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Fuzzy Inference Procedure for Intelligent and Automated Control of Refrigerant Charging

Abstract

Fuzzy logic controllers are readily customizable in natural language terms and can effectively deal with nonlinearities and uncertainties in control systems. This paper presents an intelligent and automated fuzzy control procedure for the refrigerant charging of refrigerators. The elements that affect the experimental charging and the optimization of the performance of refrigerators are fuzzified and used in an inference model. The objective is to represent the intelligent behavior of a human tester and ultimately make the developed model available for the use in an automated data acquisition, monitoring, and decision-making system. The proposed system is capable of determining the needed amount of refrigerant in the shortest possible time. The system automates the refrigerant charging and performance testing of parallel units. The system is built using data acquisition systems from National Instruments and programmed under LabVIEW. The developed fuzzy models, and their testing results, are evaluated according to their compatibility with the principles that govern the intelligent behavior of human experts when performing the refrigerant-charging process. In addition, comparisons of the fuzzy models with classical inference models are presented. The obtained results confirm that the proposed fuzzy controllers outperform traditional crisp controllers and provide major test time and energy savings. The paper includes thorough discussions, analysis, and evaluation.

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Acknowledgements

The authors would like to thank Mr. M. El-Khalili, a Senior Mechanical Engineer and Market Area Director at Eberspcher Strak GmbH, Germany, for the help and advice he provided in some paper-related issues. In addition, the authors are grateful for the thorough reviews, by the editor and the anonymous reviewers, that enabled great improvements to this paper.

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Correspondence to Issam Damaj.

Appendix: List of Acronyms and Symbols

Appendix: List of Acronyms and Symbols

Acronym or symbol Definition
\(^{\circ }{\hbox {C}}\) Degree celsius
COI Compositional rule of inference
COP Coefficient of performance
CPT Computerized performance test
DAQ Data acquisition
DTemp Temperature change variable
E Energy
GUI Graphical user interface
kWh Kilo Watts per hour
max Maximum
\(\wedge\) Minimum operator
MF Membership function
MISO Multiple input single output
NI National instruments
P Power
PR Performance
S4 Suction tube
t Time
TH Throughput
TpD Tests per 12-h work day
TpH Tests per hour
TS Time saving
TT Test time
Time Observation time variable

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Damaj, I., Saade, J., Al-Faisal, H. et al. Fuzzy Inference Procedure for Intelligent and Automated Control of Refrigerant Charging. Int. J. Fuzzy Syst. 20, 1790–1807 (2018). https://doi.org/10.1007/s40815-018-0486-3

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Keywords

  • Refrigerant charging
  • Modeling human expertise
  • Performance
  • Fuzzy inference
  • LabVIEW