面向反应再生过程的量子粒子群多目标优化

白竣仁; 易军; 李倩; 吴凌; 陈雪梅

doi:10.11949/j.issn.0438-1157.20181361

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面向反应再生过程的量子粒子群多目标优化

过程系统工程 | 更新时间：2026-07-24

- 面向反应再生过程的量子粒子群多目标优化
- Multi-objective optimization of QPSO for thereaction-regeneration process
- 化工学报 2019年70卷第2期页码：750-756
- 作者机构：
  
  1.重庆科技学院电气工程学院，重庆 401331
  2.重庆邮电大学移通学院数理教学部，重庆 401520
- 作者简介：
  
  白竣仁(1994—)，男，硕士研究生，bjr25793@qq.com
  易军(1973—)，男，教授，博士，laoyifrcq@163.com
- 基金信息：
  
  重庆市基础科学与前沿技术研究项目(cstc2015jcyjBX0099);重庆科技学院研究生科技创新计划项目基金(YKJCX1620411;YKJCX1720406)
- DOI：10.11949/j.issn.0438-1157.20181361
  中图分类号： TP 18
- 收稿：2018-11-18，
  
  修回：2018-12-08，
  
  网络首发：2020-06-16，
  
  纸质出版：2019-02-05
- 稿件说明：
移动端阅览
白竣仁, 易军, 李倩, 吴凌, 陈雪梅. 面向反应再生过程的量子粒子群多目标优化[J]. 化工学报, 2019, 70(2): 750-756

BAI Junren, YI Jun, LI Qian, WU Ling, CHEN Xuemei. Multi-objective optimization of QPSO for thereaction-regeneration process[J]. CIESC Journal, 2019, 70(2): 750-756
白竣仁, 易军, 李倩, 吴凌, 陈雪梅. 面向反应再生过程的量子粒子群多目标优化[J]. 化工学报, 2019, 70(2): 750-756 DOI： 10.11949/j.issn.0438-1157.20181361.

BAI Junren, YI Jun, LI Qian, WU Ling, CHEN Xuemei. Multi-objective optimization of QPSO for thereaction-regeneration process[J]. CIESC Journal, 2019, 70(2): 750-756 DOI： 10.11949/j.issn.0438-1157.20181361.

摘要

针对催化裂化反应再生过程难以有效解决提升效率、降低损耗、减少排放的多目标优化问题，利用改进的多目标量子粒子群算法进行求解。建立轻油收率、焦炭产率和硫化物排量的多目标优化模型；引入拥挤熵排序更新外部档案，精确估计非支配解集分布性；构造自适应因子以动态调整吸引子，平衡算法的收敛性和多样性；再引入高斯变异进行分段式扰动，增强算法的局部搜索精度，最后求解该优化模型。对某厂催化裂化进行实验，得到轻质油吸收率76.22%，焦炭产率5.72%和硫化物排放量626 mg/m

的结果，均优于其他比较算法，表明改进后的算法可以快速、准确地获得分布均匀的 Pareto 最优解，能有效解决反应再生过程多目标优化问题。

Abstract

It is difficult to solve the multi-objective optimization problem of improving efficiency

reducing loss and reducing emissions for the catalytic cracking reaction regeneration process. The improved multi-objective quantum-based particle swarm optimization-crowding entropy sorting (MQPSO-CES) is used to solve the problem. A multi-objective optimization model is established to maximize the light oil absorption rate and synchronously minimize the coke yield and sulfide emissions. Particularly

crowding entropy sorting is used to update the archive

which accurately estimates the distribution of the non-dominated solutions. Afterwards

an adaptive factor is introduced to self-adaptively and dynamically adjust the construction of the attractor

which can balance the convergence and diversity of the proposed algorithm. In addition

with the application of a piecewise Gauss mutation operator

the precision of the local search can be enhanced. Finally

the multi-objective model is resolved with the novel algorithm. The results indicate that the improved algorithm can outperform other algorithms with convergent and well-distributed approximate Pareto fronts when dealing with ZDT3-4 and DTLZ1-2 benchmark problems. In addition

the proposed algorithm can obtain 76.22% of light oil absorption rate

5.72% of coke yield and 626 mg/m

of sulfide emissions in the reaction and generation process

illustrate its superiority compared with other algorithms.

关键词

Keywords

references

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