A two-phase dynamic model,describing gas phase propylene polymerization in a fluidized bed reactor,was used to explore the dynamic behavior and process control of the polypropylene production rate and reactor temperature.The open loop analysis revealed the nonlinear behavior of the polypropylene fluidized bed reactor,justifying the use of an advanced control algorithm for efficient control of the process variables.In this case,a centralized model predictive control(MPC) technique was implemented to control the polypropylene production rate and reactor temperature by manipulating the catalyst feed rate and cooling water flow rate respectively.The corresponding MPC controller was able to track changes in the setpoint smoothly for the reactor temperature and production rate while the setpoint tracking of the conventional proportional-integral(PI) controller was oscillatory with overshoots and obvious interaction between the reactor temperature and production rate loops.The MPC was able to produce controller moves which not only were well within the specified input constraints for both control variables,but also non-aggressive and sufficiently smooth for practical implementations.Furthermore,the closed loop dynamic simulations indicated that the speed of rejecting the process disturbances for the MPC controller were also acceptable for both controlled variables. A two-phase dynamic model, describing gas phase propylene polymerization in a fluidized bed reactor, was used to explore the dynamic behavior and process control of the polypropylene production rate and reactor temperature. The open loop analysis revealed the nonlinear behavior of the polypropylene fluidized bed reactor, justifying the use of an advanced control algorithm for efficient control of the process variables. In this case, a centralized model predictive control (MPC) technique was implemented to control the polypropylene production rate and reactor temperature by manipulating the catalyst feed rate and cooling water flow rate respectively. The corresponding MPC controller was able to track changes in the setpoint smoothly for the reactor temperature and production rate while the setpoint tracking of the conventional proportional-integral (PI) controller was oscillatory with overshoots and obvious interaction between the reactor temperature and production rate loops.The MPC was able to produce controller moves which not only were well within the specified input constraints for both control variables, but also non-aggressive and smooth smooth for practical implementations. Morerther, the closed loop dynamic simulations indicated that the speed of rejecting the process disturbances for the MPC controller were also acceptable for both controlled variables