期刊
ISIJ INTERNATIONAL
卷 60, 期 6, 页码 1225-1236出版社
IRON STEEL INST JAPAN KEIDANREN KAIKAN
DOI: 10.2355/isijinternational.ISIJINT-2019-570
关键词
continuous annealing; time series prediction; dynamic robust operation optimization; multi objective evolutionary algorithm
资金
- National Key Research and Development Program of China [2018YFB1700404]
- Fund for the National Natural Science Foundation of China [61573086]
- Major Program of National Natural Science Foundation of China [71790614]
- Fund for Innovative Research Groups of the National Natural Science Foundation of China [71621061]
- Major International Joint Research Project of the National Natural Science Foundation of China [71520107004]
- 111 Project [B16009]
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据