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In-Line Near Real Time Monitoring Of A complex fluid separation system Using Spectroscopic And Physicochemical Measurements By Multivariate Analysis.

机译:使用多变量分析通过光谱和理化测量对复杂的流体分离系统进行在线近实时监测。

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摘要

Applying spectroscopic tools for chemical processes has been intensively studied in various industries owing to its rapid and non-destructively analysis for detecting chemical components and physical characteristic in a process stream. The general complexity of separation processes for used nuclear fuel, e.g., chemical speciation, temperature variations, and prominent process security and safety concerns, require a well-secured monitoring system to provide precise information of the process streams at real time without interference. Multivariate analysis accompanied with spectral measurements is a powerful statistic tool that can be used to monitor this complex chemical system. In this thesis, chemometric models that respond to the chemical components in the samples were calibrated and validated to establish an inline near real time monitoring system. The models show good prediction accuracy using partial least square regression analysis on the spectral data obtained from NIR, Raman and UV/Vis spectroscopies. Ultimately, an extraction process using a single stage centrifugal contactor was tested in our laboratory to determine the performance of an inline near real time monitoring system for a solvent extraction process representative of used nuclear fuel separation processes.
机译:由于光谱分析工具可以快速,无损地检测过程流中的化学成分和物理特性,因此已在各个行业中广泛研究了光谱工具的应用。废旧核燃料分离过程的一般复杂性,例如化学形态,温度变化以及突出的过程安全性和安全性,都需要一个安全可靠的监控系统,以便实时,无干扰地提供过程流的精确信息。伴随光谱测量的多元分析是功能强大的统计工具,可用于监视此复杂的化学系统。在本文中,对响应样品中化学成分的化学计量模型进行了校准和验证,以建立在线近实时监测系统。使用从NIR,拉曼光谱和UV / Vis光谱获得的光谱数据进行偏最小二乘回归分析,模型显示出良好的预测准确性。最终,在我们的实验室中测试了使用单级离心接触器的提取过程,以确定代表核燃料分离过程的溶剂提取过程的在线近实时监控系统的性能。

著录项

  • 作者

    Nee, Ko.;

  • 作者单位

    University of California, Irvine.;

  • 授予单位 University of California, Irvine.;
  • 学科 Chemical engineering.
  • 学位 Ph.D.
  • 年度 2014
  • 页码 100 p.
  • 总页数 100
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-17 11:54:06

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