Decentralized Advanced Model Predictive Controller of Fluidized-Bed for Polymerization Process

The control of fluidized-bed operations processes is still one of the major areas of research due to the complexity of the process and the inherent nonlinearity and varying dynamics involved in its operation. There are varieties of problems in chemical engineering that can be formulated as NonLinear Programming (NLPs). The quality of the developed solution significantly affects the performance of such system. Controller design involves tuning the process controllers and implementing them to achieve certain performance of controlled variables by using Sequential Quadratic Programming (SQP) method to tackle the constrained high NLPs problem for modified mathematical model for gas phase olefin polymerization in fluidized-bed catalytic reactor. The objective of this work is to present a comparative study; PID control is compared to an advanced neural network based MPC decentralized controller and also, see the effect of SQP on the performance of controlled variables. The two control approached were evaluated for set point tracking and load rejection properties giving acceptable results.