The control of a gas phase propylene polymerization model in a fluidized bed reactor was studied, where the rigorous two phase dynamic model takes into account the polymerization reactions occurring in the bubble and emulsion phases. Due to the nonlinearity of the process, the employment of an advanced control scheme for efficient regulation of the process variables is justified. In this case, the Adaptive Predictive Model-Based Control (APMBC) strategy (an integration of the Recursive Least Squares algorithm, RLS and the Generalized Predictive Control algorithm, GPC) was employed to control the polypropylene production rate and emulsion phase temperature by manipulating the catalyst feed rate and reactor cooling water flow, respectively. Closed loop simulations revealed the superiority of the APMBC in setpoint tracking as compared to the conventional PI controllers tuned using the Internal Model Control (IMC) method and the standard Ziegler-Nichols (Z-N) method. Moreover, the APMBC was able to efficiently arrest the effects of superficial gas velocity, hydrogen concentration and monomer concentration on the process variables, thus exhibiting excellent regulatory control properties.
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