Abstract
This is a detailed review on olefin polymerization models, and the most recent process control approaches used to control these nonlinear systems are presented. Great focus has been given to the various approaches of fluidized-bed reactor (FBR) modeling. Currently, there has yet to be a single model that blends these modeling aspects together into one single formulation. In this article, the classification of models works by looking at their assumption in considering the phases inside the system. Researchers have been unraveling vast information to narrate in detail the relations between various variables that can be found in FBRs. Although it is not difficult to understand about the basics of modeling polymer properties, a gap exists for future researchers to justify in detail the phenomena and reduce the gap between model predictions and the actual data. The various controlling approaches to control these FBRs have also been reviewed and categorized depending on the method they used to control significant parameters of this nonlinear system. The progress that can be expected in this field leads to the creation of more efficient reactors and minimizing waste.
Neural Networks for Prediction and Control of Chaotic Fluidized Bed Hydrodynamics