Improvement of Multicomponent Batch Reactive Distillation under Parameter Uncertainty by Inferential State with Model Predictive Control

CATEGORY: BATCH REACTIVE DISTILLATION
Proceedings of the International MultiConference of Engineers and Computer Scientists 2013 Vol I,
IMECS 2013, March 13 - 15, 2013, Hong Kong
Improvement of Multicomponent Batch Reactive Distillation under Parameter Uncertainty by Inferential State with Model Predictive Control
W. Weerachaipichasgul, is with Department of Chemical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, THAILAND.
P. Kittisupakorn, is with Department of Chemical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, THAILAND(corresponding author to provide phone: +66-02-2186878; fax.: +66-02-2186877; e-mail: Paisan.K@chula.ac.th .
I. M. Mujtaba, is with School of Engineering Design and Technology, University of Bradford, EDT 3, West Yorkshire BD7 1DP, UK.
Abstract
Batch reactive distillation is aimed at achieving a high purity product, therefore, there is a great deal to find an optimal operating condition and effective control strategy to obtain maximum of the high purity product. An off-line dynamic optimization is first performed with an objective function to provide optimal product composition for the batch reactive distillation: maximum productivity. An inferential state estimator (an extended Kalman filter, EKF) based on simplified mathematical models and on-line temperature measurements, is incorporated to estimate the compositions in the reflux drum and the reboiler. Model Predictive Control (MPC) has been implemented to provide tracking of the desired product compositions subject to simplified model equations. Simulation results demonstrate that the inferential state estimation can provide good estimates of compositions.
Therefore, the control performance of the MPC with the inferential state is better than that of PID. In addition, in the presence of unknown/uncertain parameters (forward reaction rate constant), the estimator is still able to provide accurate concentrations. As a result, the MPC with the inferential state is still robust and applicable in real plants.
I. INTRODUCTION
The modeling of batch reactive distillation [2- 5] have been applied to achieve the high quality of product by the optimization technique. An objective function in the optimization problem depends on the nature of the problem maximum profit, minimum time, maximum conversion, and maximum product concentration. To obtain the product purity, the controller can be employed directly by using on-line measured composition but this measurement is expensive, difficult to maintain, necessitating frequent calibrations and it introduces measurement delay. Although the temperature measurement is suitable than the composition measurement, the product quality maybe off-spec and it can be known only at the end of the batch by using a direct temperature control. The tray temperature in distillation column does not correspond exactly to the compositions. Thus inferential state control is one of the solutions that can be applied.
For the batch distillation with/without reaction processes, there are many techniques to infer compositions from the temperature data and then the estimated states are fed back to the controller; for example, an extended Luenberger Observer (ELO) with a conventional PI controller , an extended Kalman filter (EKF) , a Kalman filter based on multiple reduced order models with a model predictive control based on reduced order model , and an artificial neural network (ANN) estimator. When composition control for batch reactive distillation is focused, it has not been much addressed.
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