基于遗传算法的费托合成反应动力学模型参数优化

详细动力学模型是费托合成反应技术从实验室走向工业化过程中最关键的基础研究项目之一。目前对动力学模型的参数估算仍然停留在传统的LM算法上，LM算法属于无约束方法，在计算中容易因参数越界而使计算失败，计算结果强烈依赖于初值，且容易陷于局部最优，运用遗传算法来解决费托合成反应详细动力学模型的参数优化问题，是一种全新的尝试，通过系统的实验我们获得了比较满意的参数估算结果，证明该算法用于解决动力学模型参数优化是非常有效的。

OPTIMIZATION OF PARAMETERS OF FTS KINETIC MODEL BY GENETIC ALGORITHM

Detailed kinetic model is one of the most i mportant basic research items for Fischer-Tropsch synthesis (FTS). LM(Levenber g-Marquardt) algorithm still plays a leading role in estimating parameters of t he kinetic model. As an unlimited algorithm, LM often makes an inaccurate concl usion because of parameters exceeding limit. Its computation deeply depends on the initial point, and easily falls into non-global optima. It is a new attemp t to apply Genetic Algorithm to the solutions of optimization problems of FTS pa rameters. After a number of systemic tests, comparatively satisfying results of parameters-estimating and a lot of precious experience on GA have been obtaine d.