OPTIMIZATION AND PREDICTION OF CENTRAL AC SYSTEM PERFORMANCE WITH RESPONSE SURFACE METHODOLOGY (RSM) MODELING
OPTIMIZATION AND PREDICTION OF CENTRAL AC SYSTEM PERFORMANCE WITH RESPONSE SURFACE METHODOLOGY (RSM) MODELING
Muhammad Nuriyadi
Politeknik Negeri Bandung
Muhammad Akmal
Politeknik Negeri Bandung
Cecep Sunardi
Politeknik Negeri Bandung
DOI: https://doi.org/10.19184/rotor.v17i2.53067
ABSTRACT
Optimizing AC system performance is important to optimize energy consumption, especially at partial load. The aim of this research is to obtain a performance model of the central AC system based on its operational conditions which include environmental air conditions, heat load conditions and other operational parameters, so that the performance of the AC system can be optimized which includes cooling capacity, power consumption and energy efficiency ratio. Data is obtained by experiment, then analyzed by Response Surface Methodology (RSM) to obtain optimal system performance. This research resulted in models of AC performance with coefficient of correlation (R2) of 0.9745, 0.2041 and 0.8965 for cooling capacity, power consumption and EER respectively. By analysis of varians for models, it is obtained that Model F-values are 9920.83, 313.45, and 2245.59 for cooling capacity, power and EER respectively, and implied that the models are significant. The Adequate precision ratios were 1078.33, 93.08, and 344.32 for those parameters respectively, and indicated the adequate signals. The optimum results obtained were a cooling capacity of 46.7 kW, compressor power consumption of 4.48 kW, and an energy efficiency ratio of 8.5.
Keywords: Performance, Central air conditioning, Prediction, Optimization, RSM.
REFERENCES
[1] Kemajou, A., Mba, L. and Pako-Mbou, G., 2012. Energy efficiency in air-conditioned buildings of the tropical humid climate. IJRRAS, Vol. 11(2), pp.235-240.
[2] Ran, B., Qiu, S., Zhang, Y., Zeng, L., Zhu, J., Xiang, Z. and Long, J., 2023. Air conditioning energy consumption measurement and saving strategy analysis for an office building in hot summer and cold winter area. Advances in Building Energy Research, Vol. 17 (1) pp.73-97.
[3] Aziz, M.B.A., Zain, Z.M., Baki, S.R.M.S. and Hadi, R.A., 2012, July. Air-conditioning energy consumption of an education building and it's building energy index: A case study in engineering complex, UiTM Shah Alam, Selangor. In 2012 IEEE Control and System Graduate Research Colloquium (pp. 175-180). IEEE.
[4]. yongshun, W. and dong, W., 2019, June. Energy consumption evaluation of air conditioning systems for public buildings. In 2019 Chinese Control And Decision Conference (CCDC), pp. 6380-6384. IEEE.
[5] Chua, K.J., Chou, S.K., Yang, W.M. and Yan, J., 2013. Achieving better energy-efficient air conditioning–a review of technologies and strategies. Applied energy, Vol. 104 pp.87-104.
[6] Pan, L., Wang, S., Wang, J., Xiao, M. and Tan, Z., 2022. Research on central air conditioning systems and an intelligent prediction model of building energy load. Energies, Vol. 15 (24) p.9295.
[7] Xu, X., Huang, G., Liu, H., Chen, L. and Liu, Q., 2015. The study of the dynamic load forecasting model about air-conditioning system based on the terminal user load. Energy and Buildings, Vol. 94 pp.263-268.
[8] Wu, J., Lu, B. and Liang, Z., 2018. Performance prediction of room air conditioners and optimization of control strategy for energy conservation. Heat Transfer Engineering, Vol. 39 (17-18) pp.1616-1626.
[9] Yang, J., Gao, X. and Yang, C., 2018, August. An optimization method for energy consumption of central air conditioning. In 2018 10th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), Vol. 1 pp. 243-246. IEEE.
[10] Anggraini, E., Anwar, S. and Rudiyanto, B., 2020. Optimasi pada Cooling Tower menggunakan Response Surface Methodology. In Prosiding Seminar Nasional NCIET, Vol. 1 (1) pp. 79-88. Jurusan Teknik Mesin Politeknik Negeri Semarang.
[11] Ayu, F.T., HG, I.R. and Asdi, Y., 2019. Optimasi Respon Pada Percobaan Faktorial Dengan Menggunakan Metode Permukaan Respon. Jurnal Matematika UNAND, Vol. 4(2), pp. 51-57.
[12] Wang, D., Yu, B., Li, W., Shi, J. and Chen, J., 2018. Heating performance evaluation of a CO2 heat pump system for an electrical vehicle at cold ambient temperatures. Applied Thermal Engineering, Vol. 142 pp. 656-664.
Published
30-11-2024
Issue
Vol. 17 No. 2 2024: ROTOR: Jurnal Ilmiah Teknik Mesin
Pages
51-57
License
Copyright (c) 2024 ROTOR:Jurnal Ilmiah Teknik Mesin
How to Cite
Nuriyadi, M., Akmal, M. and Sunardi, C., 2024. OPTIMIZATION AND PREDICTION OF CENTRAL AC SYSTEM PERFORMANCE WITH RESPONSE SURFACE METHODOLOGY (RSM) MODELING. ROTOR, 17(2), pp.51-57. https://doi.org/10.19184/rotor.v17i2.53067