| 引用本文: | 严爱军,柴天佑,王 普.基于参量预报的磁选管回收率智能优化控制[J].控制理论与应用,2008,25(5):908~912.[点击复制] |
| YAN Ai-jun,CHAI Tian-you,WANG Pu.Intelligently optimizing control of magnetic-tube-recovery-rate(MTRR) based on variable prediction[J].Control Theory and Technology,2008,25(5):908~912.[点击复制] |
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| 基于参量预报的磁选管回收率智能优化控制 |
| Intelligently optimizing control of magnetic-tube-recovery-rate(MTRR) based on variable prediction |
| 摘要点击 1555 全文点击 1239 投稿时间：2006-12-25 修订日期：2007-06-22 |
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| DOI编号 10.7641/j.issn.1000-8152.2008.5.022 |
| 2008,25(5):908-912 |
| 中文关键词 竖炉焙烧 磁选管回收率(MTRR) 预报 优化设定 智能控制 |
| 英文关键词 shaft furnace roasting process magnetic tube recovery rate(MTRR) prediction optimizing setting intelligent control |
| 基金项目 国家重点基础研究发展规划资助项目(2002CB312201); 北京工业大学 博士科研启动基金资助项目(52002017200701). |
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| 中文摘要 |
| 竖炉焙烧过程的关键工艺指标磁选管回收率难以实时测量, 因而实现优化控制很困难. 将优化设定、参量预报与回路控制技术相结合, 提出一种磁选管回收率的智能优化控制方法. 基于案例推理的优化设定模型根据工况的变化和磁选管回收率的实时预报值给出基础控制回路的设定值, 并通过先进的控制方法实现回路的稳定控制.该方法应用于竖炉焙烧过程的生产实际, 使磁选管回收率的实际值保持在其目标值范围内, 取得显著应用成效. |
| 英文摘要 |
| The shaft furnace roasting is a synthetic complex process, its key technical parameter, magnetic-tuberecovery-rate(MTRR), is hard to be measured online, so the optimizing control is very difficult. An intelligently optimizing control approach is developed by combining the optimal setting, variable prediction and loop control. The optimal setting model using the case-based reasoning provides the setpoints for the basic control loops according to the real time prediction of MTRR and operating conditions. Therefore it achieves the stabilization control of basic control loops by using advanced control technologies. The proposed approach has been applied to the shaft furnace roasting process. As a result, the MTRR can be kept within optimal ranges, and obvious benefit is achieved. |
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