Identification of thermal process using fractional-order transfer function based on intelligent optimization

Derivative order introduced by non-integer differentiation and integration concept constitutes an additional degree of freedom allowing a more accurate modeling of several physical phenomena. At the same time its complexity coming from the character of history dependence and universe mutuality makes the identification process more difficult. According to the dynamic characteristics of some typical thermal processes such as drum water level, main steam temperature and bed temperature of a 450 t/h circulating fluidized bed boiler, a kind of fractional transfer function is designed. Particle Swarm Optimization (PSO) is used to estimate the parameters include the order and the coefficients. The fitness function is the Integral of Squared Errors (ISE) between the output of actual system and the model identified. The data used for the calculations are step response data collected from a power plant. Simulation results show that the proposed scheme offers a higher degree of accuracy, compared with integral models which are obtained with the same method.