Cure kinetic parameter estimation of thermosetting resins with isothermal data by using particle swarm optimization

The heuristic algorithms have shown to be a powerful tool in parameter estimation. Among these algorithms, particle swarm optimization (PSO) has become a method whose application has been increasing quickly. In the present work a new way for parameter estimation from cure kinetic model of polymeric resin using a differential-algebraic approach is shown. The PSO was applied to minimize the least squares function and to find the parameters from an autocatalytic model for describing cure kinetics of thermosetting resins. The isothermal data were obtained at four temperatures: 318, 333, 348 and 363 K. Three parameter estimation procedures were compared for finding a parameter set for all temperatures simultaneously. In the first one, called classical method, a curing rate was calculated with experimental values of the degree of cure and the temperature. In the second and third methods, the curing rate was obtained from the integration of a differential-algebraic system and the main difference between them is the objective function and the way to determine the ultimate reaction heat. All methods showed good results; however, the third method was more accurate than the others. The confidence regions of all parameters were found and they were used to give us indication whether the parameters estimated here by different methods are statistically different.