基于非支配排序遗传算法的细纱工艺参数优化

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

为了实现细纱生产工艺参数优化,解决细纱生产过程中高能耗问题,提出了一种基于非支配排序遗传算法的细纱工艺参数多目标优化方法。通过分析细纱生产工艺流程,确定了影响细纱成纱质量与能耗的工艺参数,提取了评价成纱质量的关键质量评价指标,结合灰色关联理论将质量评价指标转化为综合质量指标,利用二阶响应曲面法拟合工艺参数与综合质量指标、碳排放量之间的关联关系,构建了细纱工艺参数多目标优化模型,并采用非支配排序遗传算法对模型进行寻优,得到了最佳工艺参数。结果证明:在优化后的工艺条件下,细纱生产过程中各项质量评价指标值较初始值均得到改善,碳排放量平均减少5.77%。

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.