SBIR Phase I: Adaptive Control of Chemical Processes Based on Fourier Transform Infrared (FT-IR) Spectroscopy and Artificial Neural Networks

SBIR 第一阶段：基于傅里叶变换红外 (FT-IR) 光谱和人工神经网络的化学过程自适应控制

• 批准号: 9761057
• 负责人: Michael Serio
• 金额: $ 9.98万
• 依托单位: Advanced Fuel Research, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-01-01 至 1998-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9761057&HistoricalAwards=false
• 关键词: SBIR; Phase; Adaptive; Control; Chemical

*** 9761057 Serio This Small Business Innovation Research Phase I project will demonstrate the feasibility of using Fourier Transform Infrared (FT-IR) spectroscopy measurements and artificial neural networks (ANNS) for adaptive control of chemical processes. Three recent events together provide an opportunity to develop a new generation of process monitoring and control systems: 1) the availability of low cost, high speed computation with personal computers; 2) the availability of user friendly software involving artificial intelligence techniques such as neural networks; 3) the availability of rugged, compact FT-IR spectrometers. The capabilities of FT-IR spectrometers to monitor multiple components of a gas phase process stream have been demonstrated by Advanced Fuel Research, .Inc. (AFR) and other researchers in numerous field demonstrations. However, the marriage of this technology with adaptive process control software such as ANNs to produce a superior process control algorithm remains to be proven. The objective of the Phase I program is to demonstrate the proof-of-concept of this approach for a model chemical process.. The Phase II program will concentrate on refinement and generalization of the methodology so that it will work in a full-scale plant as part of a distributed control system. The successful completion of this program would result in process control methodology for maximizing conversion, selectivity and/or environmental acceptability of many common chemical processes which involve gas phase intermediates and products. This technology would provide both economic and environmental benefits to the nation, and potential installations exist at thousands of sites in the U.S. and abroad. ***

*9761057系列这个小型企业创新研究第一阶段项目将展示使用傅里叶变换红外(FT-IR)光谱测量和人工神经网络(ANN)对化学过程进行自适应控制的可行性。最近发生的三件事一起为开发新一代过程监测和控制系统提供了机会：1)低成本、用个人计算机进行高速计算的可用性；2)涉及神经网络等人工智能技术的用户友好软件的可用性；3)坚固、紧凑的FT-IR光谱仪的可用性。先进燃料研究公司已经证明了FT-IR光谱仪能够监测气相过程流中的多个组分。(AFR)和其他研究人员进行了大量的实地演示。然而，这项技术与自适应过程控制软件(如ANN)的结合以产生更好的过程控制算法仍有待证明。第一阶段计划的目标是为一个模型化学过程演示这种方法的概念验证。第二阶段计划将集中于方法的改进和推广，以便它将作为分布式控制系统的一部分在全规模工厂中工作。该计划的成功完成将导致过程控制方法，以最大限度地提高转化率、选择性和/或环境可接受性，许多涉及气相中间体和产品的常见化学过程。这项技术将为国家带来经济和环境效益，潜在的设施存在于美国和国外的数千个地点。***