Neural-Wiener-based Model Predictive Control (NWMPC) for Methyl Tert-butyl Ether Catalytic Distillation
The reactive distillation of methyl tert-butyl ether (MTBE) involves strong interactions between variables and is a highly nonlinear process. Here, a nonlinear model predictive control (MPC) was proposed to tackle the nonlinearity and the interaction involved in controlling the tray temperature i...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor's University
2015
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| Subjects: | |
| Online Access: | http://eprints.usm.my/42780/1/JES_Vol._11_2015_-_Art._1%281-8%29.pdf http://eprints.usm.my/42780/ http://web.usm.my/jes/11_2015/JES%20Vol.%2011%202015%20-%20Art.%201(1-8).pdf |
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| Summary: | The reactive distillation of methyl tert-butyl ether (MTBE) involves strong
interactions between variables and is a highly nonlinear process. Here, a nonlinear
model predictive control (MPC) was proposed to tackle the nonlinearity and the
interaction involved in controlling the tray temperature in MTBE reactive distillation. To
improve the performance of the MPC, an advanced nonlinear block-oriented model
known as the neural Wiener model was employed. The control study was successfully
simulated using Simulink (Matlab), which is integrated with the Aspen dynamic model.
Set-point tracking, disturbance rejection and robustness tests were conducted to evaluate
the neural-Wiener-based MPC (NWMPC) performance. The results achieved show that
the NWMPC is able to maintain the product purity at its set-point of 99%, with isobutene
conversion exceeding 99.98%. NWMPC is also able to reject disturbances, as shown in
disturbance rejection study performed by changing the feed flowrate to 30% of the
nominal value. This controller is also very robust and thus able to control the MTBE
reactive distillation, even when the column efficiency was reduced to 80%. |
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