Application of multivariable model predictive control to overcome the intervariable interaction in CO 2 removal process

Abdul Wahid, Fitriani Meizvira, Yoga Wiranoto

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)


Multivariable model predictive control (MMPC) was applied in CO2 removal process in a natural gas treatment from an industry located in Subang field, which used chemical absorption. MMPC is a variation of model predictive control (MPC) which can account for more than one control variable at once and is classified in advanced control category. MMPC is expected to give a better performance in handling the process as well as being able to overcome intervariable interaction that is prone to happen in multiple input multiple output (MIMO) system. MMPC was applied in the process to get a better process control performance compared to the one using PI controller and to make any intervariable interaction in the process more manageable. The indicator for each goal was integral square error (ISE). The result showed that identified intervariable interaction was between the pressure of gas feed in and the flow of make-up water to absorber. By using MMPC, the ISE of controller's performance was improved from the PI-controller that was used in the plant. The improvement for ISE was 32.62% (PIC-1101) and 72.67% (FIC-1102) in the SP tracking, and 52.54% (PIC-1101) and 57.41% (FIC-1102) in the disturbance rejection. MMPC implementation also showed a better response in handling intervariable interaction in the process.

Original languageEnglish
Article number03049
JournalE3S Web of Conferences
Publication statusPublished - 26 Nov 2018
Event3rd International Tropical Renewable Energy Conference "Sustainable Development of Tropical Renewable Energy", i-TREC 2018 - Kuta, Bali, Indonesia
Duration: 6 Sep 20188 Sep 2018


Dive into the research topics of 'Application of multivariable model predictive control to overcome the intervariable interaction in CO <sub>2</sub> removal process'. Together they form a unique fingerprint.

Cite this