...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Industrial Electronics >SA-ANN-Based Slag Carry-Over Detection Method and the Embedded WME Platform
【24h】

SA-ANN-Based Slag Carry-Over Detection Method and the Embedded WME Platform

机译：基于SA-ANN的炉渣残留检测方法及嵌入式WME平台

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Slag carry-over detection technology (SCDT) is of important significance for steel continuous casting production (CCP), but has the problems with manufacture cost, service life, installation, and maintenance. Aiming at the problems, this paper brings forward a novel vibration style SCDT realization method based on simulated annealing artificial neural network (SA-ANN). According to ladle pouring process, the vibration signal of steel stream is regarded as the target signal for SCDT. Then, the time point of slag carry-over can be obtained in light of the vibration amplitude difference of pure molten steel and steel slag. Based on the fluid flow similarity principles, an embedded water model experiment (WME) platform is established. The WME platform can simulate the physical process of ladle pouring, reduce the system debugging time under formidable CCP field conditions, and improve the industrial suitability of SCDT. Using an improved SA-ANN algorithm, the status of steel stream is identified to realize automatic control for ladle pouring. WME simulated test results show that the slag detection accuracy (SDA) of this method can reach more 99%. CCP industrial field experiment proves that this method requires low cost and little rebuilding for the current CCP devices, and the practical SDA can reach more 96%.
机译：炉渣残留检测技术（SCDT）对于钢连铸生产（CCP）具有重要意义，但存在制造成本，使用寿命，安装和维护方面的问题。针对这些问题，提出了一种基于模拟退火人工神经网络（SA-ANN）的振动式SCDT实现方法。根据钢包浇注过程，将钢流的振动信号作为SCDT的目标信号。然后，可以根据纯钢水和钢渣的振动幅度差来获得炉渣残留的时间点。基于流体流动相似性原理，建立了嵌入式水模型实验（WME）平台。 WME平台可以模拟钢包浇注的物理过程，减少在恶劣的CCP现场条件下的系统调试时间，并提高SCDT的工业适用性。利用改进的SA-ANN算法，识别钢流状态，实现钢包浇注的自动控制。 WME模拟测试结果表明，该方法的渣检测精度（SDA）可以达到99％以上。 CCP工业现场实验证明，该方法对目前的CCP器件要求成本低廉，几乎不需要重建，实际SDA可以达到96％以上。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号