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Neural methods of interpretation of data obtained from optical sensor for flame monitoring

机译:从光学传感器获得火焰监测中获得的数据的神经方法

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摘要

Burner systems and their control are getting more and more sophisticated and there is a growing need to obtain information about the course of combustion process in individual flames. Optical sensors offer the benefit of being selective, rapid and able to gather data from extremely hostile environments (e.g. the combustion zone of pulverised coal burners or gas turbines). Passive optical sensors offer the further advantage of simplicity, which make them attractive candidates. With the rapidly growing capability of these technologies for sensor hardware, there is an increased interest and need to develop data interpretation strategies that will allow optical flame emission data to be converted into meaningful combustor state information. The article describes various approaches to apply artificial neural network approaches to estimate parameters of combustion. One is acquiring information about emission of nitrogen oxides and carbon monoxide from fiberoptic systems for flame monitoring, developed in Department of Electronics of Lublin University of Technology and another is identification of flames in gas burners.
机译:燃烧器系统及其控制越来越复杂,并且越来越需要获取有关各个火焰中燃烧过程课程的信息。光学传感器提供了选择性,快速和能够从极其敌意的环境中收集数据的益处(例如,粉煤燃烧器或燃气轮机的燃烧区)。被动光学传感器提供了简单的进一步优势,使其成为有吸引力的候选人。随着这些技术对于传感器硬件的快速增长能力,有兴趣增加并且需要开发将允许光火焰发射数据转换为有意义的燃烧器状态信息的数据解释策略。本文介绍了应用人工神经网络方法来估计燃烧参数的各种方法。一个是获取有关来自燃料监测的火纤维系统的氮氧化物和一氧化碳排放的信息,在卢布林理工大学电子设备系中发育,另一个是在燃气燃烧器中的识别。

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