摘要
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。
Reactor networks synthesis is generally a nonlinear programming problem which is difficult to obtain the global optimal results. Genetic algorithm can find global optimal results more easily than traditional methods on the problem. In this paper, the substituted structures for several kinds of reactors and the general reactor networks model based on CSTR were studied. Then genetic algorithm and GAMS were used respectively to solve the CSTR networks model. The results of a typical example show that genetic algorithm is better than GAMS in terms of optimum value. Also compared with the literature the proposed approaches can give better results. Thus the genetic algorithm is an effective method for solving the reactor networks model based on CSTR.
出处
《计算机与应用化学》
CAS
CSCD
北大核心
2005年第3期173-177,共5页
Computers and Applied Chemistry
基金
国家自然科学基金(20276032)
山东省自然科学基金(Y2003B02)
