Spectroscopic Inversion Procedures for Smart Sensor Based Process Control

一个协调的、跨学科的实验研究项目 并分析了一种智能的、计算机控制的 提出了用于处理器控制应用的光学传感器。 温度和气体的空间分辨测量 在工艺流程中，使用多条生产线， 可见光红外吸收测量和层析反演 阴谋 研究的目的是在适当的领域取得进展， 反演方法及其对矢量并行的适应 处理、信噪比改善和系统集成 用于自适应控制。 这种在线自适应控制将允许实际的 自动优化电厂运行， 最大允许污染物水平。 由于管制 对这种优化的约束独立于 燃料或燃烧器，该算法将能够搜索一个 最佳品质因数，无论煤的等级如何 送到工厂。 换句话说，这个过程不是 它是一种燃烧的方式;它是真正的。 适应性 预期的煤炭消费节省将 大的经济比例。

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.