| 引用本文: | 黄大建,张文安,桂卫华.基于局部多尺度滤波的铜箔电解过程高可靠性节能优化[J].控制理论与应用,2025,42(11):2207~2220.[点击复制] |
| HUANG Da-jian,ZHANG Wen-an,GUI Wei-hua.Energy-saving optimization of copper foil electrolysis process with high reliability based on partial multi-scale filtering[J].Control Theory & Applications,2025,42(11):2207~2220.[点击复制] |
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| 基于局部多尺度滤波的铜箔电解过程高可靠性节能优化 |
| Energy-saving optimization of copper foil electrolysis process with high reliability based on partial multi-scale filtering |
| 摘要点击 2358 全文点击 137 投稿时间：2024-11-11 修订日期：2025-07-18 |
| 查看全文 查看/发表评论 下载PDF阅读器 HTML |
| DOI编号 10.7641/CTA.2025.40580 |
| 2025,42(11):2207-2220 |
| 中文关键词 铜箔 电解过程 节能优化 局部多尺度滤波 过程可靠性增强 |
| 英文关键词 electrolytic copper foil electrolytic process energy-saving optimization multi-scale filtering process reliability enhancement |
| 基金项目 国家资助博士后研究人员计划B档项目(GZB20230869),湖南省自然科学基金项目(2024JJ6528),中国博士后面上基金项目(2024M763700)资助. |
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| 中文摘要 |
| 铜箔电解过程中的能耗建模精度不足会降低节能优化的可靠性,给现有的节能优化方法带来了严峻挑战.
针对这一问题,在难以进一步提升能耗模型精度的背景下,本文提出了一种基于局部多尺度滤波的铜箔电解过程节
能优化方法,并对其可靠性进行了系统分析.首先,针对电解过程在工业应用中的能耗建模误差,通过循环–判断结
构生成不同精度的模型,分析建模精度对能耗优化的影响机制.其次,基于现有回归模型的拟合优度评估指标,设计
了可靠性分析方法,实现了对节能优化可靠性的定量评估.再次,提出基于局部多尺度滤波的能耗优化改进方法,
在能耗优化模型中引入多尺度的局部均值滤波,减小预测误差对智能仿生优化算法的误导影响,从而提高因建模精
度不足导致的可靠性问题,并进行相关理论推导.最后,基于某企业的铜箔电解过程工业实验验证,所提方法将能
耗优化的绝对误差从4.29%降至1.53%,表明该方法在能耗模型低精度条件下依然能够显著提升智能仿生优化算法
的可靠性. |
| 英文摘要 |
| The insufficient accuracy of energy consumption modeling in the copper foil electrolysis process compromis
es the reliability of energy-saving optimization, posing significant challenges to existing optimization methods. To address
this issue, particularly given the difficulty of further enhancing modeling accuracy, this study proposes a novel energy
saving optimization approach for copper foil electrolysis based on local multi-scale filtering and systematically analyzes
its reliability. Firstly, to investigate the impact of modeling accuracy on energy consumption optimization, models with
varying levels of accuracy are generated through a loop-judgment structure, which enables analysis of the underlying influ
ence mechanisms. Secondly, a reliability analysis method is developed based on the goodness-of-fit metrics from existing
regression models, enabling a quantitative assessment of the reliability of energy-saving optimization. Additionally, an
improved strategy employing local multi-scale filtering is introduced to mitigate the influence of prediction errors on the
intelligent bionic optimization algorithm, thereby addressing reliability issues arising from modeling accuracy limitations;
relevant theoretical derivations are also provided. Finally, an industrial experiment in a copper foil electrolysis enterprise
demonstrates that the proposed method reduces the absolute error of energy consumption optimization from 4.29% to
1.53%, indicating that this approach can substantially enhance the reliability of intelligent bionic optimization algorithms
even with lower model accuracy. |
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