TY - JOUR
T1 - Application of multivariable model predictive control to overcome the intervariable interaction in CO 2 removal process
AU - Wahid, Abdul
AU - Meizvira, Fitriani
AU - Wiranoto, Yoga
N1 - Publisher Copyright:
© The Authors, published by EDP Sciences, 2018.
PY - 2018/11/26
Y1 - 2018/11/26
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85058714312&partnerID=8YFLogxK
U2 - 10.1051/e3sconf/20186703049
DO - 10.1051/e3sconf/20186703049
M3 - Conference article
AN - SCOPUS:85058714312
VL - 67
JO - E3S Web of Conferences
JF - E3S Web of Conferences
SN - 2555-0403
M1 - 03049
T2 - 3rd International Tropical Renewable Energy Conference "Sustainable Development of Tropical Renewable Energy", i-TREC 2018
Y2 - 6 September 2018 through 8 September 2018
ER -