Application of Multivariable Model Predictive Control (4x4) for Dimethyl Ether Purification from Methanol
Multi-variable model predictive control (MMPC) was used to control the dimethyl ether (DME) purification process of methanol in the production of DME from synthesis gas. The use of MMPC aims to capture the phenomenon of the interaction between the variables in the process in order to improve the...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Universitas Indonesia
2019-11-01
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| Series: | International Journal of Technology |
| Subjects: | |
| Online Access: | http://ijtech.eng.ui.ac.id/article/view/3631 |
| Summary: | Multi-variable model predictive
control (MMPC) was used to control the dimethyl ether (DME) purification
process of methanol in the production of DME from synthesis gas. The use of
MMPC aims to capture the phenomenon of the interaction between the variables in
the process in order to improve the control performance. As the process
comprises four input variables and four output variables, MMPC (4×4) is used
in this study. The inter-variable interaction is shown in a 4×4 matrix,
where each matrix element is a first-order plus dead-time (FOPDT) model. MMPC
(4×4)
was tested by changing the set point (SP) and disturbance rejection. The
control performance indicators used are integral absolute error (IAE) and
integral square error (ISE) and, as a comparison, the control performance of
the single-input single-output (SISO) model predictive control (MPC). The
results show that MMPC (4×4) is better than MPC in both IAE and ISE. In terms of SP change,
MMPC (4×4)
is able to significantly improve the control performance of MPC, by 78% (IAE)
and 90% (ISE). Whereas in the disturbance rejection testing, the improvements
in control performance were 58 % (IAE) and 81% (ISE). |
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| ISSN: | 2086-9614 2087-2100 |