Approximate dynamic programming based optimal control applied to an integrated plant with a reactor and a distillation column with recycle

An approximate dynamic programming (ADP) method has shown good performance in solving optimal control problems in many small-scale process control applications. The offline computational procedure of ADP constructs an approximation of the optimal "cost-to-go" function, which parameterizes...

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Veröffentlicht in:AIChE journal Jg. 55; H. 4; S. 919 - 930
Hauptverfasser: Tosukhowong, Thidarat, Lee, Jay H
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.04.2009
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Abstract An approximate dynamic programming (ADP) method has shown good performance in solving optimal control problems in many small-scale process control applications. The offline computational procedure of ADP constructs an approximation of the optimal "cost-to-go" function, which parameterizes the optimal control policy with respect to the state variable. With the approximate "cost-to-go" function computed, a multistage optimization problem that needs to be solved online at every sample time can be reduced to a single-stage optimization, thereby significantly lessening the real-time computational load. Moreover, stochastic uncertainties can be addressed relatively easily within this framework. Nonetheless, the existing ADP method requires excessive offline computation when applied to a high-dimensional system. A case study of a reactor and a distillation column with recycle was used to illustrate this issue. Then, several ways were proposed to reduce the computational load so that the ADP method can be applied to high-dimensional integrated plants. The results showed that the approach is much more superior to NMPC in both deterministic and stochastic cases. © 2009 American Institute of Chemical Engineers AIChE J, 2009
AbstractList An approximate dynamic programming (ADP) method has shown good performance in solving optimal control problems in many small-scale process control applications. The offline computational procedure of ADP constructs an approximation of the optimal cost-to-go function, which parameterizes the optimal control policy with respect to the state variable. With the approximate "cost-to- go" function computed, a multistage optimization problem that needs to be solved online at every sample time can be reduced to a single-stage optimization, thereby significantly lessening the real-time computational load. Moreover, stochastic uncertainties can be addressed relatively easily within this framework. Nonetheless, the existing ADP method requires excessive offline computation when applied to a high-dimensional system. A case study of a reactor and a distillation column with recycle was used to illustrate this issue. Then, several ways were proposed to reduce the computational load so that the ADP method can be applied to high-dimensional integrated plants. The results showed that the approach is much more superior to NMPC in both deterministic and stochastic cases. [PUBLICATION ABSTRACT]
An approximate dynamic programming (ADP) method has shown good performance in solving optimal control problems in many small-scale process control applications. The offline computational procedure of ADP constructs an approximation of the optimal "cost-to-go" function, which parameterizes the optimal control policy with respect to the state variable. With the approximate "cost-to-go" function computed, a multistage optimization problem that needs to be solved online at every sample time can be reduced to a single-stage optimization, thereby significantly lessening the real-time computational load. Moreover, stochastic uncertainties can be addressed relatively easily within this framework. Nonetheless, the existing ADP method requires excessive offline computation when applied to a high-dimensional system. A case study of a reactor and a distillation column with recycle was used to illustrate this issue. Then, several ways were proposed to reduce the computational load so that the ADP method can be applied to high-dimensional integrated plants. The results showed that the approach is much more superior to NMPC in both deterministic and stochastic cases. © 2009 American Institute of Chemical Engineers AIChE J, 2009
An approximate dynamic programming (ADP) method has shown good performance in solving optimal control problems in many small-scale process control applications. The offline computational procedure of ADP constructs an approximation of the optimal cost-to-go function, which parameterizes the optimal control policy with respect to the state variable. With the approximate cost-to-go function computed, a multistage optimization problem that needs to be solved online at every sample time can be reduced to a single-stage optimization, thereby significantly lessening the real-time computational load. Moreover, stochastic uncertainties can be addressed relatively easily within this framework. Nonetheless, the existing ADP method requires excessive offline computation when applied to a high-dimensional system. A case study of a reactor and a distillation column with recycle was used to illustrate this issue. Then, several ways were proposed to reduce the computational load so that the ADP method can be applied to high-dimensional integrated plants. The results showed that the approach is much more superior to NMPC in both deterministic and stochastic cases.
Author Tosukhowong, Thidarat
Lee, Jay H.
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10.1016/j.jprocont.2005.04.010
10.1002/rnc.822
10.1137/0905052
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10.1016/S0959-1524(01)00014-2
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Issue 4
Keywords Uncertainty
approximate dynamic programming
Approximation
Process control
dynamic optimization
Real time
Modeling
Real time system
Optimization
State variable
integrated plant
Optimal control
Non linear model
nonlinear optimal control
nonlinear model predictive control
Dynamic programming
Reactor
Distillation column
Mathematical programming
Predictive control
Language English
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An approximate dynamic programming (ADP) method has shown good performance in solving optimal control problems in many small‐scale process control...
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SubjectTerms Applications of mathematics to chemical engineering. Modeling. Simulation. Optimization
Applied sciences
approximate dynamic programming
Chemical engineering
Distillation
dynamic optimization
Dynamic programming
Exact sciences and technology
integrated plant
nonlinear model predictive control
nonlinear optimal control
Parameter optimization
Process control
Reactors
Stochastic models
Uncertainty
Title Approximate dynamic programming based optimal control applied to an integrated plant with a reactor and a distillation column with recycle
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