Adaptive multi-model predictive control applied to continuous stirred tank reactor

Performance evaluation of adaptive based model predictive control for ethylene glycol production from dimethyl oxide hydrogenation

Advance process control is a proven control and optimization technology delivering measurable and sustainable improvements in production yield, coupled with the added value of energy savings. In this work, an adaptive based model predictive control (aMPC) is developed and implemented to control the hydrogenation of dimethyl oxide to ethylene glycol (EG) in a plug flow reactor. The aMPC is compared with 3 other control schemes; proportional-integral (PI), decoupled PI (dPI) and linear model predictive control. The aim is to determine the reliability of aMPC in controlling the production rate and reactor temperature for an optimized hydrogenation reactor. The aspects compared include set point tracking, disturbance rejection and robustness test. The analysis concludes that the aMPC offers the best overall performance compared to the other control schemes.