Optimal tuning of thermodynamic-based decentralized PI control loops: Application to the Tennessee Eastman Process

The need of designing decentralized control loops emerges to ensure the global stability of a given process plant. To that purpose, it has been proposed in recent works a systematic approach to derive robust decentralized controllers, which is based on the link between thermodynamics and passivity t...

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Veröffentlicht in:	AIChE journal Jg. 54; H. 11; S. 2904 - 2924
Hauptverfasser:	Antelo, Luis T, Exler, Oliver, Banga, Julio R, Alonso, Antonio A
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.11.2008 Wiley Subscription Services American Institute of Chemical Engineers
Schlagworte:	Applications of mathematics to chemical engineering. Modeling. Simulation. Optimization Applied sciences Chemical engineering Closed loop systems controller parameters Controllers decentralized control Design Exact sciences and technology Nonlinear programming nonlinear programming (NLP) PI controller Problem solving Tennessee Eastman Process Thermodynamics tuning United States > US Tennessee USA, Tennessee Closed loop Integral proportional control Stability nonlinear programming (NLP) tuning Benchmarks Non linear programming controller parameters PI controller Optimization Design Tennessee Eastman Process Decentralized control Cost function Inventory control Instability Mathematical programming
ISSN:	0001-1541, 1547-5905
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Abstract	The need of designing decentralized control loops emerges to ensure the global stability of a given process plant. To that purpose, it has been proposed in recent works a systematic approach to derive robust decentralized controllers, which is based on the link between thermodynamics and passivity theory, as well as on the fundamentals of process networks. This thermodynamic-based control (TBC) methodology has several steps: (1) Decomposition of the considered process system into abstract mass and energy inventory networks; (2) design of conceptual mass and energy inventory control loops to guarantee the convergence of the states of the plant to a compact convex region defined by constant inventories, where input-output stability follows; (3) definition of intensive variable control loops (if needed) to achieve global stability, and (4) realization of the conceptual inventory and intensive variable control loops over the available degrees of freedom in the system by using, for instance, PI controllers. A tool to tune these PI control loops is developed, based on the solution of a nonlinear programming optimization problem (NLP), in order to complete the proposed hierarchical and systematic TBC design. The aim is to minimize a given cost function, subject to both the system dynamics, as well as the linear and nonlinear constraints (no disturbances affecting the system are considered), where the vector of decision variables will be formed by the parameters of the PI controllers used in the defined decentralized control loops. We will test this tuning procedure in several control designs developed for the challenging benchmark of the Tennessee Eastman Process (TEP) by Ricker and Larsson et al., as well as in two TBC candidates, concluding that the best candidate among the proposed ones (in terms of final cost function) will be one of these TBC designs. For solving the NLP problem, two local (FMINCON and NOMADm) solvers, and a new global (MITS) one are used, comparing their performances. Finally, the dynamic analysis of the optimal tuned closed loop systems is carried out, finding that the presented TBC control candidates will be stable, while the other control structures considered exhibit complex dynamic behaviors or even instability when disturbances affecting the process are considered. © 2008 American Institute of Chemical Engineers AIChE J, 2008
AbstractList	The need of designing decentralized control loops emerges to ensure the global stability of a given process plant. To that purpose, it has been proposed in recent works a systematic approach to derive robust decentralized controllers, which is based on the link between thermodynamics and passivity theory, as well as on the fundamentals of process networks. This thermodynamic-based control (TBC) methodology has several steps: (1) Decomposition of the considered process system into abstract mass and energy inventory networks; (2) design of conceptual mass and energy inventory control loops to guarantee the convergence of the states of the plant to a compact convex region defined by constant inventories, where input-output stability follows; (3) definition of intensive variable control loops (if needed) to achieve global stability, and (4) realization of the conceptual inventory and intensive variable control loops over the available degrees of freedom in the system by using, for instance, PI controllers. A tool to tune these PI control loops is developed, based on the solution of a nonlinear programming optimization problem (NLP), in order to complete the proposed hierarchical and systematic TBC design. The aim is to minimize a given cost function, subject to both the system dynamics, as well as the linear and nonlinear constraints (no disturbances affecting the system are considered), where the vector of decision variables will be formed by the parameters of the PI controllers used in the defined decentralized control loops. We will test this tuning procedure in several control designs developed for the challenging benchmark of the Tennessee Eastman Process (TEP) by Ricker and Larsson et al., as well as in two TBC candidates, concluding that the best candidate among the proposed ones (in terms of final cost function) will be one of these TBC designs. For solving the NLP problem, two local (FMINCON and NOMADm) solvers, and a new global (MITS) one are used, comparing their performances. Finally, the dynamic analysis of the optimal tuned closed loop systems is carried out, finding that the presented TBC control candidates will be stable, while the other control structures considered exhibit complex dynamic behaviors or even instability when disturbances affecting the process are considered. [PUBLICATION ABSTRACT] The need of designing decentralized control loops emerges to ensure the global stability of a given process plant. To that purpose, it has been proposed in recent works a systematic approach to derive robust decentralized controllers, which is based on the link between thermodynamics and passivity theory, as well as on the fundamentals of process networks. This thermodynamic-based control (TBC) methodology has several steps: (1) Decomposition of the considered process system into abstract mass and energy inventory networks; (2) design of conceptual mass and energy inventory control loops to guarantee the convergence of the states of the plant to a compact convex region defined by constant inventories, where input-output stability follows; (3) definition of intensive variable control loops (if needed) to achieve global stability, and (4) realization of the conceptual inventory and intensive variable control loops over the available degrees of freedom in the system by using, for instance, PI controllers. A tool to tune these PI control loops is developed, based on the solution of a nonlinear programming optimization problem (NLP), in order to complete the proposed hierarchical and systematic TBC design. The aim is to minimize a given cost function, subject to both the system dynamics, as well as the linear and nonlinear constraints (no disturbances affecting the system are considered), where the vector of decision variables will be formed by the parameters of the PI controllers used in the defined decentralized control loops. We will test this tuning procedure in several control designs developed for the challenging benchmark of the Tennessee Eastman Process (TEP) by Ricker and Larsson et al., as well as in two TBC candidates, concluding that the best candidate among the proposed ones (in terms of final cost function) will be one of these TBC designs. For solving the NLP problem, two local (FMINCON and NOMADm) solvers, and a new global (MITS) one are used, comparing their performances. Finally, the dynamic analysis of the optimal tuned closed loop systems is carried out, finding that the presented TBC control candidates will be stable, while the other control structures considered exhibit complex dynamic behaviors or even instability when disturbances affecting the process are considered. © 2008 American Institute of Chemical Engineers AIChE J, 2008 The need of designing decentralized control loops emerges to ensure the global stability of a given process plant. To that purpose, it has been proposed in recent works a systematic approach to derive robust decentralized controllers, which is based on the link between thermodynamics and passivity theory, as well as on the fundamentals of process networks. This thermodynamic‐based control (TBC) methodology has several steps: (1) Decomposition of the considered process system into mass and energy inventory networks; (2) design of conceptual mass and energy inventory control loops to guarantee the convergence of the states of the plant to a compact convex region defined by constant inventories, where input‐output stability follows; (3) definition of intensive variable control loops (if needed) to achieve global stability, and (4) realization of the conceptual inventory and intensive variable control loops over the available degrees of freedom in the system by using, for instance, PI controllers. A tool to tune these PI control loops is developed, based on the solution of a nonlinear programming optimization problem (NLP), in order to complete the proposed hierarchical and systematic TBC design. The aim is to minimize a given cost function, subject to both the system dynamics, as well as the linear and nonlinear constraints (no disturbances affecting the system are considered), where the vector of decision variables will be formed by the parameters of the PI controllers used in the defined decentralized control loops. We will test this tuning procedure in several control designs developed for the challenging benchmark of the Tennessee Eastman Process (TEP) by Ricker and Larsson et al., as well as in two TBC candidates, concluding that the best candidate among the proposed ones (in terms of final cost function) will be one of these TBC designs. For solving the NLP problem, two local (FMINCON and NOMADm) solvers, and a new global (MITS) one are used, comparing their performances. Finally, the dynamic analysis of the optimal tuned closed loop systems is carried out, finding that the presented TBC control candidates will be stable, while the other control structures considered exhibit complex dynamic behaviors or even instability when disturbances affecting the process are considered. © 2008 American Institute of Chemical Engineers AIChE J, 2008 The need of designing decentralized control loops emerges to ensure the global stability of a given process plant. To that purpose, it has been proposed in recent works a systematic approach to derive robust decentralized controllers, which is based on the link between thermodynamics and passivity theory, as well as on the fundamentals of process networks. This thermodynamic-based control (TBC) methodology has several steps: (1) Decomposition of the considered process system into abstract mass and energy inventory networks; (2) design of conceptual mass and energy inventory control loops to guarantee the convergence of the states of the plant to a compact convex region defined by constant inventories, where input-output stability follows; (3) definition of intensive variable control loops (if needed) to achieve global stability, and (4) realization of the conceptual inventory and intensive variable control loops over the available degrees of freedom in the system by using, for instance, PI controllers. A tool to tune these PI control loops is developed, based on the solution of a nonlinear programming optimization problem (NLP), in order to complete the proposed hierarchical and systematic TBC design. The aim is to minimize a given cost function, subject to both the system dynamics, as well as the linear and nonlinear constraints (no disturbances affecting the system are considered), where the vector of decision variables will be formed by the parameters of the PI controllers used in the defined decentralized control loops. We will test this tuning procedure in several control designs developed for the challenging benchmark of the Tennessee Eastman Process (TEP) by Ricker and Larsson et al., as well as in two TBC candidates, concluding that the best candidate among the proposed ones (in terms of final cost function) will be one of these TBC designs. For solving the NLP problem, two local (FMINCON and NOMADm) solvers, and a new global (MITS) one are used, comparing their performances. Finally, the dynamic analysis of the optimal tuned closed loop systems is carried out, finding that the presented TBC control candidates will be stable, while the other control structures considered exhibit complex dynamic behaviors or even instability when disturbances affecting the process are considered.
Author	Antelo, Luis T. Exler, Oliver Banga, Julio R. Alonso, Antonio A.
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Keywords	Closed loop Integral proportional control Stability nonlinear programming (NLP) tuning Benchmarks Non linear programming controller parameters PI controller Optimization Design Tennessee Eastman Process Decentralized control Cost function Inventory control Instability Mathematical programming
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References	Downs JJ,Vogel EF. A plant-wide industrial process control problem. Comput Chem Eng. 1993; 17: 245-255. Antelo LT,Otero-Muras I,Banga JR,Alonso AA. A systematic approach to plant-wide control based on thermodynamics. Comput Chem Eng. 2007a; 31(7): 677-691. Ricker NL,Lee JH. Nonlinear model predictive control of the Tennessee Eastman challenge process. Comput Chem Eng. 1995; 19(9): 961-981. Pedret C,Vilanova R,Moreno R,Serra I. A refinement procedure for PID control tuning. Comput Chem Eng. 2002; 26: 903-908. Battiti R,Tecchiolli G. The continuous tabu search: Blending combinatorial optimization and stochastic search for global optimization. Annals of Operations Res. 1996; 63: 153-188. Ogunnaike BA,Ray WH. Process Dynamics, Modeling and Control. New York: Oxford University Press; 1994. Alonso AA,Ydstie BE. Stabilization of distributed systems using irreversible thermodynamics. Automatica. 2001; 37: 1739-1755. Luyben ML,Tyreus BD,Luyben W. Plantwide control design procedure. AIChE J. 1997; 43: 3163-3174. Antelo L,Banga JR,Alonso AA. Hierarchical design of decentralized control structures for the Tennessee Eastman Process. Submitted to Comput Chem Eng. 2007b; 32: 1995-2015. Ricker NL. Decentralized control of de Tennessee Eastman Challenge Process. J Proc Contr. 1996; 6: 205-221. Tyreus BD. Dominant variables for partial control. 2. Application to the Tennessee Eastman Challenge Process. Ind Eng Chem Res. 1998; 38(4): 1444-1455. Price RM,Lyman PR,Georgakis C. Throughput manipulation in plantwide control structures. Ind Eng Chem Res. 1994; 33(5): 1197-1207. Ydstie BE,Alonso AA. Process systems and passivity via the Clausius-Planck inequality. Syst Contr Letts. 1997; 30: 253-264. Morari M,Zafirou E. Robust process control. Prentice-Hall International; 1989. Exler O,Antelo LT,Alonso AA,Banga JR. A Tabu Search-based algorithm for integrated process and control system design. Comput Chem Eng. 2006; 32: 1877-1891. Larsson T,Skogestad S. Self-Optimizing control of a large-scale plant: the Tennessee Eastman process. Ind Chem Eng Res. 2001; 40: 4889-4901. Jockenhövel T,Biegler LT,Wächter A. Dynamic optimization of the Tennessee Eastman process using the OptControlCentre. Comput Chem Eng. 2003; 27(11); 1513-1531. Lyman PR,Georgakis C. Plant-wide control of de Tennessee Eastman Problem. Comput Chem Eng. 1995; 19: 321-331. Abramson, M. A. Pattern Search Algorithms for Mixed Variable General Constrained Optimization Problems. Rice University: Houston, TX; 2002. Exler O,Schittkowski K. A trust region SQP algorithm for mixed-integer nonlinear programming. Optimization Letts. 2006; DOI 10.1007/s11590-006-0026-1. McAvoy TJ,Ye N. Base control for The Tennessee Eastman Problem. Comput Chem Eng. 1994; 35(8): 130-137. Farschman C,Viswanath K,Ydstie B. Process systems and inventory control. AIChE J. 1998; 44(8): 1841-1857. Baughman DR,Liu YA. Neural Networks in Bioprocessing and Chemical Engineering. San Diego: Academic Press; 1995. 1998; 38 2002; 26 1993; 17 1997; 43 2006; 32 1997; 30 1994; 33 2007a; 31 1996 1996; 63 1994; 35 2003; 27 1995 2006 2001; 37 1994 1995; 19 2002 2001; 40 2007b; 32 1998; 44 1989 1996; 6 McAvoy TJ (e_1_2_9_10_2) 1994; 35 e_1_2_9_21_2 e_1_2_9_12_2 Abramson M. A. (e_1_2_9_22_2) 2002 e_1_2_9_11_2 e_1_2_9_5_2 e_1_2_9_4_2 e_1_2_9_3_2 e_1_2_9_2_2 Morari M (e_1_2_9_8_2) 1989 e_1_2_9_9_2 Exler O (e_1_2_9_23_2) 2006 e_1_2_9_14_2 e_1_2_9_25_2 Ogunnaike BA (e_1_2_9_6_2) 1994 e_1_2_9_13_2 e_1_2_9_24_2 e_1_2_9_16_2 Baughman DR (e_1_2_9_20_2) 1995 e_1_2_9_15_2 Skogestad S (e_1_2_9_7_2) 1996 e_1_2_9_18_2 e_1_2_9_17_2 e_1_2_9_19_2
References_xml	– reference: Luyben ML,Tyreus BD,Luyben W. Plantwide control design procedure. AIChE J. 1997; 43: 3163-3174. – reference: Farschman C,Viswanath K,Ydstie B. Process systems and inventory control. AIChE J. 1998; 44(8): 1841-1857. – reference: Price RM,Lyman PR,Georgakis C. Throughput manipulation in plantwide control structures. Ind Eng Chem Res. 1994; 33(5): 1197-1207. – reference: Alonso AA,Ydstie BE. Stabilization of distributed systems using irreversible thermodynamics. Automatica. 2001; 37: 1739-1755. – reference: Pedret C,Vilanova R,Moreno R,Serra I. A refinement procedure for PID control tuning. Comput Chem Eng. 2002; 26: 903-908. – reference: Exler O,Schittkowski K. A trust region SQP algorithm for mixed-integer nonlinear programming. Optimization Letts. 2006; DOI 10.1007/s11590-006-0026-1. – reference: Antelo L,Banga JR,Alonso AA. Hierarchical design of decentralized control structures for the Tennessee Eastman Process. 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A plant-wide industrial process control problem. Comput Chem Eng. 1993; 17: 245-255. – reference: Antelo LT,Otero-Muras I,Banga JR,Alonso AA. A systematic approach to plant-wide control based on thermodynamics. Comput Chem Eng. 2007a; 31(7): 677-691. – reference: Ricker NL,Lee JH. Nonlinear model predictive control of the Tennessee Eastman challenge process. Comput Chem Eng. 1995; 19(9): 961-981. – reference: Ogunnaike BA,Ray WH. Process Dynamics, Modeling and Control. New York: Oxford University Press; 1994. – reference: Exler O,Antelo LT,Alonso AA,Banga JR. A Tabu Search-based algorithm for integrated process and control system design. Comput Chem Eng. 2006; 32: 1877-1891. – reference: Tyreus BD. Dominant variables for partial control. 2. Application to the Tennessee Eastman Challenge Process. Ind Eng Chem Res. 1998; 38(4): 1444-1455. – reference: Abramson, M. A. Pattern Search Algorithms for Mixed Variable General Constrained Optimization Problems. 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Snippet	The need of designing decentralized control loops emerges to ensure the global stability of a given process plant. To that purpose, it has been proposed in...
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SubjectTerms	Applications of mathematics to chemical engineering. Modeling. Simulation. Optimization Applied sciences Chemical engineering Closed loop systems controller parameters Controllers decentralized control Design Exact sciences and technology Nonlinear programming nonlinear programming (NLP) PI controller Problem solving Tennessee Eastman Process Thermodynamics tuning
Title	Optimal tuning of thermodynamic-based decentralized PI control loops: Application to the Tennessee Eastman Process
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