基于改进差分进化算法的超临界水氧化动力学参数估计

Kinetic Parameter Estimation of Oxidation in Supercritical Water Based on Modified Differential Evolution

为了准确地估计反应动力学参数,提出一种改进差分进化算法(MDE),能根据算法搜索进展情况而自适应地确定变异率,使算法在初期保持个体的多样性,避免早熟;在后期逐步降低变异率,保留优良信息,避免最优解遭到破坏,增加搜索到全局最优值的概率。与传统的差分进化算法(DE)相比较,MDE算法的离线性能和在线性能都有较大的改进,搜索到全局最优解的概率获得较大提高,对算法参数的敏感性低。将MDE算法应用于2-氯苯酚在超临界水中氧化反应动力学参数的估算,获得模型的拟合相对误差绝对值之和比文献报道值降低了14.2%。

In order to obtain global optimum of kinetic parameters, a novel modified differential evolution （MDE） algorithm containing the adaptive mutation operator, in which the mutation probability is determined according to the evolved generations, is proposed. The adaptive mutation operator keeps the individuals＇ diversity in the population at the initial generations to overcome the premature and reduces the mutation probability gradually during the evolutionary process to preserve the excellent individuals and enhance the probability of obtaining the global optimum. To compare the performances of MDE with those of the traditional differential evolution （DE）, MDE and DE are applied to searching the global optimum of analytical function. The results demonstrate that MDE＇s on-line and off-line performances are all superior to those of DE, the probability of obtaining the global optimum is larger than that of DE, and the parameter sensitivity degree of MDE is lower than that of DE. Further more, MDE is applied to estimating the kinetic parameters of the 2-chloropheol oxidation in supercritical water. Satisfactory results show the absolute value of model＇s analog relative total error decreases by 14.2% compared with the reported literature data.