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Ph. D. ThesisPh. D. Thesis 9. Results  All Data Sets9. Results All Data Sets 9.1. Methanol and Ethanol by SPR9.1. Methanol and Ethanol by SPR
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Ph. D. Thesis
  Table of Contents
  1. Introduction
  2. Theory Fundamentals of the Multivariate Data Analysis
  3. Theory Quantification of the Refrigerants R22 and R134a: Part I
  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
    9.1. Methanol and Ethanol by SPR
      9.1.1. Single Analytes
      9.1.2. Parallel Growing Neural Network Framework
      9.1.3. Sensitivity Analysis
      9.1.4. Brute Force Variable Selection
      9.1.5. Conclusions
    9.2. Methanol, Ethanol and 1-Propanol by SPR
    9.3. Methanol, Ethanol and 1-Propanol by the RIfS Array and the 4l Setup
    9.4. Quaternary Mixtures by the SPR Setup and the RIfS Array
    9.5. Quantification of the Refrigerants R22 and R134a in Mixtures: Part II
  10. Results Various Aspects of the Frameworks and Measurements
  11. Summary and Outlook
  12. References
  13. Acknowledgements
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9.1.   Methanol and Ethanol by SPR

Measurements of the two homologues Methanol and Ethanol allow investigations of the difference of the kinetics of sorption and desorption, which is mainly based on the different sizes of the two analytes. Therefore several measurements of single analytes and measurements of mixtures were performed, which are described in section and in [190] in detail. Besides of non-optimized neural networks, the parallel growing neural network framework is applied to the data resulting in a very small number of variables being selected. This small number of variables allows the insight into the "black box" of the calibration by neural networks by the use of a sensitivity analysis. Additionally, the selection of the variables is confirmed by a brute-force variable selection.

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