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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Bibliographic Details
Published in:AIChE journal Vol. 55; no. 4; pp. 919 - 930
Main Authors: Tosukhowong, Thidarat, Lee, Jay H
Format: Journal Article
Language:English
Published: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.04.2009
Wiley
American Institute of Chemical Engineers
Subjects:
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
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
ISSN:0001-1541, 1547-5905
Online Access:Get full text
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Summary: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
Bibliography:http://dx.doi.org/10.1002/aic.11805
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ISSN:0001-1541
1547-5905
DOI:10.1002/aic.11805