| 引用本文: | 严爱军,黄晓倩.竖炉焙烧过程的智能设定模型[J].控制理论与应用,2015,32(5):709~715.[点击复制] |
| YAN Ai-jun,HUANG Xiao-qian.Intelligent setting model for shaft furnace roasting process[J].Control Theory and Technology,2015,32(5):709~715.[点击复制] |
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| 竖炉焙烧过程的智能设定模型 |
| Intelligent setting model for shaft furnace roasting process |
| 摘要点击 1672 全文点击 1029 投稿时间：2013-05-17 修订日期：2505-02-27 |
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| DOI编号 10.7641/CTA.2015.13075 |
| 2015,32(5):709-715 |
| 中文关键词 竖炉 智能设定 多目标评价 案例推理 群决策 |
| 英文关键词 shaft furnace intelligent setting multi-objective evaluation (MOE) case-based reasoning (CBR) group decision-making (GDM) |
| 基金项目 国家自然科学基金项目(61374143), 北京市自然科学基金项目(4152010)资助. |
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| 中文摘要 |
| 为了改善多目标评价案例推理设定模型在竖炉焙烧过程控制中的性能, 运用注水原理分配过程变量的权重和群决策修正方法对多目标评价案例推理设定方法进行改进, 得到一种新的智能设定模型. 首先引入注水原理构造Lagrange函数对过程变量的权重进行优化分配, 再通过案例检索和案例重用得到设定值的建议解, 并根据多目标评价模型预测建议解对生产指标的影响效果, 最后, 对不合理的设定值进行群决策修正. 将得到的设定模型应用于竖炉焙烧过程控制中, 通过实验测试和对比应用说明了本文方法优于其他方法, 能够有效提高多目标评价案例推理设定模型的控制性能. |
| 英文摘要 |
| To improve the control performance of the setting model with multi-objective evaluation and case-based reasoning (MOE & CBR) for shaft furnace roasting process, we make use of the water-filling based weight allocation (WFA) to allocate weights for process variables and employ the group decision-marking revision (GDMR) to develop a new intelligent setting method. First, a Lagrange function is constructed to optimize the allocation of the weights of the process variables via WFA. Subsequently, the suggested solutions of set-points are obtained through case retrieval and case reuse. These suggested solutions are used to evaluate the production performance indices based on the multi-objective evaluation (MOE) model. Those unreasonable set-points from MOE model are revised by GDMR. The proposed method has been applied to the shaft furnace roasting process. The application results indicate that the proposed method is superior to other methods and it can significantly improve the control performance of MOE & CBR model. |