Multi-step Ahead Prediction Analysis for MPC-relevant Models

H., Zabiri and M., Ramasamy and Lemma D, Tufa and Maulud, Abdulhalim (2013) Multi-step Ahead Prediction Analysis for MPC-relevant Models. In: INTERNATIONAL OIL & GAS SYMPOSIUM AND EXHIBITION , 9-11 October, Kota Kinabalu, Sabah.



Model predictive control (MPC) is one of the most successful controllers in industries and widely applied in petroleum refining and petrochemical processes. Its inherent model-based strategy, however, renders it sensitive to changes that occur when the plants operate outside the boundaries of its original operating conditions. In this paper, a nonlinear empirical model based on parallel orthonormal basis function-neural networks structure, which has been shown to be able to extend the applicable regions of the model, is evaluated for its multi-step ahead prediction capability and compared to the conventional neural networks models with different scaling procedures. It has been shown that the nonlinear model exhibited sufficient multi-step ahead prediction capability that renders it a promising candidate for MPC applications that can potentially improve the closed-loop control performance in extended regions and this is important in retaining the positive benefits of MPC in industries.

Item Type:Conference or Workshop Item (Paper)
Subjects:T Technology > TP Chemical technology
Academic Subject One:Academic Department - Chemical Engineering - Advance Process Control
Academic Subject Three:petroleum engineering
Departments / MOR / COE:Departments > Chemical Engineering
ID Code:10750
Deposited By: Haslinda Zabiri
Deposited On:16 Dec 2013 23:48
Last Modified:20 Mar 2017 01:59

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