Living polymerisation reactors: Molecular weight distribution control using inverse neural network models

In principle, it is possible to exercise control over the molecular weight distribution (MWD) of the polymers produced from living polymerisation proc esses in flow reactors through the control of reactant feeds in a predeterm ined fashion. Some of the factors that influence the extent to which contro l can be achieved with feed perturbations to a single stage continuous flow stirred tank (CSTR) reactor have been reported previously. Here, attention is given to the problem of establishing inverse process models as a first step towards a fully automatic control strategy for the synthesis of polyme rs with pre-ordained MWD in a real process. Particular attention is given t o the development of a neural network model for predicting the instantaneou s reactor feed conditions for a specified product MWD and characterising th e MWD for the purpose of dimension reduction using principal component anal ysis. Data collected from a simulated ideal reactor process are used in the study. The way in which this approach will underpin a real laboratory-scal e polymerisation system is briefly outlined.