Nonlinear Fuzzy Identification of Batch Polymerization Processes

Abstract First-principles modelling of polymer systems is usually complex and time-consuming, often leading to correlations of restricted range of applicability with unavailable parameters. Thus, the optimal control of polymerization processes using such models is a demanding task, especially when tracked batch reactors in which the systems have typical transient behaviour. In this paper, the fuzzy logic is applied to model discontinuous polymerization reactors. The proposed fuzzy methodology allows the formulation of a global nonlinear long-range prediction model from the conjunction of a number of local linear fuzzy dynamic models. The pilot-plant-scale synthesis of poly(lactic acid) (PLA) and nylon-6 were adopted for performance evaluation of proposed method. Satisfactory results were achieved. Therefore, the proposed technique can be useful to obtain appropriate representations of systems of complex modelling.