yes / 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.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/9752 |
| Date | January 2013 |
| Creators | Weerachaipichasgul, W., Kittisupakorn, P., Mujtaba, Iqbal |
| Source Sets | Bradford Scholars |
| Detected Language | English |
| Type | Conference Paper, Published version |
| Rights | © 2013 International Association of Engineers. Published Open-Access by Newswood Limited |
| Relation | http://www.iaeng.org/publication/IMECS2013/IMECS2013_pp121-126.pdf |
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