反应器网络综合问题一般都是复杂的非线性规划问题,遗传算法作为一种启发式全局优化方法,具有计算简单、功能性强的特点。本文将全局优化遗传算法应用到反应器网络综合中以避免传统的优化方法很难求得其全局最优解的缺点。本文首先分析了几种反应器的替代结构,以及在此基础上建立的通用的全混流反应器(Continuous Stirred Tank Reactor,CSTR)网络结构模型;然后采用全局优化遗传算法以及传统的数学优化工具GAMS(General Algorithm Model System)分别对该模型进行求解。实例研究表明,遗传算法可有效地求解此类反应器网络综合问题,且其计算结果优于传统优化方法的结果。
Among the techniques developed for bilinear data reconciliation problems, the method based on independent flows is well known in terms of both accuracy and efficiency. However, the independent flow method is not effective when reactor units are present in the process. In this paper, an extended independent flow method is proposed for the data reconciliation of the process with chemical reaction. By the new method, the independent flows finding algorithm is adjusted to avoid the difficulties caused by the reactors in the process, and the reaction constraints are introduced into the objective function of data reconciliation. As a result, the new method can be applied to the process with chemical reaction while retaining high solution accuracy. To test the performance, the new method and the most typical Crowe’s projection method are used in the data reconciliation of a typical industrial process. The results show that the new method can effectively accomplish the data reconciliation of the multicomponent process with chemical reaction and gives more accurate estimates than the Crowe’s method.
