TY - GEN
T1 - Modelling and Analysis of a Standing-Wave Thermo-Acoustic Refrigerator Using ANFIS
AU - Ngcukayitobi, M.
AU - Bannwart, F. C.
AU - Tartibu, L. K.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Researchers are actively working on developing technologies to address the crucial challenge of reducing the environmental impact of air conditioning and refrigeration systems, aiming to provide cooling solutions without harming the ozone layer and minimizing their contribution to global warming. To this end, a standing-wave thermo-acoustic system has been constructed and thoroughly investigated through experiments. In this study, a dataset comprising 148 data points was utilized to construct an ANFIS model. The evaluation of performance indicators demonstrates the potential utility of the ANFIS model for predicting configurations that were not directly measured during the experimental phase. Comparing the experimental data with ANFIS predictions reveals a close alignment, with the highest discrepancies amounting to a mere 0.86545%. Impressively, the results of this research study highlight the robustness of the ANFIS model, as it achieves a remarkable regression value (R2) of 0.9976, accompanied by a minimal mean square error of 1.1034e-4.
AB - Researchers are actively working on developing technologies to address the crucial challenge of reducing the environmental impact of air conditioning and refrigeration systems, aiming to provide cooling solutions without harming the ozone layer and minimizing their contribution to global warming. To this end, a standing-wave thermo-acoustic system has been constructed and thoroughly investigated through experiments. In this study, a dataset comprising 148 data points was utilized to construct an ANFIS model. The evaluation of performance indicators demonstrates the potential utility of the ANFIS model for predicting configurations that were not directly measured during the experimental phase. Comparing the experimental data with ANFIS predictions reveals a close alignment, with the highest discrepancies amounting to a mere 0.86545%. Impressively, the results of this research study highlight the robustness of the ANFIS model, as it achieves a remarkable regression value (R2) of 0.9976, accompanied by a minimal mean square error of 1.1034e-4.
KW - Adaptive Neuro-Fuzzy Inference System
KW - Cooling Load
KW - Loudspeaker
KW - Thermo-acoustic Refrigerator
UR - http://www.scopus.com/inward/record.url?scp=85187296174&partnerID=8YFLogxK
U2 - 10.1109/ICECET58911.2023.10389448
DO - 10.1109/ICECET58911.2023.10389448
M3 - Conference contribution
AN - SCOPUS:85187296174
T3 - International Conference on Electrical, Computer and Energy Technologies, ICECET 2023
BT - International Conference on Electrical, Computer and Energy Technologies, ICECET 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Electrical, Computer and Energy Technologies, ICECET 2023
Y2 - 16 November 2023 through 17 November 2023
ER -
