%0 Journal Article
%@ 09252312
%A Ramli, N.M.
%A Hussain, M.A.
%A Jan, B.M.
%D 2016
%F scholars:6980
%I Elsevier B.V.
%J Neurocomputing
%K Inverse problems; Liquefied petroleum gas; MIMO systems; Multivariable control systems; Neural networks, Artificial neural network modeling; Debutanizer columns; Equation based; Multi input multi output; Multivariable control; Network-based modeling; Neural network model; Neural network techniques, Petroleum refining, Article; artificial neural network; control strategy; control system; controlled study; debutanizer column; flow rate; fuel and fuel related phenomena; intermethod comparison; mathematical computing; mathematical model; multi input multi output; priority journal
%P 135-150
%R 10.1016/j.neucom.2016.02.026
%T Multivariable control of a debutanizer column using equation based artificial neural network model inverse control strategies
%U https://khub.utp.edu.my/scholars/6980/
%V 194
%X The debutanizer column is an important unit operation in petroleum refining industries as it is the main column to produce liquefied petroleum gas as its top product and light naphtha as its bottom product. This system is difficult to handle from a control standpoint due to its nonlinear behavior, multivariable interaction and existence of numerous constraints on both its manipulated and state variable. Neural network techniques have been increasingly used for a wide variety of applications where statistical methods have been traditionally employed. In this work we propose to use an equation based MIMO (Multi Input Multi Output) neural network based multivariable control strategy to control the top and bottom temperatures of the column simultaneously, while manipulating the reflux and reboiler flow rates respectively. This equation based neural network model represented by a multivariable equation, instead of the normal black box structure, has the advantage of being robust in nature while being easier to interpret in terms of its input output variables. It is implemented for set point changes and disturbance changes and the results show that the neural network based model method in the direct inverse and internal model approach performs better than the conventional PID method in both cases. © 2016 Elsevier B.V.
%Z cited By 15