Spectroscopic Inversion Procedures for Smart Sensor Based Process Control

基于智能传感器的过程控制的光谱反演程序

• 批准号: 8714040
• 负责人: Frederick Gouldin
• 金额: $ 37万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1987
• 资助国家: 美国
• 起止时间: 1987-09-01 至 1991-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8714040&HistoricalAwards=false
• 关键词: Spectroscopic; Inversion; Procedures; Smart; Sensor

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.

提出了针对处理器控制应用程序的智能，计算机控制的光学传感器开发实验研究和分析的协调的跨学科计划。 使用多个视线红外吸收测量和层析成像倒置方案，对过程流中温度和气体组成的空间分辨测量进行了。 该研究的目的是在适当的反转方法领域的进步，它们对矢量并行处理的适应性，信噪比的改进以及自适应控制的系统集成。 这种在线自适应控制将允许以最大允许污染物水平的形式对动力植物操作进行实际优化。 由于此优化的调节限制与燃料或燃烧器的选择无关，因此无论将煤的等级提供给工厂，算法都将能够寻找最佳的功绩。 换句话说，该过程并未预定为某种类型的燃烧。这是真正的适应性。 煤炭消费的预期节省将具有很大的经济比例。