INTELLIGENT MONITORING SYSTEM WITH HIGH TEMPERATURE DISTRIBUTED FIBEROPTIC SENSOR FOR POWER PLANT COMBUSTION PROCESSES

摘要

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, improvement was made on the performance of in-fiber grating fabricated in single crystal sapphire fibers, test was performed on the grating performance of single crystal sapphire fiber with new fabrication methods, and the fabricated grating was applied to high temperature sensor. Under Task 2, models obtained from 3-D modeling of the Demonstration Boiler were used to study relationships between temperature and NOx, as the multi-dimensionality of such systems are most comparable with real-life boiler systems. 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 investigate 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. The 3D temperature data is furnished by the Penn State Energy Institute using FLUENT. Given a set of empirical data with no analytic expression, we first develop an analytic description and then extend that model along a single axis.