Branch and bound method for multiobjective control structure design
Control structure design deals with the selection of controlled and manipulated variables, and the pairings interconnecting these variables. The available criteria for these tasks require enumeration of every alternative, which can be computationally forbidding for large-scale process. Owing...
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Main Authors: | , |
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Other Authors: | |
Format: | Conference or Workshop Item |
Language: | English |
Published: |
2009
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Subjects: | |
Online Access: | https://hdl.handle.net/10356/91099 http://hdl.handle.net/10220/6028 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | Control structure design deals with the selection of controlled and manipulated variables, and the pairings interconnecting
these variables. The available criteria for these tasks require enumeration of every alternative, which can be computationally forbidding for large-scale process. Owing to
the computational complexity, variables and pairings are often selected sequentially, which may result in sub-optimal control
structures. In this paper, an efficient branch and bound (BAB) method is proposed to select the variables and pairings together
in a multiobjective optimization framework. As an illustration of the proposed multiobjective BAB framework, the minimum
singular value rule and the mu-interaction measure are used as the criteria for selection of controlled variables and pairings, respectively. Numerical tests using randomly generated matrices and large-scale case study of HDA process show that the BAB method is able to reduce the solution time by several orders of magnitude in comparison with exhaustive search. |
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