Improvement of multicomponent batch reactive distillation under parameter uncertainty by inferential state with model predictive control

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.

URI

http://hdl.handle.net/10454/9752

Version

Published version

Citation

Weerachaipichasgul W, Kittisupakorn P and Mujtaba IM (2013) Improvement of multicomponent batch reactive distillation under parameter uncertainty by inferential state with model predictive control. Lecture Notes in Engineering and Computer Science. Proceedings of the International MultiConference of Engineers and Computer Scientists, IMECS 2013, March 13-15, 2013, Hong Kong. Vol I, pp. 121-126.

Link to published version

http://www.iaeng.org/publication/IMECS2013/IMECS2013_pp121-126.pdf

Type

Conference Paper