios Manousiouthakis,et al.  Synthesis of decentralized process control structures using the concept of block relative gain , 1986 .

[31]  Michael Baldea,et al.  Dynamic considerations in the synthesis of self-optimizing control structures , 2008 .

[32]  Nael H. El-Farra,et al.  Quasi-decentralized model-based networked control of process systems , 2008, Comput. Chem. Eng..

[33]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[34]  Prodromos Daoutidis,et al.  Design and control of energy integrated SOFC systems for in situ hydrogen production and power generation , 2011, Comput. Chem. Eng..

[35]  Jie Bao,et al.  Dissipativity-based decentralized control of interconnected nonlinear chemical processes , 2012, Comput. Chem. Eng..

[36]  Panagiotis D. Christofides,et al.  Smart plant operations: Vision, progress and challenges , 2007 .

[37]  Jie Bao,et al.  Distributed model predictive control based on dissipativity , 2013 .