Multivariable control of a debutanizer column using equation based artificial neural network model inverse control strategies

CATEGORY: DEBUTANIZER 
Multivariable control of a debutanizer column using equation based artificial neural network model inverse control strategies

Type	Journal Article
Author	Nasser Mohamed Ramli
Author	Mohd Azlan Hussain
URL	http://www.sciencedirect.com/science/article/pii/S0925231216002290
Volume	194
Pages	135-150
Publication	Neurocomputing
Date	June 19, 2016
Abstract	The debutanizer column is an important unit operation in petroleum refining industries, becaues it is the main column to produce liquefied petroleum gas as its top product and light naphtha as its bottom product. The system is difficult to handle from a control standpoint because of its nonlinear behavior, multivariable interaction and existence of numerous constraints on both its manipulated and state variable. Recently, neural network techniques have been used for a wide variety of applications where statistical methods have been traditionally employed. Authors 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. Results reveal 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.