液氮洗工艺的多目标遗传算法优化

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液氮洗工艺的多目标遗传算法优化

更新时间：2025-05-08

- 液氮洗工艺的多目标遗传算法优化
- Issue 5, Pages: 114-118(2011)
- 作者机构：
  
  西安交通大学能源与动力工程学院
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP18
- Online First：10 May 2011，
  
  Published：2011
- 稿件说明：
移动端阅览
[J]. 2011, (5): 114-118.
DOI：

[J]. 2011, (5): 114-118. DOI：

摘要

为了优化大化肥中的液氮洗关键流程以提高能量效率、控制CO2排放

在使用Aspen Plus软件对某低温液氮洗工艺进行模拟分析的基础上

结合多目标遗传算法和二次规划法(MOGA-QP)来解决局部极值和大规模优化问题.根据超结构方法

构建了液氮洗工艺流程总结构

即在原流程中加入了一个减压阀和一个分离器

由此可以增补29.546 kJ/kg冷量

使得液氮洗涤塔出口净化气中的CO体积分数为0.55×10-6.利用MOGA-QP求解了超结构

即在进化过程中用二次规划法来模拟流程表单的各个子模块

在最终收敛的环节用二次规划法求解子问题

从而对遗传算法产生的Pareto解做进一步的精细优化.实际应用显示:在满足工艺指标的前提下

优化后的液氮入口流量减至0.131 2 kmol/s

出口CO体积分数降至0.25×10-6

液氮入口流量减少了1.7%

火用损失减少了15.7%

表明改进流程更佳

MOGA-QP算法有效.

Abstract

A multi-objective genetic algorithm with quadratic programming(MOGA-QP)was proposed to optimize the liquid-nitrogen washing process in large fertilizer plants based on the simulation by using Aspen Plus software. According to the superstructure method

the flowsheet of liquid-nitrogen washing process was constructed by adding a throttle valve and a separator. The proposed algorithm MOGA-QP was employed to solve the superstructure of the liquid-nitrogen washing process. The results of the case study show that the optimization of the liquid-nitrogen washing process reduced the inlet flow rate of liquid-nitrogen and the CO volume fraction at the exit to 0.131 2 kmol/s and 0.25×10

-6

respectively. Moreover

the inlet flow rate of liquid nitrogen and the exergy loss decreased by 1.7% and 15.7%

respectively. It appears that the improved process is much better and the MOGA-QP algorithm is effective.

关键词

Keywords

references

Weifang Yu,K. Hidajat,Ajay K. Ray.Optimization of reactive simulated moving bed and Varicol systems for hydrolysis of methyl acetate[J].Chemical Engineering Journal,2005(1).

张旭,颜立伟,李云,张早校,莫沅,朱建立.合成氨液氮洗深冷净化过程模拟及分析[J].化工进展,2010(S1).

颜立伟,余云松,李云,卢红芳,张早校.吸收法捕集二氧化碳过程的离子浓度软测量[J].化工学报,2010(05).

朱平,冯霄.积累优化理论及换热过程的积累优化[J].西安交通大学学报,2003(05).

张早校,冯霄,郁永章.间歇过程热回收目标的研究[J].西安交通大学学报,2000(08).

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