(Noida International University, India)(2) * Owais Ahmad Shah
(K. R. Mangalam University, India)(3) Rakesh Kumar
(Noida International University, India)*corresponding author
AbstractThis study aims to enhance operational efficiency in chiller plants by implementing the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. The primary objectives are to simultaneously reduce energy consumption and increase cooling efficiency, addressing the challenges posed by variable environmental and operational conditions. Employing the MOPSO algorithm, this research conducts a detailed analysis using real-time environmental data and operational parameters. This approach facilitates a dynamic adaptation to changes in ambient temperature and electricity pricing, ensuring that the algorithm's application remains effective under fluctuating conditions. The application of MOPSO has resulted in significant reductions in energy use and improvements in cooling efficiency. These results demonstrate the algorithm's capacity to optimize chiller plant operations dynamically, adapting to changes in environmental conditions and operational demands. The study finds that MOPSO's adaptability to dynamic operational conditions enables robust energy management in chiller plants. This adaptability is crucial for maintaining efficiency and cost-effectiveness in industrial applications, especially under varying environmental impacts. The paper contributes to the field by enhancing the understanding of how advanced optimization algorithms like MOPSO can be effectively integrated into energy management systems for chiller plants. A novel aspect of this research is the integration of real-time data analytics into the optimization process, which significantly improves the sustainability and operational efficiency of HVAC systems. Furthermore, the study outlines the potential for similar research applications in large-scale HVAC systems, where such algorithmic improvements can extend practical benefits. The findings underscore the importance of considering a broad range of environmental and operational factors in the optimization process and suggest that MOPSO's flexibility and robustness make it a valuable tool for achieving sustainable and cost-effective energy management in industrial settings.
KeywordsMulti-Objective Optimization; Chiller Plant Efficiency; Particle Swarm Optimization; Energy Management; HVAC Systems Optimization
|
DOIhttps://doi.org/10.31763/ijrcs.v4i3.1501 |
Article metrics10.31763/ijrcs.v4i3.1501 Abstract views : 299 | PDF views : 82 |
Cite |
Full Text Download
|
References
[1] M. Karami and L. Wang, “Particle Swarm optimization for control operation of an all-variable speed water-cooled chiller plant,” Applied Thermal Engineering, vol. 130, pp. 962-978, 2018, https://doi.org/10.1016/j.applthermaleng.2017.11.037.
[2] Y. Fukuyama, H. Nishida, and Y. Todaka, “Particle Swarm Optimization for Multi-Objective Optimal Operational Planning of Energy Plants,” Knowledge Mining Using Intelligent Agents, pp. 201-216, 2010, https://doi.org/10.1142/9781848163874_0007.
[3] Y. He, Q. Xu, D. Li, S. Mei, Z. Zhang, and Q. Ji, “Energy-saving method and performance analysis of chiller plants group control based on Kernel Ridge Regression and Genetic Algorithm,” Science and Technology for the Built Environment, vol. 29, no. 5, pp. 545-559, 2023, https://doi.org/10.1080/23744731.2023.2179304.
[4] M. Behl, T. X. Nghiem and R. Mangharam, “Green Scheduling for Energy-Efficient Operation of Multiple Chiller Plants,” 2012 IEEE 33rd Real-Time Systems Symposium, pp. 195-204, 2012, https://doi.org/10.1109/RTSS.2012.71.
[5] J. Li and M. Wang, “Application of improved particle swarm optimization in the energy saving strategy for a central air-conditioning system,” IET Generation, Transmission & Distribution, vol. 17, no. 15, pp. 3512-3523, 2023, https://doi.org/10.1049/gtd2.12889.
[6] C. Underwood, B. Ng, and F. Yik, “Scheduling of multiple chillers in trigeneration plants,” Energies, vol. 8, no. 10, pp. 11095–11119, 2015, https://doi.org/10.3390/en81011095.
[7] G. Zhang et al., “Optimal design and integration of multiple-function solar water heating systems based on a co-adaptive matchmaking method,” Energy & Buildings, vol. 252, p. 111492, 2021, https://doi.org/10.1016/j.enbuild.2021.111492.
[8] L. Jia, J. Liu, and S. Wei, “Optimal chiller loading in dual-temperature chilled water plants for energy saving,” Energy & Buildings, vol. 252, p. 111425, 2021, https://doi.org/10.1016/j.enbuild.2021.111425.
[9] C. W. Chen, C. W. Lee, and C. Y. Chen, “To Enhance the Energy Efficiency of Chiller Plants with System Optimization Theory,” Energy & Environment, vol. 21, no. 5, pp. 409-424, 2010, https://doi.org/10.1260/0958-305X.21.5.409.
[10] E. B. Kyere, J. Tien-Chien, and L. Tartibu, “Parametric analysis of chiller plant energy consumption in a tropical climate,” International Journal of Air-Conditioning and Refrigeration, vol. 31, no. 28, pp. 1–22, 2023, https://doi.org/10.1007/s44189-023-00045-5.
[11] K. F. Fong, C. K. Lee, and T. T. Chow, “Investigation on variable flow control in existing water-cooled chiller plant of high-rise commercial building in subtropical climate,” HVAC&R Research, vol. 20, no. 1, pp. 51-60, 2014, https://doi.org/10.1080/10789669.2013.822253.
[12] Y. Wu, F. Zhang and Y. Su, “Optimization of additional heat production performance of air conditioning compressor based on hybrid particle swarm optimization algorithm,” 2023 IEEE 2nd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA), pp. 612-617, 2023, https://doi.org/10.1109/EEBDA56825.2023.10090644.
[13] R. Alayi et al., “Technical and Economic Evaluation and Optimization of an Off-Grid Wind/Hydropower Hybrid System,” International Journal of Robotics and Control Systems, vol. 3, no. 3, pp. 380-395, 2023, https://doi.org/10.31763/ijrcs.v3i3.996.
[14] H. Maghfiroh, M. Nizam, M. Anwar and A. Ma’Arif, “Improved LQR Control Using PSO Optimization and Kalman Filter Estimator,” IEEE Access, vol. 10, pp. 18330-18337, 2022, https://doi.org/10.1109/ACCESS.2022.3149951.
[15] Z. Ishrat, S. Vats, K. B. Ali, O. Ahmad Shah and T. Ahmed, “A Comprehensive Study on Conventional HPPT Techniques for Solar PV System,” 2023 International Conference on Computational Intelligence, Communication Technology and Networking (CICTN), pp. 315-321, 2023, https://doi.org/10.1109/CICTN57981.2023.10140264.
[16] A. Kumar, P. Chaudhary, and O. A. Shah, “Intelligent Controller Based on Artificial Neural Network and INC Based MPPT for Grid Integrated Solar PV System,” International Journal of Robotics and Control Systems, vol. 3, no. 4, pp. 839-852, 2023, https://doi.org/10.31763/ijrcs.v3i4.1150.
[17] R. Patra, P. Chaudhary, and O. A. Shah, “Design and Development of ANFIS based Controller for Three Phase Grid Connected System,” International Journal of Robotics and Control Systems, vol. 4, no. 1, pp. 125–138, 2024, https://doi.org/10.31763/ijrcs.v4i1.1242.
[18] M. Singh, S. Arora, and O. Shah, “Enhancing Hybrid Power System Performance with GWO-Tuned Fuzzy-P.I.D. Controllers: A Comparative Study,” International Journal of Robotics and Control Systems, vol. 4, no. 2, pp. 709-726, 2024, https://doi.org/10.31763/ijrcs.v4i2.1388.
[19] S. Bordihn, A. Fladung, J. Schlipf and M. Köntges, “Machine Learning Based Identification and Classification of Field-Operation Caused Solar Panel Failures Observed in Electroluminescence Images,” IEEE Journal of Photovoltaics, vol. 12, no. 3, pp. 827-832, 2022, https://doi.org/10.1109/JPHOTOV.2022.3150725.
[20] A. Asrari, T. X. Wu and B. Ramos, “A Hybrid Algorithm for Short-Term Solar Power Prediction—Sunshine State Case Study,” IEEE Transactions on Sustainable Energy, vol. 8, no. 2, pp. 582-591, 2017, https://doi.org/10.1109/TSTE.2016.2613962.
[21] B. Singh, O. Shah, and S. Arora, “Seasonal Electrical Load Forecasting Using Machine Learning Techniques and Meteorological Variables,” International Journal of Robotics and Control Systems, vol. 4, no. 3, pp. 1092-1108, 2024, https://doi.org/10.31763/ijrcs.v4i3.1446.
[22] R. K. Tipu, O. A. Shah, S. Vats and S. Arora, “Deep Learning Projections for High-Performance Concrete Strength Forecasting,” 2024 4th International Conference on Innovative Practices in Technology and Management (ICIPTM), pp. 1-4, 2024, https://doi.org/10.1109/ICIPTM59628.2024.10563896.
[23] V. Rajagopal, D. Sharath, G. Vishwas, J. Bangarraju, S. R. Arya and C. Venkatesh, “Optimized Controller Gains Using Grey Wolf Algorithm for Grid Tied Solar Power Generation with Improved Dynamics and Power Quality,” Chinese Journal of Electrical Engineering, vol. 8, no. 2, pp. 75-85, 2022, https://doi.org/10.23919/CJEE.2022.000016.
[24] R. K. Tipu, O. A. Shah, S. Vats and S. Purohit, “Enhancing Concrete Properties Through the Integration of Recycled Coarse Aggregate: A Machine Learning Approach for Sustainable Construction,” 2024 4th International Conference on Innovative Practices in Technology and Management (ICIPTM), pp. 1-5, 2024, https://doi.org/10.1109/ICIPTM59628.2024.10563490.
[25] A. A. Z. Diab, H. M. Sultan, T. D. Do, O. M. Kamel and M. A. Mossa, “Coyote Optimization Algorithm for Parameters Estimation of Various Models of Solar Cells and PV Modules,” IEEE Access, vol. 8, pp. 111102-111140, 2020, https://doi.org/10.1109/ACCESS.2020.3000770.
[26] Z. Zheng et al., “An Edge Based Data-Driven Chiller Sequencing Framework for HVAC Electricity Consumption Reduction in Commercial Buildings,” IEEE Transactions on Sustainable Computing, vol. 7, no. 3, pp. 487-498, 2022, https://doi.org/10.1109/TSUSC.2019.2932045.
[27] Z. Chen, J. Wang and Q. -L. Han, “Distributed Chiller Loading via Collaborative Neurodynamic Optimization With Heterogeneous Neural Networks,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 54, no. 4, pp. 2067-2078, 2024, https://doi.org/10.1109/TSMC.2023.3331260.
[28] Z. Chen, J. Wang and Q. -L. Han, “Receding-Horizon Chiller Operation Planning via Collaborative Neurodynamic Optimization,” IEEE Transactions on Smart Grid, vol. 15, no. 2, pp. 2321-2331, 2024, https://doi.org/10.1109/TSG.2023.3305326.
[29] D. Zhang, K. Mittal, J. Wilson, P. B. Luh, J. Fan and S. Gupta, “Efficiency and Reliability Joint Optimization of Chiller Plants Based on a Hybrid Model,” IEEE Robotics and Automation Letters, vol. 4, no. 4, pp. 3224-3231, 2019, https://doi.org/10.1109/LRA.2019.2924126.
[30] Yung-Chung Chang, “An outstanding method for saving Energy-optimal chiller operation,” IEEE Transactions on Energy Conversion, vol. 21, no. 2, pp. 527-532, 2006, https://doi.org/10.1109/TEC.2006.871358.
[31] F. Simmini, M. Rampazzo, F. Peterle, G. A. Susto and A. Beghi, “A Self-Tuning KPCA-Based Approach to Fault Detection in Chiller Systems,” IEEE Transactions on Control Systems Technology, vol. 30, no. 4, pp. 1359-1374, 2022, https://doi.org/10.1109/TCST.2021.3107200.
[32] S. M. Namburu, M. S. Azam, J. Luo, K. Choi and K. R. Pattipati, “Data-Driven Modeling, Fault Diagnosis and Optimal Sensor Selection for HVAC Chillers,” IEEE Transactions on Automation Science and Engineering, vol. 4, no. 3, pp. 469-473, 2007, https://doi.org/10.1109/TASE.2006.888053.
[33] Z. Chen, J. Wang and Q. -L. Han, “Chiller Plant Operation Planning via Collaborative Neurodynamic Optimization,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 8, pp. 4623-4635, 2023, https://doi.org/10.1109/TSMC.2023.3247633.
[34] D. Zhang, P. B. Luh, J. Fan and S. Gupta, “Chiller Plant Operation Optimization With Minimum Up/Down Time Constraints,” IEEE Robotics and Automation Letters, vol. 3, no. 1, pp. 9-15, 2018, https://doi.org/10.1109/LRA.2017.2723467.
[35] D. Zhang, P. B. Luh, J. Fan and S. Gupta, “Chiller Plant Operation Optimization: Energy-Efficient Primary-Only and Primary–Secondary Systems,” IEEE Transactions on Automation Science and Engineering, vol. 15, no. 1, pp. 341-355, 2018, https://doi.org/10.1109/TASE.2017.2751605.
[36] F. Oliveira and A. Ukil, “Comparative Performance Analysis of Induction and Synchronous Reluctance Motors in Chiller Systems for Energy Efficient Buildings,” IEEE Transactions on Industrial Informatics, vol. 15, no. 8, pp. 4384-4393, 2019, https://doi.org/10.1109/TII.2018.2890270.
[37] M. A. El-Shorbagy and A. E. Hassanien, “Particle swarm optimization from theory to applications,” International Journal of Rough Sets and Data Analysis, vol. 5, no. 2, pp. 1–24, 2018, https://doi.org/10.4018/IJRSDA.2018040101
[38] T. M. Shami, A. A. El-Saleh, M. Alswaitti, Q. Al-Tashi, M. A. Summakieh and S. Mirjalili, “Particle Swarm Optimization: A Comprehensive Survey,” IEEE Access, vol. 10, pp. 10031-10061, 2022, https://doi.org/10.1109/ACCESS.2022.3142859.
[39] L. G. Ramón-Canul et al., “Technique for order of preference by similarity to ideal solution (TOPSIS) method for the generation of external preference mapping using rapid sensometric techniques,” Journal of the Science Food and Agriculture, vol. 101, no. 8, pp. 3298-3307, 2021, https://doi.org/10.1002/jsfa.10959
[40] R. Rahim et al., “Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method for decision support system in top management,” Pro Marketing, vol. 8, no. 2, pp. 290-293, 2018, https://ejurnal.ulbi.ac.id/index.php/promark/article/download/908/679.
[41] M. Shamoushaki and M. A. Ehyaei, “Exergy, economic and environmental (3E) analysis of a gas turbine power plant and optimization by MOPSO algorithm,” Thermal Science, vol. 22, no. 6, pp. 2641-2651, 2018, https://doi.org/10.2298/TSCI161011091S.
[42] I. Marouani, C. Dhifaoui, and H. H. Abdallah, “Optimization Hydro-Thermal-Wind-PV Solar Using MOPSO Algorithm Applied to Economic/Environmental Dispatch,” Bioscience Biotechnology Research Communications, vol. 14, no. 5, pp. 118-125, 2021, http://dx.doi.org/10.21786/bbrc/14.5/23.
[43] C.-C. Lo, S.-H. Tsai, and B.-S. Lin, “Economic dispatch of chiller plant by improved ripple bee swarm optimization algorithm for saving energy,” Applied Thermal Engineering, vol. 100, pp. 1140-1148, 2016, https://doi.org/10.1016/j.applthermaleng.2016.02.114.
[44] F. Panahizadeh, M. Hamzehei, M. Farzaneh-Gord, and A. A. Ochoa Villa, “Thermo-economic analysis and optimization of the steam absorption chiller network plant,” Thermal Science, vol. 26, no. 1, pp. 95-106, 2022, https://doi.org/10.2298/TSCI200619058P.
[45] Z. -J. Wang, Z. -H. Zhan, S. Kwong, H. Jin and J. Zhang, “Adaptive Granularity Learning Distributed Particle Swarm Optimization for Large-Scale Optimization,” IEEE Transactions on Cybernetics, vol. 51, no. 3, pp. 1175-1188, 2021, https://doi.org/10.1109/TCYB.2020.2977956.
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Yogesh Bhardwaj, Owais Ahmad Shah, Rakesh Kumar
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
| About the Journal | Journal Policies | Author | Information |
International Journal of Robotics and Control Systems
e-ISSN: 2775-2658
Website: https://pubs2.ascee.org/index.php/IJRCS
Email: ijrcs@ascee.org
Organized by: Association for Scientific Computing Electronics and Engineering (ASCEE), Peneliti Teknologi Teknik Indonesia, Department of Electrical Engineering, Universitas Ahmad Dahlan and Kuliah Teknik Elektro
Published by: Association for Scientific Computing Electronics and Engineering (ASCEE)
Office: Jalan Janti, Karangjambe 130B, Banguntapan, Bantul, Daerah Istimewa Yogyakarta, Indonesia