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首页> 外文期刊>ISIJ international >A Data-Driven Multiobjective Dynamic Robust Modeling and Operation Optimization for Continuous Annealing Production Process
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A Data-Driven Multiobjective Dynamic Robust Modeling and Operation Optimization for Continuous Annealing Production Process

机译：用于连续退火生产过程的数据驱动多目标动态鲁棒建模与运行优化

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摘要

There are many dynamic disturbances during the continuous annealing production line (CAPL) in iron and steel enterprise. Traditional robust operation optimization considers only the maximum disturbance range in previous production and overrides the dynamic changes of these disturbances, which often results in high production cost and low product quality. Therefore, this paper proposes a novel multiobjective dynamic robust optimization (MODRO) modeling method by further taking into account the dynamic changes of these disturbances and adopting a time series prediction model based on a least square support vector regression (LSSVR) to predict the range of disturbances in next time slot. The main feature of the model is that the robustness can be dynamically adjusted according to the disturbance range predicted by the LSSVR. To solve this model, an improved NSGA-Ⅱ algorithm is developed based on a new crowding metric. Numerical results based on actual production process data illustrate that the proposed MODRO modeling method is obviously superior to traditional static robust operation optimization, and that it can significantly improve the strip quality and the capacity utilization of the CAPL, and reduce the total energy consumption.
机译：钢铁企业连续退火生产线（CAPL）期间存在许多动态障碍。传统的鲁棒操作优化仅考虑以前的生产中的最大干扰范围，并覆盖这些干扰的动态变化，这导致生产成本高，产品质量低。因此，本文通过进一步考虑了这些干扰的动态变化，并采用基于最小二乘支持向量回归（LSSVR）来预测范围来提出一种新的多目标动态鲁棒优化（MODORS）建模方法。下次插槽中的干扰。模型的主要特征是可以根据LSSVR预测的干扰范围动态调整鲁棒性。为了解决该模型，基于新的拥挤度量开发了一种改进的NSGA-Ⅱ算法。基于实际生产过程数据的数值结果表明，所提出的MODOR模型方法明显优于传统的静态稳健运行优化，并且它可以显着提高CAPL的条带质量和能力利用率，并降低总能耗。

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  • 来源
    《ISIJ international》 |2020年第6期|1225-1236|共12页
  • 作者

    Yao WANG; Xianpeng WANG; Zhiming DONG; Zan WANG;

  • 作者单位

    Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Ministry of Education Northeastern University Shenyang 110819 China;

    Liaoning Engineering Laboratory of Operation Analytics and Optimization for Smart Industry Northeastern University Shenyang 110819 China;

    Liaoning Engineering Laboratory of Operation Analytics and Optimization for Smart Industry Northeastern University Shenyang 110819 China;

    Liaoning Key Laboratory of Manufacturing System and Logistics Institute of Industrial & Systems Engineering Northeastern University Shenyang 110819 China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    continuous annealing; time series prediction; dynamic robust operation optimization; multi-objective evolutionary algorithm;

    机译：连续退火;时间序列预测;动态鲁棒操作优化;多目标进化算法;

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