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Ph. D. ThesisPh. D. Thesis 3. Theory – Quantification of the Refrigerants R22 and R134a: Part I3. Theory – Quantification of the Refrigerants R22 and R134a: Part I
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Ph. D. Thesis
  Abstract
  Table of Contents
  1. Introduction
  2. Theory – Fundamentals of the Multivariate Data Analysis
  3. Theory – Quantification of the Refrigerants R22 and R134a: Part I
    3.1. Experimental
    3.2. Single Analytes
    3.3. Sensitivities
    3.4. Calibrations of the Mixtures
    3.5. Variable Selection by Brute Force
    3.6. Conclusions
  4. Experiments, Setups and Data Sets
  5. Results – Kinetic Measurements
  6. Results – Multivariate Calibrations
  7. Results – Genetic Algorithm Framework
  8. Results – Growing Neural Network Framework
  9. Results – All Data Sets
  10. Results – Various Aspects of the Frameworks and Measurements
  11. Summary and Outlook
  12. References
  13. Acknowledgements
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3.   Theory – Quantification of the Refrigerants R22 and R134a: Part I

In this chapter, an example of a multivariate calibration in chemical sensing is shown whereby the focus of this example is the demonstration of the methods, which are widely accepted and which can be found in literature over and over again. This allows an easy comparison with the new approaches proposed in this study, which are going far beyond the widespread techniques in the areas of multivariate calibration and measurement principles. Furthermore, this data set will be examined later again (section 9.5) using the new approaches proposed in this work. Although the recording of the data set was not optimized for these approaches, the new methods of data analysis show better results. Additionally some concepts and theories of chemical sensing of vapors by polymer-based sensors are introduced in this chapter.

The objective of this example is the quantitative detection of the ozone depleting R22 (chlorodifluoromethan) in the vapor of its harmless substitute R134a (1,1,1,2-tetrafluoroethan) and in air for preliminary studies of on-line measure­ments in recycling stations. More details of the environmental background of these refrigerants can be found in section 4.5.1. First, the sorption characteristics of 6 different polymers, which are exposed to different concentrations of the refrigerants R22 and R134a, are investigated with a sensor array setup in respect to sensitivities, sensitivity patterns, and calibration curves. Based on these investigations two polymers are selected for the application in a miniaturized low-cost 4l sensor setup, which complies best with the conditions for on-site measurements at recycling stations. Finally, different binary mixtures of R22 and R134a are measured by both setups and a multivariate calibration is performed by the use of neural networks.

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