...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Combustion Science and Technology >Prediction of NOx Emissions From a Biomass Fired Combustion Process Based on Flame Radical Imaging and Deep Learning Techniques
【24h】

Prediction of NOx Emissions From a Biomass Fired Combustion Process Based on Flame Radical Imaging and Deep Learning Techniques

机译：基于火焰自由基成像和深度学习技术的生物质燃烧过程中NOx排放的预测

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

This article presents a methodology for predicting NOx emissions from a biomass combustion process through flame radical imaging and deep learning (DL). The dataset was established experimentally from flame radical images captured on a biomass-gas fired test rig. Morphological component analysis is undertaken to improve the quality of the dataset, and the region-of-interest extraction is introduced to extract the flame radical part and rescale the image size. The developed DL-based prediction model contains three successive stages for implementing the feature extraction, feature fusion, and emission prediction. The fine-tuning based on the prediction is introduced to adjust the process of the feature fusion. The effects of the feature fusion and fine-tuning are discussed in detail. A comparison between various image- and machine-learning-based prediction models show that the proposed DL prediction model outperforms other models in terms of root mean square error criteria. The predicted NOx emissions are in good agreement with the measurement results.
机译：本文介绍了一种通过火焰自由基成像和深度学习（DL）预测生物质燃烧过程中NOx排放的方法。该数据集是根据在生物质气体燃烧试验台上捕获的火焰自由基图像实验建立的。进行形态成分分析以提高数据集的质量，并引入感兴趣区域提取以提取火焰自由基部分并重新缩放图像大小。开发的基于DL的预测模型包含三个连续的阶段，用于实现特征提取，特征融合和发射预测。引入了基于预测的微调来调整特征融合的过程。详细讨论了特征融合和微调的效果。各种基于图像和机器学习的预测模型之间的比较表明，就均方根误差标准而言，所提出的DL预测模型优于其他模型。预测的NOx排放量与测量结果非常吻合。

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