连续过程的多目标优化

Multiobjective optimization of continuous process

在模块环境(Aspen Plus)下,建立了基于多目标遗传算法NSGA-Ⅱ求解多目标优化问题的系统结构,并对含循环物流的连续过程废料最小化问题进行求解。在求解过程中遗传算法需要反复调用流程模拟,而流程中循环物流的迭代收敛使优化计算效率较低。为减少流程迭代次数本文提出2个加速策略:一是变收敛精度策略,在优化计算初始阶段,使流程在较低精度下收敛,快速淘汰劣点,随着优化的进行,将流程收敛精度逐步提高,得到高质量的非劣解;二是循环流初值策略,利用已有的计算值,回归决策变量与循环流变量的对应关系,改善循环流初值。实例结果表明,加速策略减少了一半左右的流程迭代次数,效率提高50%,本文提出的求解多目标问题的方法能方便地得到问题的Pareto最优解集,可应用于一般连续化工过程的多目标优化。

A framework based on NSGA-Ⅱ in modular environment （Aspen Plus） is proposed and a waste minimization problem of a continuous process which contains recycle is solved on this platform. MOGA needs to spend lots of computer time to simulate flowsheet, especially when the flowsheet contains recycle. For reducing iteration number of simulation, we propose two strategies ： one is changing tolerance strategy, that at initial stage of optimization the convergence tolerance of recycle is lower in order to get rid of inferior solution quickly, then the tolerance is higher gradually in order to get better non-inferior solution set, the other strategy is improved initial recycle strategy, that the initial value of recycle is calculated by a correlation which is acquiredfrom the initial flowsheet simulation. Results showed that the number of iteration is reduced about a half. The method in this paper can attain the Pareto front of the problem and the framework can be easily used to optimize other continuous processes.