4.5.1.2. R22 and R134a by the RIfS Array and the 4l-Setup (Dr. Frank Dieterle)

Frank Dieterle

Ph. D. Thesis

4. Experiments, Setups and Data Sets

4.5. Data Sets

4.5.1. Refrigerants R22 and R134a

4.5.1.2. R22 and R134a by the RIfS Array and 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
		4.1. The Sensor Principle
		4.2. SPR Setup
		4.3. RIfS Sensor Array
		4.4. 4l Miniaturized RIfS Sensor
		4.5. Data Sets
			4.5.1. Refrigerants R22 and R134a
				4.5.1.1. R22 and R134a by the SPR Setup
				4.5.1.2. R22 and R134a by the RIfS Array and the 4l-Setup
			4.5.2. Homologous Series of the Low Alcohols
	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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4.5.1.2. R22 and R134a by the RIfS Array and the 4l-Setup

This data set was already introduced and described in chapter 3 and will be further investigated in section 9.5 as the sensor response of the Makrolon layer was also recorded in a time-resolved mode. The signal of the Makrolon sensor could be extracted from the raw measurements at 18 time points during sorption and desorption. Thus, 5 independent variables of the static measurements of 5 polymers and additionally 18 independent variables of the time-resolved measurements were available.

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