Incorporating feedforward action into self-optimising control policies

Show simple item record Umar, Lia Maisarah - Cao, Yi - Kariwala, Vinay - 2014-07-09T04:00:55Z 2014-07-09T04:00:55Z 2014-01-31T00:00:00Z -
dc.identifier.citation Lia Maisarah Umar, Yi Cao and Vinay Kariwala, Incorporating feedforward action into self-optimising control policies, The Canadian Journal of Chemical Engineering, Volume 92, Issue 1, pages 90–96, January 2014.
dc.identifier.issn 0008-4034 -
dc.identifier.uri -
dc.description.abstract Control structure design traditionally involves two steps of selections, namely the selection of controlled and manipulated variables and the selection of pairings interconnecting these variables. The available criteria for both selections require enumeration of every alternative. Hence, an exhaustive search can be computationally forbidding for large-scale processes. On the other hand, owing to the computational complexity, variables and pairings are often selected sequentially, which may result in suboptimal 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 μ-interaction measure are used as the criteria for selection of controlled variables and pairings, respectively. Numerical tests using randomly generated matrices and the large-scale case study of hydrodealkylation of toluene (HDA) process show that the BAB method is able to reduce the solution time by several orders of magnitude in comparison with exhaustive search. en_UK
dc.publisher Chemical Institute of Canada en_UK
dc.rights The definitive version is available at
dc.title Incorporating feedforward action into self-optimising control policies en_UK
dc.type Article -

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