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基于泡沫网捕食行为的灰狼优化算法在流化催化裂化装置主分馏塔建模中的应用。

Gray wolf optimizer with bubble-net predation for modeling fluidized catalytic cracking unit main fractionator.

机构信息

School of Information and Control Engineering, Liaoning Petrochemical University, Fushun, 113000, PR China.

State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, 310027, PR China.

出版信息

Sci Rep. 2022 May 9;12(1):7548. doi: 10.1038/s41598-022-10496-2.

DOI:10.1038/s41598-022-10496-2
PMID:35534491
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9085762/
Abstract

Fluidized catalytic cracking unit (FCCU) main fractionator is a complex system with multivariable, nonlinear and uncertainty. Its modeling is a hard nut to crack. Ordinary modeling methods are difficult to estimate its dynamic characteristics accurately. In this work, the gray wolf optimizer with bubble-net predation (GWO_BP) is proposed for solving this complex optimization problem. GWO_BP can effectively balance the detectability and exploitability to find the optimal value faster, and improve the accuracy. The head wolf has the best fitness value in GWO. GWO_BP uses the spiral bubble predation method of whale to replace the surrounding hunting scheme of the head wolf, which enhances the global search ability and speeds up the convergence speed. And Lévy flight is applied to improve the wolf search strategy to update the positions of wolfpack for overcoming the disadvantage of easily falling into local optimum. The experiments of the basic GWO, the particle swarm optimization (PSO) and the GWO_BP are carried out with 12 typical test functions. The experimental results show that GWO_BP has the best optimization accuracy. Then, the GWO_BP is used to solve the parameter estimation problem of FCCU main fractionator model. The simulation results show that the FCCU main fractionator model established by the proposed modeling method can accurately reflect the dynamic characteristics of the real world.

摘要

流化催化裂化装置(FCCU)主分馏塔是一个具有多变量、非线性和不确定性的复杂系统。其建模是一个难题。普通的建模方法很难准确估计其动态特性。在这项工作中,提出了带泡泡网捕食的灰狼优化器(GWO_BP)来解决这个复杂的优化问题。GWO_BP 可以有效地平衡探测性和利用性,以更快地找到最优值,并提高准确性。在 GWO 中,头狼具有最佳的适应度值。GWO_BP 使用鲸鱼的螺旋泡泡捕食方法来替代头狼的周围狩猎方案,从而增强了全局搜索能力并加快了收敛速度。并应用 Lévy 飞行来改进狼的搜索策略,以更新狼群的位置,从而克服容易陷入局部最优的缺点。对基本 GWO、粒子群优化(PSO)和 GWO_BP 进行了 12 个典型测试函数的实验。实验结果表明,GWO_BP 具有最佳的优化精度。然后,将 GWO_BP 用于解决 FCCU 主分馏塔模型的参数估计问题。仿真结果表明,所提出的建模方法建立的 FCCU 主分馏塔模型能够准确地反映真实世界的动态特性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d9b9/9085762/e5a33d39e937/41598_2022_10496_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d9b9/9085762/3c87daf2c7c5/41598_2022_10496_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d9b9/9085762/64fad26f869f/41598_2022_10496_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d9b9/9085762/7f10374f1741/41598_2022_10496_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d9b9/9085762/e5a33d39e937/41598_2022_10496_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d9b9/9085762/3c87daf2c7c5/41598_2022_10496_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d9b9/9085762/64fad26f869f/41598_2022_10496_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d9b9/9085762/7f10374f1741/41598_2022_10496_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d9b9/9085762/e5a33d39e937/41598_2022_10496_Fig4_HTML.jpg

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本文引用的文献

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