粒子群优化算法在催化裂化模型参数估计中的应用

参数估计是化工模型工业应用中的重要课题,有相当的难度。针对催化裂化八集总模型的动力学参数估计问题,考察了不同类型优化算法的应用效果,结果表明,粒子群优化算法简单、容易实现,而且可以避免传统方法对初始值的依赖,并进一步提出用结合Levenberg-Marquardt算法的混合粒子群优化算法提高参数估计效果。工业实例表明,用混合粒子群优化算法得到的动力学参数可以保证模型的预测精度。

Parameter estimation of catalytic cracking model using PSO algorithm

The estimation of kinetic parameters is an important topic for chemical process model application.Different optimization algorithms are used to estimate parameters for the eight lumps model of FCC（fluid catalytic cracking）process.It is shown that the particle swarm optimization（PSO）algorithm is simple and can be easily implemented.The PSO algorithm also exhibits a good global optimization performance that avoids the dependence on initial parameters.Furthermore a hybrid particle swarm optimization（HPSO）algorithm combined with Levenberg-Marquardt algorithm is proposed to improve the effect of parameter estimation.By use of real industrial data，the simulation results show that model prediction accuracy is ensured by HPSO method.