首页> 美国政府科技报告 >Intelligent Monitoring System with High Temperature Distributed Fiberoptic Sensor for Power Plant Combustion Processes. Final Technical Report (09/27/2002 to 09/26/2006)
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Intelligent Monitoring System with High Temperature Distributed Fiberoptic Sensor for Power Plant Combustion Processes. Final Technical Report (09/27/2002 to 09/26/2006)

机译:高温分布式光纤传感器智能监控系统在电厂燃烧过程中的应用。最终技术报告(09/27/2002至09/26/2006)

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The objective of the proposed work is to develop an intelligent distributed fiber optical sensor system for real-time monitoring of high temperature in a boiler furnace in power plants. Of particular interest is the estimation of spatial and temporal distributions of high temperatures within a boiler furnace, which will be essential in assessing and controlling the mechanisms that form and remove pollutants at the source, such as NOx. The basic approach in developing the proposed sensor system is three fold: (1) development of high temperature distributed fiber optical sensor capable of measuring temperatures greater than 2000 C degree with spatial resolution of less than 1 cm; (2) development of distributed parameter system (DPS) models to map the three-dimensional (3D) temperature distribution for the furnace; and (3) development of an intelligent monitoring system for real-time monitoring of the 3D boiler temperature distribution. Under Task 1, we have set up a dedicated high power, ultrafast laser system for fabricating infiber gratings in harsh environment optical fibers, successhlly fabricated gratings in single crystal sapphire fibers by the high power laser system, and developed highly sensitive long period gratings (lpg) by electric arc. Under Task 2, relevant mathematical modeling studies of NOx formation in practical combustors have been completed. Studies show that in boiler systems with no swirl, the distributed temperature sensor may provide information sufficient to predict trends of NOx at the boiler exit. Under Task 3, we have investigated a mathematical approach to extrapolation of the temperature distribution within a power plant boiler facility, using a combination of a modified neural network architecture and semigroup theory. Given a set of empirical data with no analytic expression, we first developed an analytic description and then extended that model along a single axis.

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