Nasser Mohamed Ramli

Additional information is available at the end of the chapter Nasser Mohamed Ramli

http://dx.doi.org/10.5772/intechopen.70704 Additional information is available at the end of the chapter

Abstract

The debutanizer column is an important unit operation in petroleum refining industries. The top product is liquefied petroleum gas and the bottom product is light naphtha. This system is difficult to handle. This is because due to its non-linear behavior, multivariable interaction and existence of numerous constraints on its manipulated variable. Neural network techniques have been increasingly used for a wide variety of applications. In this book, equation-based multi-input multi-output (MIMO) neural network has been proposed for multivariable control strategy to control the top and bottom temperatures of the column. The manipulated variables for column are reflux and reboiler flow rates, respectively. This neural network model are based on multivariable equation, instead of the normal black box structure. It has the advantage of being robust in nature while being easier to interpret in terms of its input-output variables. It has been employed for set point changes and disturbance changes. The results show that the neural network equation-based model for direct inverse and internal model approach performs better than the conventional proportional, integral and derivative (PID) controller.

DOI: 10.5772/intechopen.70704

Keywords: distillation column, artificial neural network, equation-based method, multivariable process control
