Fluid catalytic cracking (FCC) kinetic models are characterized by high dimension, nonlinearity, discontinuity, and non‐differentiability. Particle swarm optimization is easy to fall into local optima prematurely when it is applied to the optimization of kinetic models. To solve this problem, an improved two‐swarm cooperative particle swarm optimization (ITCPSO) is proposed. Considering the reaction mechanism of FCC, an 8‐lumps kinetic model was developed. According to the pilot data, nine PSO algorithms and ITCPSO are presented to estimate the parameters. The results demonstrate that better performance of global searching is gained by ITCPSO compared to other PSOs, thus, ITCPSO is expected to be implemented in the optimization of complex kinetic models.

中文翻译：

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改进的粒子群算法优化流体催化裂化动力学模型

流化催化裂化（FCC）动力学模型的特点是尺寸大，非线性，不连续和不可微。当将粒子群优化应用于动力学模型的优化时，很容易过早地陷入局部最优。为了解决这个问题，提出了一种改进的两群合作粒子群优化算法（ITCPSO）。考虑到FCC的反应机理，建立了8团块动力学模型。根据试验数据，提出了九种PSO算法和ITCPSO算法进行参数估计。结果表明，与其他PSO相比，ITCPSO获得了更好的全局搜索性能，因此，有望在复杂动力学模型的优化中实现ITCPSO。