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Fuzzy Inference Procedure for Intelligent and Automated Control of Refrigerant Charging

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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.

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, American University of Kuwait, Salmiya, Kuwait

    Issam Damaj

  2. Department of Electrical and Computer Engineering, American University of Beirut, Beirut, Lebanon

    Jean Saade & Hassan Diab

  3. Department of Computer Engineering, Kuwait University, Khaldiya, Kuwait

    Hala Al-Faisal

Authors
  1. Issam Damaj
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  2. Jean Saade
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  3. Hala Al-Faisal
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  4. Hassan Diab
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Corresponding author

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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  • DOI: https://doi.org/10.1007/s40815-018-0486-3

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