Application of neural networks for the definition of the operating conditions of fluidized bed polymerization reactors

The development of polymer resins can benefit from the application of neural networks. The aim of this paper is to present how neural networks can help deal with the development of new resins, starting from the end user properties to set up the reactor's operating conditions. The procedure presented in this paper consists of a network that predicts the operating conditions of the reactor with maximum error of 2%. New resins can be developed and the reactor's operating conditions can be set in a much faster way, reducing the number of experiments and pilot plant tests, and hence, time and money spent on development.