The thermal process parameters identification by swarm intelligence algorithm has disadvantages of strong randomness

slow convergence and time-consuming.Against this problem

this paper proposes a quick identification method.First of all

simulation was carried out to verify that the identification problem's target function shows unimodal characteristics to each parameter.Then

combing the golden section method with coordinate alternation method

the author proposed the dimension-by-dimension golden section method

and improved it by selecting the optimal initial point and enhancing the optimization accuracy.Finally

on the basis of the field tests data

the particle swarm optimization(PSO)algorithm

dimension-bydimension golden section method and its improved method were applied to identify the process parameters of air/coal ratio and excess oxygen content model.The results show that

the improved dimension-by-dimension golden section method is superior to the other two methods in quickness and accuracy

which is more suitable for online identification of the thermal process parameters

so it provides the conditions for adjusting parameters' online optimization in thermal control system.