The optimum sizing of zero-emission water-cooled VCR cycle based on exergo-economic-environmental assessment criteria by triple-objective MPSO
Status PubMed-not-MEDLINE Jazyk angličtina Země Anglie, Velká Británie Médium electronic
Typ dokumentu časopisecké články
PubMed
39572614
PubMed Central
PMC11582600
DOI
10.1038/s41598-024-78994-z
PII: 10.1038/s41598-024-78994-z
Knihovny.cz E-zdroje
- Klíčová slova
- 5E analysis, Cold storage, Compression cycle, Triple-objective MPSO optimization, Wind energy,
- Publikační typ
- časopisecké články MeSH
Renewable energies are interesting as an alternative and sustainable resource for air conditioning applications. But initial investment cost of equipment, whose employed for converting the renewable energy into usable shape and also for air conditioning duty, are significant. Therefore, determining the optimum sizing has high priority. In current study, water cooled vapor compression refrigeration cycle powered by wind energy and storage tank is proposed, simulated and optimized. To contribute the total effective aspects in system optimum size, the thermo-economic-environmental criteria is defined. By the help of databank of parametric analysis, the optimum design variables are determined by employing the GA optimization algorithm. In the following, an intelligence neural network is developed to learn the reliable correlation between the inputs and outputs data. Finally, the optimum size of each subsystem is determined by using triple-objective MPSO. Based on detailed economic analysis, the system payback period is estimated about 450 days which is 41% less than the conventional system. The daily COP and exergy efficiency of the whole system has improved up to 98% and 40%, after substituting the optimum design variable parameters. Triple-objective MPSO results show that, the ice storage tank should be selected 22% smaller than the initial amount.
College of Engineering University of Business and Technology 21448 Jeddah Saudi Arabia
Department of Electrical Engineering Faculty of Engineering University of Zabol Zabol Iran
Department of Electrical Engineering Graphic Era Dehradun 248002 India
Department of Mechanical Engineering Faculty of Engineering University of Zabol Zabol Iran
Hourani Center for Applied Scientific Research Al Ahliyya Amman University Amman Jordan
Mechanical Engineering Department Faculty of Engineering Ferdowsi University of Mashhad Mashhad Iran
Zobrazit více v PubMed
Li, S., Pan, Y., Wang, Q. & Huang, Z. A non-cooperative game-based distributed optimization method for chiller plant control. Build. Simul.10.1007/s12273-021-0869-5 (2020). PubMed
Jia, L., Wei, S. & Liu, J. A review of optimization approaches for controlling water-cooled central cooling systems. Build Environ.203, 108100. 10.1016/j.buildenv.2021.108100 (2021).
Zhou, J. et al. Modeling and configuration optimization of the natural gas-wind-photovoltaic-hydrogen integrated energy system : A novel deviation satisfaction strategy. Energy Convers Manag.243, 114340. 10.1016/j.enconman.2021.114340 (2021).
Liu, J., Zhou, Y., Yang, H. & Wu, H. Net-zero energy management and optimization of commercial building sectors with hybrid renewable energy systems integrated with energy storage of pumped hydro and hydrogen taxis. Appl. Energy321, 119312. 10.1016/j.apenergy.2022.119312 (2022).
Yi, T. et al. Energy storage capacity optimization of wind-energy storage hybrid power plant based on dynamic control strategy. J. Energy Storage55, 105372. 10.1016/j.est.2022.105372 (2022).
Tiwari, R. & Babu, N. R. Recent developments of control strategies for wind energy conversion system. Renew. Sustain Energy Rev.66, 268–285. 10.1016/j.rser.2016.08.005 (2016).
Liu, Z. et al. Energy and exergy analysis of a novel direct-expansion ice thermal storage system based on three-fluid heat exchanger module. Appl. Energy330, 120371. 10.1016/j.apenergy.2022.120371 (2023).
Miri, S. M., Farzaneh-gord, M. & Kianifar, A. Evaluating the dynamic behaviour of wind-powered compression refrigeration cycle integrated with an ice storage tank for air conditioning application. Energy Convers Manag.269, 116093. 10.1016/j.enconman.2022.116093 (2022).
Mahdavia, S., Shiria, M. E. & Rahnamayanb, S. Metaheuristics in large-scale global continues optimization: A survey. Inform. Sci.295, 407–428 (2015).
Farzaneh-gord, M., Reza, H., Mohseni-gharesafa, B., Toikka, A. & Zvereva, I. Journal of Petroleum Science and Engineering Accurate determination of natural gas compressibility factor by measuring temperature, pressure and Joule-Thomson coefficient : Artificial neural network approach Equations of State. J. Pet. Sci. Eng.202, 108427. 10.1016/j.petrol.2021.108427 (2021).
Yang, L., Zhao, L., Zhang, C. & Gu, B. Loss-efficiency model of single and variable-speed compressors using neural networks ` le de la diminution d ’ efficacite ´ des compresseurs Mode ` vitesse variable a ` l ’ aide de re ´ seaux neuronaux simples et a. Int. J. Refrig.32, 1423–1432. 10.1016/j.ijrefrig.2009.03.006 (2009).
Kalogirou, S. A. Optimization of solar systems using artificial neural-networks and genetic algorithms. Appl. Energy77, 383–405. 10.1016/S0306-2619(03)00153-3 (2004).
She, X. et al. Energy-efficient and -economic technologies for air conditioning with vapor compression refrigeration: A comprehensive review. Appl. Energy232, 157–186. 10.1016/j.apenergy.2018.09.067 (2018).
Al-Otaibi, D., Dincer, I. & Kalyon, M. Thermoeconomic optimization of vapor- compression refrigeration systems. Int. Commun. Heat mass Transfer31, 95–107 (2004).
Deymi-dashtebayaz, M., Maddah, S. & Fallahi, E. Thermo-economic-environmental optimization of injection mass flow rate in the two-stage compression refrigeration cycle ( Case study : Mobarakeh steel company in Isfahan, Iran ) Optimisation thermo-économico-environnmentale du débit massique d ’ injection dans un cycle frigorifique à compression bi-étagée ( étude de cas : Mobarakeh steel company à Ispahan, Iran ). Int. J. Refrig.106, 7–17. 10.1016/j.ijrefrig.2019.06.020 (2019).
Ustaoglu, A., Kursuncu, B., Alptekin, M. & Gok, M. S. Performance optimization and parametric evaluation of the cascade vapor compression refrigeration cycle using Taguchi and ANOVA methods. Appl. Therm. Eng.180, 115816. 10.1016/j.applthermaleng.2020.115816 (2020).
Selbas, R., Kızılkan, Ã. & Arzu, S. Thermoeconomic optimization of subcooled and superheated vapor compression refrigeration cycle. Energy31, 2108–2128. 10.1016/j.energy.2005.10.015 (2006).
Kong, D., Yin, X., Ding, X., Fang, N. & Duan, P. Global optimization of a vapor compression refrigeration system with a self-adaptive differential evolution algorithm. Appl. Therm. Eng.197, 117427. 10.1016/j.applthermaleng.2021.117427 (2021).
Zhao, L., Cai, W., Ding, X. & Chang, W. Model-based optimization for vapor compression refrigeration cycle. Energy55, 392–402. 10.1016/j.energy.2013.02.071 (2013).
Zhao, L., Cai, W. J., Ding, X. D. & Chang, W. C. Decentralized optimization for vapor compression refrigeration cycle. Appl. Therm. Eng.51, 753–763. 10.1016/j.applthermaleng.2012.10.001 (2013).
Aminyavari, M., Naja, B., Shirazi, A. & Rinaldi, F. Exergetic, economic and environmental (3E ) analyses, and multi- objective optimization of a CO 2 / NH 3 cascade refrigeration system. Appl. Therm. Eng.65, 42–50. 10.1016/j.applthermaleng.2013.12.075 (2014).
Roy, R. Thermo-economic Assessment and Multi-Objective Optimization of Vapour Compression Refrigeration System using Low GWP Refrigerants. In 2019 8th Int Conf Model Simul Appl Optim, 2019:1–5.
Sayyaadi, H. & Nejatolahi, M. Multi-objective optimization of a cooling tower assisted vapor compression refrigeration system ` me frigorifique a ` compression de Optimisation d ’ un syste ´ d ’ une tour de refroidissement mene ´ e avec vapeur dote plusieurs objectifs. Int. J. Refrig34, 243–256. 10.1016/j.ijrefrig.2010.07.026 (2010).
Khanmohammadi, S., Kizilkan, O. & Waly, F. Tri-objective optimization of a hybrid solar-assisted power- refrigeration system working with supercritical carbon dioxide. Renew. Energy10.1016/j.renene.2019.11.155 (2019).
Ghaebi, H. & Rostamzadeh, H. Design and optimization of a novel dual-loop bi-evaporator ejection/compression refrigeration cycle. Appl. Therm. Eng.10.1016/j.applthermaleng.2019.01.114 (2019).
Zhar, R., Allouhi, A., Ghodbane, M., Jamil, A. & Lahrech, K. Parametric analysis and multi-objective optimization of a combined organic rankine cycle and vapor compression cycle. Sustain Energy Technol. Ass.47, 101401. 10.1016/j.seta.2021.101401 (2021).
Salim, M. S. & Kim, M. Multi-objective thermo-economic optimization of a combined organic Rankine cycle and vapour compression refrigeration cycle. Energy Convers Manag.199, 112054. 10.1016/j.enconman.2019.112054 (2019).
Ashwni, Faizan, A. & Tiwari, D. Exergy, economic and environmental analysis of organic Rankine cycle based vapor compression refrigeration system Analyse exergétique, économique et environnementale d ’ un système frigorifique à compression de vapeur basé sur le cycle organique de Rankine. Int. J. Refrig.126, 259–271. 10.1016/j.ijrefrig.2021.02.005 (2021).
Patel, B., Desai, N. B. & Kachhwaha, S. S. Optimization of waste heat based organic Rankine cycle powered cascaded vapor compression-absorption refrigeration system. Energy Convers Manag.154, 576–590. 10.1016/j.enconman.2017.11.045 (2017).
Ora, E., de Gracia, A., Castell, A., Farid, M. M. & Cabeza, L. F. Review on phase change materials (PCMs) for cold thermal energy storage applications. Appl. Energy99, 513–533. 10.1016/j.apenergy.2012.03.058 (2012).
Tam, A., Ziviani, D., Braun, J. E. & Jain, N. Energy & buildings development and evaluation of a generalized rule-based control strategy for residential ice storage systems. Energy Build.197, 99–111. 10.1016/j.enbuild.2019.05.040 (2019).
Henze, G. P. Parametric study of a simplified ice storage model operating under conventional and optimal control strategies. J. Sol. Energy Eng. Trans. ASME125, 2–12. 10.1115/1.1530629 (2003).
Chen, H., Wang, D. W. P. & Chen, S. Optimization of an ice-storage air conditioning system using dynamic programming method. Appl. Therm. Eng.25, 461–472. 10.1016/j.applthermaleng.2003.12.006 (2005).
Song, X., Zhu, T., Liu, L. & Cao, Z. Study on optimal ice storage capacity of ice thermal storage system and its in fl uence factors. Energy Convers Manag.164, 288–300. 10.1016/j.enconman.2018.03.007 (2018).
Luo, N., Hong, T., Li, H., Jia, R. & Weng, W. Data analytics and optimization of an ice-based energy storage system for commercial buildings. Appl. Energy204, 459–475. 10.1016/j.apenergy.2017.07.048 (2017).
Sanaye, S., Fardad, A. & Mostakhdemi, M. Thermoeconomic optimization of an ice thermal storage system for gas turbine inlet cooling. Energy36, 1057–1067. 10.1016/j.energy.2010.12.002 (2011).
Badar, M. A., Zubair, S. M., Abdulghani, A. & Al-Farayedhi, A. Second-law-based thermoeconomic optimization of a sensible heat thermal energy storage system. Energy18(6), 641–649 (1993).
Akif, M., Erek, A. & Dincer, I. Energy and exergy analyses of an ice-on-coil thermal energy storage system. Energy36, 6375–6386. 10.1016/j.energy.2011.09.036 (2011).
Sanaye, S. & Shirazi, A. Thermo-economic optimization of an ice thermal energy storage system for air-conditioning applications. Energy Build.60, 100–109. 10.1016/j.enbuild.2012.12.040 (2013).
Sanaye, S. & Hekmatian, M. Ice Thermal Energy Storage ( ITES ) for air-conditioning application in full and partial load operating modes. Int. J. Refrig.10.1016/j.ijrefrig.2015.10.014 (2015).
Habeebullah, B. A. Economic feasibility of thermal energy storage systems. Energy Build.39, 355–363. 10.1016/j.enbuild.2006.07.006 (2007).
Sanaye, S. & Khakpaay, N. Thermo-economic multi-objective optimization of an innovative cascaded organic Rankine cycle heat recovery and power generation system integrated with gas engine and ice thermal energy storage. J. Energy Storage32, 101697. 10.1016/j.est.2020.101697 (2020).
Shirazi, A., Najafi, B., Aminyavari, M., Rinaldi, F. & Taylor, R. A. Thermal e economic e environmental analysis and multi-objective optimization of an ice thermal energy storage system for gas turbine cycle inlet air cooling. Energy10.1016/j.energy.2014.02.071 (2014).
Zejli, D., Ouammi, A., Sacile, R., Dagdougui, H. & Elmidaoui, A. An optimization model for a mechanical vapor compression desalination plant driven by a wind/PV hybrid system. Appl. Energy88, 4042–4054. 10.1016/j.apenergy.2011.04.031 (2011).
Yang, Y., Guo, S., Liu, D., Li, R. & Chu, Y. Operation optimization strategy for wind-concentrated solar power hybrid power generation system. Energy Convers Manag.160, 243–250. 10.1016/j.enconman.2018.01.040 (2018).
Yang, J., Yang, Z. & Duan, Y. Capacity optimization and feasibility assessment of solar-wind hybrid renewable energy systems in China. J. Clean. Prod.368, 133139. 10.1016/j.jclepro.2022.133139 (2022).
Lorestani, A. & Ardehali, M. M. Optimal integration of renewable energy sources for autonomous tri-generation combined cooling, heating and power system based on evolutionary particle swarm optimization algorithm. Energy10.1016/j.energy.2017.12.155 (2018).
Soheyli, S., Shafiei Mayam, M. H. & Mehrjoo, M. Modeling a novel CCHP system including solar and wind renewable energy resources and sizing by a CC-MOPSO algorithm. Appl. Energy184, 375–395. 10.1016/j.apenergy.2016.09.110 (2016).
Ji, W. et al. Thermodynamic analysis of a novel hybrid wind-solar-compressed air energy storage system. Energy Convers Manag.142, 176–187. 10.1016/j.enconman.2017.02.053 (2017).
Assareh, E., Assareh, M., Mojtaba, S. & Jalilinasrabady, S. An extensive thermo-economic evaluation and optimization of an integrated system empowered by solar-wind-ocean energy converter for electricity generation – Case study : Bandar Abas, Iran. Therm. Sci. Eng. Prog.25, 100965. 10.1016/j.tsep.2021.100965 (2021).
Ahmadi, P., Dincer, I. & Rosen, M. A. Exergy, exergoeconomic and environmental analyses and evolutionary algorithm based multi-objective optimization of combined cycle power plants. Energy36, 5886–5898. 10.1016/j.energy.2011.08.034 (2011).
Ebadollahi, M., Rostamzadeh, H., Ghaebi, H. & Amidpour, M. Exergoeconomic analysis and optimization of innovative cascade bi-evaporator electricity / cooling cycles with two adjustable cooling temperatures. Appl. Therm. Eng.152, 890–906. 10.1016/j.applthermaleng.2019.02.110 (2019).
Ebrahimi-moghadam, A., Jabari, A. & Farzaneh-gord, M. Comprehensive techno-economic and environmental sensitivity analysis and multi-objective optimization of a novel heat and power system for natural gas city gate stations. J. Clean. Prod.262, 121261. 10.1016/j.jclepro.2020.121261 (2020).
Ebrahimi, M. & Ahookhosh, K. Integrated energy-exergy optimization of a novel micro-CCHP cycle based on MGT-ORC and steam ejector refrigerator. Appl. Therm. Eng.102, 1206–1218. 10.1016/j.applthermaleng.2016.04.015 (2016).
Liu, L. et al. Thermodynamic analysis of NH 3 / CO 2 cascade refrigeration system with thermosyphon refrigerant cooling screw compressor motor. Int. J. Refrig.130, 1–13. 10.1016/j.ijrefrig.2021.06.032 (2021).
Li, W. Simpli fied steady-state modeling for variable speed compressor. Appl. Therm. Eng.50, 318–326. 10.1016/j.applthermaleng.2012.08.041 (2013).
Shen, J., Chen, W., Yan, S., Zhou, M. & Liu, H. Study on the noise reduction methods for a semi-hermetic variable frequency twin-screw refrigeration compressor Étude sur les méthodes d ’ insonorisation pour un compresseur frigorifique semi-hermétique à fréquence variable et à double vis. Int. J. Refrig.125, 1–12. 10.1016/j.ijrefrig.2020.12.029 (2021).
Yu, F. W. Ã. & Chan, K. T. Modelling of the coefficient of performance of an air-cooled screw chiller with variable speed condenser fans. Build. Environ.41, 407–417. 10.1016/j.buildenv.2005.02.002 (2006).
Li, X., Li, Y., Seem, J. E. & Li, P. Dynamic modeling and self-optimizing operation of chilled water systems using extremum seeking control. Energy Build.58, 172–182. 10.1016/j.enbuild.2012.12.010 (2013).
Sanaye, S. & Hajabdollahi, H. Multi-objective optimization of shell and tube heat exchangers. Appl. Therm. Eng.30, 1937–1945. 10.1016/j.applthermaleng.2010.04.018 (2010).
Jain, V., Sachdeva, G. & Kachhwaha, S. S. NLP model based thermoeconomic optimization of vapor compression – absorption cascaded refrigeration system. Energy Convers Manag.93, 49–62. 10.1016/j.enconman.2014.12.095 (2015).
Kalac, S. & Liu, H. Heat exchengers and thermal design 2nd edn. (CRC Press LLC, 2002).
Jain, V., Sachdeva, G. & Kachhwaha, S. S. Energy, exergy, economic and environmental (4E) analyses based comparative performance study and optimization of vapor compression-absorption integrated refrigeration system. Energy91, 816–832. 10.1016/j.energy.2015.08.041 (2015).
Naik, B. K. & Muthukumar, P. A novel approach for performance assessment of mechanical draft wet cooling towers. Appl. Therm. Eng.10.1016/j.applthermaleng.2017.04.042 (2017).
Alasseri, R. Measurable energy savings of installing variable frequency drives for cooling towers ’ fans, compared to dual speed motors. Energy Build.67, 261–266. 10.1016/j.enbuild.2013.07.081 (2013).
Liao, J., Xie, X., Nemer, H., Claridge, D. E. & Culp, C. H. A simpli fied methodology to optimize the cooling tower approach temperature control schedule in a cooling system. Energy Convers Manag.199, 111950. 10.1016/j.enconman.2019.111950 (2019).
Ruiz, J., Navarro, P., Hernández, M., Lucas, M. & Kaiser, A. S. Thermal performance and emissions analysis of a new cooling tower prototype. Appl. Therm. Eng.206, 118065. 10.1016/j.applthermaleng.2022.118065 (2022).
Dehaghani, S. T. & Ahmadikia, H. Retrofit of a wet cooling tower in order to reduce water and fan power consumption using a wet / dry approach. Appl. Therm. Eng.10.1016/j.applthermaleng.2017.07.069 (2017).
Zargar, A. et al. Numerical analysis of a counter-flow wet cooling tower and its plume. Int. J. Thermofluids14, 100139. 10.1016/j.ijft.2022.100139 (2022).
Merkel V-F, V.D.I.F. Verdunstungskühlung, no. 275, Verdunstungskuhlung. Berlin, Germany: VDI Forschungsarbeiten (1925).
Guo, Y., Wang, F., Jia, M. & Zhang, S. Parallel hybrid model for mechanical draft counter flow. Appl. Therm. Eng.10.1016/j.applthermaleng.2017.07.138 (2017).
Kloppers, J. C. Cooling tower performance evaluation : Merkel Poppe, and e -NTU methods of analysis. J. Eng. Gas Turbine Power127, 1–7. 10.1115/1.1787504 (2014).
Halasz, B. A general mathematical model of evaporative cooling devices. Revue Générale de Thermique37(4), 245–255. 10.1016/S0035-3159(98)80092-5 (1998).
Xu, Y. et al. Exergetic and economic analyses of a novel modified solar-heat-powered ejection-compression refrigeration cycle comparing with conventional cycle. Energy Convers Manag.168, 107–118. 10.1016/j.enconman.2018.04.098 (2018).
Moghimi, M., Emadi, M., Ahmadi, P. & Moghadasi, H. 4E analysis and multi-objective optimization of a CCHP cycle based on gas turbine and ejector refrigeration. Appl. Therm. Eng.141, 516–530. 10.1016/j.applthermaleng.2018.05.075 (2018).
Bejan, A., Tsatsaronis, G. & Moran, M. Thermal design and optimization (John Wiley & Songg, INC, 1996).
Khalilzadeh, S. & Hossein, N. A. Utilization of waste heat of a high-capacity wind turbine in multi effect distillation desalination: Energy, exergy and thermoeconomic analysis. Desalination439, 119–137. 10.1016/j.desal.2018.04.010 (2018).
Ahmadzadeh, A., Salimpour, M. R. & Sedaghat, A. Analyse thermique et exergoéconomique d’un nouveau système solaire combinant production d’électricité et de froid par éjecteur. Int. J. Refrig.83, 143–156. 10.1016/j.ijrefrig.2017.07.015 (2017).
Wang, S. Air conditioning and refrigeration. In CRC handbook of mechanical engineering (ed. Kreith, F.) (CRC Press, 1998). 10.1201/NOE0849397516-10.
Liu, X. et al. Energy, exergy, economic and environmental (4E) analysis of an integrated process combining CO2 capture and storage, an organic Rankine cycle and an absorption refrigeration cycle. Energy Convers Manag.210, 112738. 10.1016/j.enconman.2020.112738 (2020).
Ghafurian, M. M. & Niazmand, H. New approach for estimating the cooling capacity of the absorption and compression chillers in a trigeneration system. Int. J. Refrig.140–7007(17), 30480–30482. 10.1016/j.ijrefrig.2017.11.026 (2017).
Xu, Y., Li, Z., Chen, H. & Lv, S. Assessment and optimization of solar absorption-subcooled compression hybrid cooling system for cold storage. Appl. Therm. Eng.180, 115886. 10.1016/j.applthermaleng.2020.115886 (2020).
Rostami, S., Rostamzadeh, H. & Fatehi, R. A new wind turbine driven trigeneration system applicable for humid and windy areas, working with various nano fl uids. J. Clean. Prod.296, 126579. 10.1016/j.jclepro.2021.126579 (2021).
Mohamadi, H., Saeedi, A., Firoozi, Z., Sepasi, S. & Veisi, S. Heliyon Assessment of wind energy potential and economic evaluation of four wind turbine models for the east of Iran. Heliyon7, e07234. 10.1016/j.heliyon.2021.e07234 (2021). PubMed PMC
Ehyaei, M. A., Ahmadi, A. & Rosen, M. A. Energy, exergy, economic and advanced and extended exergy analyses of a wind turbine. Energy Convers Manag.183, 369–381. 10.1016/j.enconman.2019.01.008 (2019).
Diaf, S., Belhamel, M., Haddadi, M. & Louche, A. Technical and economic assessment of hybrid photovoltaic / wind system with battery storage in Corsica island. Energy Policy36, 743–754. 10.1016/j.enpol.2007.10.028 (2008).
Ayodele, T. R., Ogunjuyigbe, A. S. O. & Amusan, T. O. Wind power utilization assessment and economic analysis of wind turbines across fifteen locations in the six geographical Zones of Nigeria. J. Clean. Prod.959–6526(16), 30328–30336. 10.1016/j.jclepro.2016.04.060 (2016).
Makkeh, S. A., Ahmadi, A., Esmaeilion, F. & Ehyaei, M. A. Energy, exergy and exergoeconomic optimization of a cogeneration system integrated with parabolic trough collector-wind turbine with desalination. J. Clean. Prod.10.1016/j.jclepro.2020.123122 (2020).
Yu, H., Engelkemier, S. & Gençer, E. Process improvements and multi-objective optimization of compressed air energy storage ( CAES ) system. J. Clean. Prod.335, 130081. 10.1016/j.jclepro.2021.130081 (2022).
Bechtler, H., Browne, M. W., Bansal, P. K. & Kecman, V. New approach to dynamic modelling of vapour-compression liquid chillers: Artificial neural networks. Appl. Therm. Eng.21(9), 941–953. 10.1016/S1359-4311(00)00093-4 (2001).
Beghi, A., Cecchinato, L., Cosi, G. & Rampazzo, M. A PSO-based algorithm for optimal multiple chiller systems operation. Appl. Therm. Eng.32, 31–40. 10.1016/j.applthermaleng.2011.08.008 (2012).
Jain, V., Sachdeva, G., Kachhwaha, S. S. & Patel, B. Thermo-economic and environmental analyses based multi-objective optimization of vapor compression-absorption cascaded refrigeration system using NSGA-II technique. Energy Convers Manag.113, 230–242. 10.1016/j.enconman.2016.01.056 (2016).
Wang, L. et al. Thermodynamic analysis and optimization of pumped thermal–liquid air energy storage (PTLAES). Appl. Energy332, 120499. 10.1016/j.apenergy.2022.120499 (2023).
Alberto Dopazo, J., Fernández-Seara, J., Sieres, J. & Uhía, F. J. Theoretical analysis of a CO2-NH3 cascade refrigeration system for cooling applications at low temperatures. Appl. Therm. Eng.29, 1577–1583. 10.1016/j.applthermaleng.2008.07.006 (2009).
Wu, H. et al. Thermodynamic analysis and operation optimization on a novel heating and cooling integrated system with twin screw compressor and intercooler. Int. J. Refrig.131, 359–367. 10.1016/j.ijrefrig.2021.07.043 (2021).
https://www.data.irimo.ir/