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Ph. D. ThesisPh. D. Thesis 9. Results  All Data Sets9. Results All Data Sets 9.3. Methanol, Ethanol and 1-Propanol by the RIfS Array and the 4l Setup9.3. Methanol, Ethanol and 1-Propanol by the RIfS Array and the 4l Setup 9.3.3. Mixtures by the 4l Setup9.3.3. Mixtures by the 4l Setup
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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
  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
    9.2. Methanol, Ethanol and 1-Propanol by SPR
    9.3. Methanol, Ethanol and 1-Propanol by the RIfS Array and the 4l Setup
      9.3.1. Signals and Data Preparation
      9.3.2. Mixtures by the RIfS Array
      9.3.3. Mixtures by the 4l Setup
      9.3.4. Conclusions
    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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9.3.3.   Mixtures by the 4l Setup

For the quantification of the ternary mixtures measured by the 4l setup, the data were evaluated with the growing neural network framework. The optimized networks (13 input variables, 5 hidden neurons and 1 output neuron) predicted the validation data with relative errors between 17.15% and 25.2%. The corresponding true-predicted plots are shown in figure 72. The results of the 4l setup can be best compared with the 80 nm (smoothed) single sensor calibration of the array setup. Yet, the thick layer of the 4l setup needs significantly more time for the desorption of the analytes (nearly 600 seconds), whereas the 80 nm layer recovers in less than 60 seconds. The 160 nm layer of the array, which needs about 200 seconds for recovery, shows significantly better predictions than the 4l setup. This means that although the 4l setup can be successfully used for the multicomponent analysis, the price of miniaturization and simplification has to be paid in terms of longer measurement times or worse calibrations.

figure 72: True-predicted plots for the 4-setup.

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