Multi-objective optimization of spinning process parameters based on nondominated sorting genetic algorithm II

被引：0

作者：

Shao J. ^{[1
]}

Shi X. ^{[1
]}

机构：

[1] School of Management, Xi'an Polytechnic University, Xi'an

来源：

Fangzhi Xuebao/Journal of Textile Research | 2022年 / 43卷 / 01期

关键词：

Carbon emission; Multi-objective optimization; Nondominated sorting genetic algorithm II; Process optimization; Yarn quality;

D O I：

10.13475/j.fzxb.20210805609

中图分类号：

学科分类号：

摘要：

In order to optimize of parameters of the spinning production process and to reduce energy consumption, an nondominated sorting genetic algorithm II (NSGA-II) algorithm-based multi-objective optimization method was proposed. By analyzing the spinning process, the process parameters that significantly affect the quality and energy consumption of spun yarn were identified, and the key quality evaluation indexes for evaluating the quality of spun yarn were extracted. The quality evaluation indexes were transformed into comprehensive quality indexes by combining the gray correlation theory, while the correlation relationship between the process parameters and comprehensive quality indexes and carbon emission is fitted by using the second-order response surface method, leading to the establishment of the multi-objective optimization model for spinning process parameters. The NSGA-II algorithm was used to optimize the model, and the optimal process parameters were obtained. The results demonstrate that the quality evaluation indexes were improved using the optimized process conditions, with a reduction carbon emission by 5.77% on average compared with the original conditions. © 2022, Periodical Agency of Journal of Textile Research. All right reserved.

引用

页码：80 / 88

页数：8

共 19 条

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