基于组合遗传算法的反应动力学模型参数估计

摘要

Through establishing an appropriate fitness function, the parameter estimation problem for residue hydrofining reaction kinetics model was formulated as a multi-dimensional functional optimization problem, which can be solved by Composite Genetic Algorithm ( CGA). Chaotic sequences design method was introduced to construct the initialization population that was scattered uniformly over the entirely search space in order to maintain the diversity. The CGA randomly combined several effective crossover strategies with some suitable mutation strategies at each generation to create new offspring individuals. The simulation results on four benchmark problems demonstrate the effectiveness and robustness of the proposed algorithm. Taking a catalytic cracking unit in oil refinery as an example, a numerical application of the parameter estimation for residue hydrofining reaction kinetics model was solved. Satisfactory results were obtained.%通过构造一个适当的适应度函数,将渣油加氢精制反应动力学模型的参数估计问题转化为一个多维优化问题,然后提出一种组合遗传算法来求解该优化问题.该算法利用混沌序列初始化种群以保证其均匀分布在搜索空间中.在每次迭代过程中随机组合不同的交叉策略和变异以产生若干个新的子代个体.对四个标准数值优化问题进行了仿真实验,仿真结果表明了组合遗传算法的有效性.以石油炼制工业中典型装置催化裂化为例,对渣油加氢精制反应动力学模型的参数进行了优化,获得了满意的结果.