Spectroscopic Inversion Procedures for Smart Sensor Based Process Control

提出了一项跨学科的实验研究和分析计划，以开发一种用于处理器控制应用的智能计算机控制光学传感器。利用多视距红外吸收测量和层析反演方案，对工艺流中的温度和气体成分进行了空间分辨测量。研究方向包括合适的反演方法、矢量并行处理的适应性、自适应控制的信噪比改善和系统集成等。这种在线自适应控制将允许电厂运行的实际自动优化符合最大允许的污染物水平。由于这种优化的监管约束与燃料或燃烧器的选择无关，因此该算法将能够搜索最优值，而不管输送到电厂的煤的等级如何。换句话说，这个过程不是专门针对某种类型的燃烧；这是真正的适应性。预期的煤炭消费节省将具有很大的经济意义。

A coordinated, interdisciplinary program of experimental research and analysis for the development of a smart, computer controlled optical sensor for processor-controlled applications is proposed. Spatially resolved measurements of temperature and gas composition in process streams are made, using multiple line-of- sight infrared absorption measurements and tomographic inversion schemes. The research aims at progress in the area of suitable inversion methods, their adaptation to vector parallel processing, signal-to-noise improvement and system integration for adaptive control. Such an on-line adaptive control would allow for the practical automatic optimization of powerplant operation in conformity with maximum allowed pollutant levels. Since the regulatory constraints on this optimization are independent of the choice of fuel or combustor, the algorithm will be able to search for an optimum figure of merit, regardless of the grade of coal delivered to the plant. In other words, the process is not prededicated to a certain type of combustion; it is truly adaptive. The expected savings in coal consumption would have large economic proportions.