A cognitive approach to develop dynamic models: application to polymerization systems

Abstract

An alternative procedure based on cognitive approach is applied to develop dynamic models. The solution copolymerization of methyl methacrylate and vinyl acetate in a continuous stirred tank reactor is analyzed to illustrate the cognitive model development. Factorial planning was used to discriminate the process variables with higher impact on the process performance (effects) and they are used to built-up a dynamic model based on the functional fuzzy relationship of Takagi–Sugeno type. Gaussian membership functions are considered for the cognitive sets and subtractive clustering method supplied the parameters of the premises of the model. Consequence functions are obtained through an optimization problem solved by a least square based algorithm. The kinetic parameters and reactor operating conditions are obtained from the literature and a mathematical model is considered as plant for identification data generation. Dynamic cognitive models showed satisfactory predictive capabilities and may be an interesting alternative to attack problems of modeling in chemical processes.