Research on parameter identification methods for SCR system based on the improved parallel stepped-up chaos optimization algorithm

Abstract

Selective catalytic reduction (SCR) systems are distributed systems with strong time-varying parameter characteristics such that an accurate model is difficult to establish and that their parameter identification remain a formidable challenge. The normal parallel chaos optimization algorithm (PCOA) is prone to falling into local optimum problems and the search capability needs to be improved. In this paper, an improved parallel stepped-up chaos optimization algorithm (PSCOA) is proposed to effectively coordinate multiple search processes by further mapping between control sequences and optimization variables. The test results on a series of standard functions show that the PSCOA algorithm proposed in this paper has better capability to find optimization objective and algorithm stability. Subsequently, this method is applied to the parameter identification of the SCR system model. The comparison between estimated values and experimented data shows that the proposed PSCOA method can achieve a significant improvement in the identification of accurate parameters compared to PCOA method in place of existing text.