Self-adaptive chaotic local search particle swarm optimization for propylene explosion region parameter identification

Abstract

A new parameter identification method for propylene explosion region based on self-adaptive chaotic local search particle swarm optimization (SACLSPSO) algorithm is proposed. First, the chaotic local search is integrated with the particle swarm optimization (PSO), which contributes to avoid PSO falling into local minima. Then, the nonlinear dynamics inertia weight coefficient is incorporated into the chaos local search particle swarm optimization (CLSPSO) algorithm to balance the overall search ability and the local improvement ability of the CLSPSO. Third, the learning factors with asynchronous change are developed to enhance the capability of the information exchange. It is shown that the proposed algorithm has some advantages, such as good stability, strong information exchange capacity and fast convergence. Meanwhile, it can also overcome the drawback of easily falling into the local minimum. Finally, the simulation results show that the proposed SACLSPSO algorithm could achieve high accuracy in the parameter identification of the propylene explosion region. The propylene explosion region of reactor is from 2.5% to 12.7% and the propylene explosion region of mixer is from 2.6% to 11.4%, which are in the reasonable interval (2%∼11%). Therefore, the safety of propylene oxidation process is ensured and the efficacy of the proposed algorithm is proved.