Abstract
Batch reactors are widely used in the polymer industry, especially for multi-purpose processes where different types of polymers are produced on demand. Batch polymerization reactors impose rather great operational difficulties due to the complex reaction kinetics and inherent process nonlinearities. Thus it is a difficult task to develop mathematical models for polymerization processes. If required for process control purposes the model should be accurate but simple, so that it can be used in a control loop. The present work shows the application of the neural network approach in the development of a predictive model for a styrene polymerization pilot plant, located at the Laboratory of Chemical Systems Engineering, School of Chemical Engineering at UNICAMP. Artificial Neural Networks have become a usual application in many areas of engineering, and are well suited for chemical processes due to their ability to describe multi-variable non-linear models. However to control purposes, the consideration of the variation of the process variables in real time is required as input to the model, to ensure the representation at the dynamics of the process. The experimental prototype consists of a jacketed stirred reactor, using thermal fluid as a heat source. Reaction progress was measured by a density sensor situated in a external recycle loop. Temperature sensors were positioned both inside the reactor and in the inlet and outlet of the jacket. Traditional feedforward neural networks with back-step inputs and the Elman network were applied to obtain the best model to be employed in a control loop. A comparison between the networks was performed, showing that, for process dynamics modeling, both networks were able to create suitable polymerization models.
