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Research to estimate energy efficiency of a ventilation and air conditioning heat pump system inside a production premise with ventilation air recovery

Myhailo Kostiantynovych Bezrodny Tymofii Oleksiyovych Misiura
Archives of Thermodynamics | 2021 | vol. 42 | No 4 | 69-86 | DOI: 10.24425/ather.2021.139651
Keywords Air conditioning Heat pump system Refrigeration efficiency recuperation
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Abstract

The research provides a thermodynamic analysis of the theoretical model of a ventilation and air conditioning heat pump system with the ventilation air cold energy recovery depending on outside air parameters, the recovery efficiency and characteristics of a premise. A confectionery production workshop was taken as a prototype where technological conditions (temperature and humidity) must be maintained during the warm season. Calculations using the method of successive approximations to estimate air parameters at system’s nodal points were conducted. It allowed to determine theoretical refrigeration efficiency of the studied system and proved advantages of heat recuperation for smaller energy consumption. The model can be applied for design of heating, ventilation, and air conditioning units which work as a heat pump. The studied system has the highest energy efficiency in the area of relatively low environment temperatures and relative humidity which is suitable for countries with temperate continental climates characterized by low relative humidity.
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Bibliography

[1] Zhang J., Zhang H.-H., He Y.-L., Tao W.-Q.: A comprehensive review on advances and applications of industrial heat pumps based on the practices in China. Appl. Energ. 178(2016), 800–825.
[2] Chwieduk D.: Analysis of utilization of renewable energies as heat sources for heat pumps in building sector. Renew. Energ. 9(1996), 720–723.
[3] Khrustaliov B.M.: Heat Supply and Ventilation. ASV, Moscow 2007 (in Russian).
[4] Mazzeo D.: Solar and wind assisted heat pump to meet the building air conditioning and electric energy demand in the presence of an electric vehicle charging station and battery storage. J. Clean. Prod. 213(2019), 1228–1250.
[5] Chwieduk B., Chwieduk D.: Analysis of operation and energy performance of a heat pump driven by a PV system for space heating of a single family house in Polish conditions. Renew. Energ. 165(2021), 117–126.
[6] Bezrodny M., Prytula N., Tsvietkova M.: Efficiency of heat pump systems of air conditioningfor removing excessive moisture. Arch. Thermodyn. 40(2019), 2, 151–165.
[7] Bezrodny E.K., Misiura T.O.: The heat pump system for ventilation and air conditioning inside the production area with an excessive internal moisture generation. Eurasian Phys. Tech. J. 17(2020), 118–132.
[8] Adamkiewicz A., Nikonczuk P.: Waste heat recovery from the air preparation room in a paint shop. Arch. Thermodyn. 40(2019), 3, 229–241.
[9] Szreder M.: Investigations into the influence of functional parameters of a heat pump on its thermal efficiency. Teka. Commission of Motorization and Energetics in Agriculture 13(2013), 191–196.
[10] Redko A., Redko O., DiPippo R.: Low-Temperature Energy Systems with Applications of Renewable Energy. Academic Press, Elsevier, 2020.
[11] Morozjuk T.V.: The Theory of Chillers and Heat Pumps. Studija “Negociant”, Odessa 2006 (in Russian).
[12] Jaber S., Ezzat A.W.: Investigation of energy recovery with exhaust air evaporative cooling in ventilation system. Energ. Buildings 139(2017), 439–448.
[13] Bozhenko M.F.: Heat Sources and Heat Consumers. NTUU KPI “Politehnika”, Kyiv 2004 (in Ukrainian).
[14] State Building Standards of Ukraine DBN B.2.5-67: 2013, “Heating, ventilation and air conditioning”. Ministry of Regional Development, Construction and Housing of Ukraine, Kyiv 2013 (in Ukrainian).
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Authors and Affiliations

Myhailo Kostiantynovych Bezrodny
1
Tymofii Oleksiyovych Misiura
1
  1. National Technical University of Ukraine, Igor Sikorsky, Kyiv Polytechnic Institute, Prosp. Peremohy 37, 03056 Kyiv, Ukraine

Investigation of thermal-flow characteristics of the minichannel heat exchanger of variable louvers height

Artur Romaniak Michał Jan Kowalczyk Marcin Łęcki Artur Gutkowski Grzegorz Górecki
Archives of Thermodynamics | 2023 | vol. 44 | No 3 | 119-141 | DOI: 10.24425/ather.2023.147540
Keywords CFD Heat transfer Heat pump Louvered fins Minichannel heat exchanger
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Abstract

The numerical simulation of the heat transfer in the flow channels of the minichannel heat exchanger was carried out. The applied model was validated on the experimental stand of an air heat pump. The influence of louver heights was investigated in the range from 0 mm (plain fin) to 7 mm (maximum height). The set of simulations was prepared in Ansys CFX. The research was carried out in a range of air inlet velocities from 1 to 5 m/s. The values of the Reynolds number achieved in the experimental tests ranged from 93 to 486. The dimensionless factors, the Colburn factor and friction factor, were calculated to evaluate heat transfer and pressure loss, respectively. The effectiveness of each louver height was evaluated using the parameter that relates to the heat transfer and the pressure drop in the airflow. The highest value of effectiveness (1.53) was achieved by the louver height of 7 mm for the Reynolds number of around 290.
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Authors and Affiliations

Artur Romaniak
1
Michał Jan Kowalczyk
1
Marcin Łęcki
1
Artur Gutkowski
1
Grzegorz Górecki
1
  1. Lodz University of Technology, Zeromskiego 116, 90-924 Łódz, Poland

Testing of a price-based system for power balancing on real-life HVAC installation in real life

Weronika Radziszewska Marcin A. Bugaj Mirosław Łuniewski Gerwin Hoogsteen Patryk Chaja Sebastian Bykuć
Bulletin of the Polish Academy of Sciences Technical Sciences | 2022 | 70 | 2 | e140690 | DOI: 10.24425/bpasts.2022.140690
Keywords distributed management heat pump HVAC power management waterloop
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Abstract

HVAC systems use a substantial part of the whole energy usage of buildings. The optimizing of their operation can greatly affect the power use of a building, making them an interesting subject when trying to save energy. However, this should not affect the comfort of the people inside. Many approaches aim to optimize the operation of the heating and cooling system; in this paper, we present an approach to steer the heat pumps to reduce energy usage while aiming to maintain a certain level of comfort. For this purpose, we employ a market-based distributed method for power-balancing. To maintain the comfort level, the market-based distributed system assigns each device a cost-curve, parametrized with the current temperature of the room. This allows the cost to reflect the urgency of the HVAC operation. This approach was tested in a real-world environment: we use 10 heat pumps responsible for temperature control in 10 comparable-sized rooms. The test was performed for 3 months in summer. We limited the total peak power, and the algorithm balanced the consumption of the heat pumps with the available supply. The experiments showed that the system successfully managed to operate within the limit (lowering peak usage), and - to a certain point - reduce the cost without significantly deteriorating the working conditions of the occupants of the rooms. This test allowed us to estimate the minimal peak power requirement for the tested set-up that will still keep the room temperatures in or close to comfortable levels. The experiments show that a fully distributed market-based approach with parametrized cost functions can be used to limit peak usage while maintaining temperatures.
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Authors and Affiliations

Weronika Radziszewska
1
ORCID: ORCID
Marcin A. Bugaj
2
ORCID: ORCID
Mirosław Łuniewski
1
ORCID: ORCID
Gerwin Hoogsteen
3
ORCID: ORCID
Patryk Chaja
1
ORCID: ORCID
Sebastian Bykuć
1
ORCID: ORCID
  1. Institute of Fluid-Flow Machinery Polish Academy of Science, ul. Fiszera 14, 80-231 Gdańsk, Poland
  2. Faculty of Power and Aeronautical Engineering, Warsaw University of Technology, ul. Nowowiejska 21/25, 00-665 Warsaw, Poland
  3. Department of Electrical Engineering, Mathematica and Computer Science,University of Twente, PO BOX 217, 7500 AE Enschede, Netherlands

Digital twins application in control systems for distributed generation of heat and electric energy

Makhsud Mansurovich Sultanov Edik Koirunovich Arakelyan Ilia Anatolevich Boldyrev Valentina Sergeevna Lunenko Pavel Dmitrievich Menshikov
Archives of Thermodynamics | 2021 | vol. 42 | No 2 | 89-101 | DOI: 10.24425/ather.2021.137555
Keywords Solar collectors Heat pump Renewable energy complex Digital doubles Efficiency Reliability Safety
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Abstract

By the emergence of distributed energy resources, with their associated communication and control complexities, there is a need for an efficient platform that can digest all the incoming data and ensure the reliable operation of the power system, which can be achieved by using digital twins. The paper discusses the advantages of using digital twins in the development of control systems and operation of distributed heat and electric power generation facilities. The possibilities of using the digital doubles for increasing the efficiency of the considered objects is presented as the example of optimizing the configuration of a control system of solar collectors in the presence of heat losses in pipelines of the external circuit. Further, the total balance consumed and generated electric and heat energy are presented. Examples of algorithms for protecting equipment to improve security are given, and the possibilities of improving the reliability of distributed power systems are considered. The system use of the digital twins provides the possibility of developing and debugging control algorithms, which increase the efficiency, reliability and safety of control objects, including distributed thermal and electrical power generation complexes.
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Authors and Affiliations

Makhsud Mansurovich Sultanov
1
Edik Koirunovich Arakelyan
1
Ilia Anatolevich Boldyrev
1
Valentina Sergeevna Lunenko
1
Pavel Dmitrievich Menshikov
1
  1. National Research University MPEI, Krasnokazarmennaya 17, Moscow, 111250 Russia

Study of the heat pump for a passenger electric vehicle based on refrigerant R744

Maria Laura Canteros Jiri Polansky
Archives of Thermodynamics | 2022 | vol. 43 | No 2 | 17-36 | DOI: 10.24425/ather.2022.141976
Keywords R744 (CO2) Heat pump Battery electric vehicle Thermal comfort
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Abstract

Energy management plays a crucial role in cabin comfort as well as enormously affects the driving range. In this paper energy balances contemplating the implementation of a heat pump and an expansion device in battery electric vehicles are elaborated, by comparing the performances of refrigerants R1234yf and R744, from –20°C to 20°C. This work calculates the coefficient of performance, energy requirements for ventilation (from 1 to 5 people in the cabin) and energy required with the implementation of a heat pump, with the employment of a code in Python with the aid of Cool- Prop library. The work ratio is also estimated if the work recovery device recuperates the work during the expansion. Comments on the feasibility of the implementation are as well explicated. The results of the analysis show that the implementation of an expansion device in an heat pump may cover the energy requirement of the compressor from 27% to more than 35% at 20°C in cycles operating with R744, and from 15% to more than 20% with refrigerant R1234yf, considering different compressor efficiencies. At –20°C, it would be possible to recuperate between around 30 and 24%. However, the risk of suction when operating with R1234yf at ambient temperatures below –10°C shows that the heat pump can only operate with R744. Thus, it is the only refrigerant that achieves the reduction of energy consumption at these temperatures.
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Bibliography

  1. Global electric car sales by key markets, 2010-2020 – Charts – Data & Statistics IEA, https://www.iea.org/data-and-statistics/charts/global-electric-car-sales-by-key- markets-2015-2020 (accessed 17 March 2021).
  2. Rietmann N., Hügler B., Lieven T.: Forecasting the trajectory  of  electric  vehicle sales and the consequences for worldwide CO2 emissions. J. Clean. Prod. 261(2020), 121038. https://doi.org/10.1016/j.jclepro.2020.121038.
  3. Greaves , Backman H., Ellison A.B.: An empirical assessment of the feasibility of battery electric vehicles for day-to-day driving. Transport. Res. A-Pol. 66(2014), 226–237. https://doi.org/10.1016/j.tra.2014.05.011.
  4. Kempton W.: Electric vehicles: Driving range. Energ. 1 (2016), 1–2. https:// doi.org/ 10.1038/nenergy.2016.131.
  5. Klamut : Attitude towards electric vehicles. Research  on the students of a tech- nical university. Zeszyty Naukowe Instytutu Gospodarki Surowcami Mineralnymi PAN 107(2018), 105–118 (in Polish). https://doi.org/10.24425/123719.
  6. Varga O., Sagoian A., Mariasiu F.: Prediction of electric vehicle range: A comprehensive review of current issues and challenges. Energies 12(2019), 946. https://doi.org/10.3390/en12050946.
  7. Lajunen , Suomela   J.:   Evaluation   of   energy   storage   system   requirements for hybrid mining loaders. IEEE T. Veh. Technol. 61(2012), 3387–3393. https:// doi.org/10.1109/TVT.2012.2208485.
  8. Garg ,  Chen  F.,  Zhang  J.: State-of-the-art of designs studies for batteries packs  of electric vehicles. In: Proc. IET Int. Conf. on Intelligent and Connected Vehicles (ICV 2016). https://doi.org/10.1049/cp.2016.1181.
  9. Hannan M.A., Hoque M.M., Hussain A., Yusof Y., Ker P.J.: State-of-the-art and energy management system of lithium-ion batteries in electric vehicle applica- tions: Issues and recommendations. IEEE Access 6(2018), 19362–19378. https://org/10.1109/ACCESS.2018.2817655.
  10. Petitjean C., Guyonvarch G., Benyahia M., Beauvis R.: TEWI analysis for different automotive air conditioning systems. In: Proc. The Future Car Congress 2000, 2000-01–1561. https://doi.org/10.4271/2000-01-1561.
  11. Guyonvarch G., Aloup C., Petitjean C., De  Monts  De  Savasse :  42  V  electric air conditioning systems (E-A/CS) for  low  emissions,  architecture,  comfort and safety of next generation vehicles. In: Proc. The Future Transportation Tech- nology Conf. & Expo. 2001, 2001-01–2500. https://doi.org/10.4271/2001-01-2500.
  12. Bashirpour-Bonab H.: Thermal behavior of lithium batteries used in electric  ve- hicles using phase change materials. Int. J. Energ. Res. 44(2020), 12583–12591. https://doi.org/10.1002/er.5425.
  13. Karimi G., Li X.: Thermal management of lithium-ion batteries for electric vehicles. Int. J. Energ. Res. 37(2013), 13–24. https://doi.org/10.1002/er.1956.
  14. Kizilel R., Lateef A., Sabbah R., Farid M., Selman J.R., Al-Hallaj S.:  Passive control of temperature excursion and uniformity in high-energy Li-ion bat- tery packs at high current and ambient temperature. J. Power Sources 183(2008), 1, 370–375. https://doi.org/10.1016/j.jpowsour.2008.04.050.
  15. Agarwal ,  Sarviya  R.M.:  Characterization  of  Commercial  Grade  Paraffin  wax as Latent Heat Storage material for Solar dryers. Materials Today 4(2017), 779–789, Proc. 5th Int. Conf. on Materials Processing and Characterization (ICMPC 2016). https://doi.org/10.1016/j.matpr.2017.01.086.
  16. Ettouney H., Alatiqi , Al-Sahali M., Al-Hajirie K.: Heat transfer enhance- ment in energy storage in spherical capsules filled with paraffin wax and metal beads. Energ. Convers. Manage. 47(2006), 211–228. https://doi.org/10.1016/j.enconman. 2005.04.003.
  17. Heath A.: Amendment to the Montreal protocol on substances that  deplete  the ozone layer (Kigali amendment). Int. Legal Mater. 56(2017), 193–205. https:// doi.org/10.1017/ilm.2016.2.
  18. Lee Y., Jung D.: A brief performance comparison  of  R1234yf  and  R134a  in  a bench tester for automobile applications. Appl. Therm. Eng. 35(2012), 240–242. https://doi.org/10.1016/j.applthermaleng.2011.09.004.
  19. Ozgur A.E., Kabul A., Kizilkan : Exergy  analysis  of  refrigeration  systems using an alternative refrigerant (hfo-1234yf) to R-134a. Int. J. Low-Carb. Technol. 9(2014), 56–62. https://doi.org/10.1093/ijlct/cts054.
  20. Vaghela K.: Comparative evaluation of an automobile air – conditioning  system using R134a and its alternative refrigerants. Energy Proced. 109(2017), 153–160, Int. Conf. on Recent Advancement in Air Conditioning and Refrigeration, RAAR 2016, 10-12 November 2016, Bhubaneswar. https://doi.org/ 10.1016/j.egypro. 2017. 03.083.
  21. Reasor P., Aute V., Radermacher R.: Refrigerant R1234yf performance com- parison investigation. Refrigeration and Air Conditioning Conference 8, 2010.
  22. Cho H., Lee H., Park : Performance characteristics of an automobile air condi- tioning system with internal heat exchanger using refrigerant R1234yf. Appl. Therm. Eng. 61(2013), 563–569. https://doi.org/10.1016/j.applthermaleng.2013.08.030.
  23. Direk M., Kelesoglu A., Akin A.: Drop-in  performance  analysis  and  effect  of IHX for an automotive air conditioning system with R1234yf as a replacement of R134a. SV-JME 63(2017), 314–319. https://doi.org/10.5545/sv-jme.2016.4247.
  24. Feng L., Hrnjak P.: Experimental Study of an Air Conditioning-Heat Pump Sys- tem for Electric Vehicles. In: Proc: SAE 2016 World Exhibit., 2016-01–0257. https://doi.org/10.4271/2016-01-0257.
  25. Wu , Zhou G., Wang M.: A comprehensive assessment of refrigerants for cabin heating and cooling on electric vehicles. Appl. Therm. Eng. 174(2020), 115258. https://doi.org/10.1016/j.applthermaleng.2020.115258.
  26. Maina P., Huan Z.: A review of carbon dioxide as a refrigerant in refrigeration technology. Afr. J. Sci. 111(2015). https://doi.org/10.17159/sajs.2015/20140258.
  27. Song X., Lu D., Lei Q., Cai Y., Wang , Shi J., Chen J.: Experimental study   on heating performance of a CO2 heat pump system for an electric bus. Appl. Therm. Eng. 190(2021), 116789. https://doi.org/10.1016/j.applthermaleng.2021.116789.
  28. Wu D., Hu B., Wang Z.: Vapor compression heat pumps with pure low-GWP refrigerants. Renew. Sust. Energ. Rev. 138(2021), 110571. https://doi.org/10.1016/ j.rser.2020.110571.
  29. Lorentzen G.: Revival of carbon dioxide as a refrigerant. International Journal of Refrigeration 17(1994), 292–301. https://doi.org/10.1016/0140-7007(94)90059-0.
  30. Großmann H.: Comparing the refrigerant R1234yf and CO2. ATZ Worldw 118(2016), 70. https://doi.org/10.1007/s38311-016-0119-0.
  31. Ma Y., Liu Z., Tian H.: A review of transcritical carbon dioxide heat pump and refrigeration cycles. Energy 55(2013), 156–172. https://doi.org/10.1016/j.energy. 03.030.
  32. Li W., Liu Y., Liu R., Wang , Shi J., Yu Z., Cheng L., Chen J.L.: Perfor- mance evaluation of secondary loop low-temperature heat pump system for frost pre- vention in electric vehicles. Appl. Therm. Eng. 182(2021), 115615. https://doi.org/ 10.1016/j.applthermaleng.2020.115615.
  33. Menken J.C., Ricke M., Weustenfeld  A.,  Koehler  J.:  Simulative  analysis of secondary loop automotive refrigeration systems operated with an HFC and carbon dioxide. SAE Int. J. Passeng. Cars-Mech. Syst. 9(2016), 434–440. https://doi.org/ 10.4271/2016-01-9107.
  34. Wang , Yu B., Hu J., Chen L., Shi J., Chen J.: Heating performance char- acteristics of CO2 heat pump system for electrical vehicle in a cold climate. Int. J.Refrig. 85(2018), 27–41. https://doi.org/10.1016/j.ijrefrig.2017.09.009.
  35. Wang , Wang D., Yu,B., Shi J., Chen J.: Experimental and numerical in- vestigation of a CO2 heat pump system for electrical vehicle with series gas coolerconfiguration. Int. J. Refrig. 100(2019), 156–166. https://doi.org/10.1016/j.ijrefrig. 2018.11.001.
  36. Bruno F., Belusko M., Halawa : CO2 refrigeration and heat pump systems – A comprehensive review. Energies 12(2019), 15, 2959. https://doi.org/10.3390/ en12152959.
  37. Baek J.S., Groll E.A., Lawless B.: Piston-cylinder work producing expansion device in a transcritical carbon dioxide cycle. Part I: experimental investigation. Int. J. Refrig. 28(2005), 141–151. https://doi.org/10.1016/j.ijrefrig.2004.08.006.
  38. Ferrara G., Ferrari L., Fiaschi , Galoppi  G.,  Karellas  S.,  Secchi  R.,  Tempesti D.: A small power recovery expander for heat pump COP improvement. Energ. Proced. 81(2015), 1151–1159, 69th Conf. Ital. Therm. Eng. Assoc., ATI 2014. https://doi.org/10.1016/j.egypro.2015.12.140.
  39. Kohsokabe H., Funakoshi S., Tojo K., Nakayama , Kohno K., Kurashige  K.: Basic operating characteristics of CO2 refrigeration cycles with expander- compressor unit 10 (2006). 
  40. Specific Heat Capacities of Air – (Updated 7/26/08). https://www.ohio.edu/mecha nical/thermo/property_tables/air/air_Cp_Cv.html (accessed 6 March 2021).
  41. Abas N., Kalair A.R., Khan  ,  Haider  A.,  Saleem  Z.,  Saleem  M.S.:  Natu-  ral and synthetic refrigerants, global warming: A review. Renew. Sust. Energ. Rev. 90(2018), 557–569. https://doi.org/10.1016/j.rser.2018.03.099.
  42. Bell H., Wronski J., Quoilin S., Lemort V.: Pure and pseudo-pure fluid thermophysical property evaluation and the open-source thermophysical property li- brary CoolProp. Ind. Eng. Chem. Res. 53(2014), 6, 2498–2508. https://doi.org/ 10.1021/ie4033999.
  43. Richter M., McLinden M.O., Lemmon E.W.: Thermodynamic Properties of 2,3,3,3-Tetrafluoroprop-1-ene (R1234yf): Vapor Pressure and p–ρ–T Measurements and an Equation of State. ACS Publications (2011). https://doi.org/10.1021/ je200369m.
  44. Span , Wagner W.: A new equation  of  state  for  carbon  dioxide  covering  the fluid region from  the triple-point temperature  to 1100 K at pressures  up to 800 MPa.  J. Phys. Chem. Ref. Data 25(1996), 1509–1596. https://doi.org/10.1063/1.555991.
  45. Fukuda ,  Kojima  H.,  Kondou  C.,  Takata  N.,  Koyama S.:  Experimen-   tal assessment on performance of a heat pump cycle using R32/R1234yf and R744/R32/R1234yf. In; Proc. Int. Refrigeration and Air Conditioning Conf. 2016.
  46. Shin Y., Cho H.: Performance comparison of a truck refrigeration system  with R404A, R134a, R1234yf, and R744 refrigerants under frosting conditions. Int. J. Air-Cond. Ref. 24(2016), 1650005.https://doi.org/10.1142/S201013251650005X.
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Authors and Affiliations

Maria Laura Canteros
1
Jiri Polansky
2
  1. Czech Technical University in Prague, Jugoslávských partyzánu 1580/3, 160 00 Prague 6 – Dejvice, Czech Republic
  2. ESI Group, Brojova 16, 326 00 Plzen, Czech Republic

Adsorption chiller in a combined heating and cooling system: simulation and optimization by neural networks

Jarosław Krzywanski Karol Sztekler Marcin Bugaj Wojciech Kalawa Karolina Grabowska Patryk Robert Chaja Marcin Sosnowski Wojciech Nowak Łukasz Mika Sebastian Bykuć
Bulletin of the Polish Academy of Sciences Technical Sciences | 2021 | 69 | 3 | e137054 | DOI: 10.24425/bpasts.2021.137054
Keywords adsorption heat pumps polygeneration cooling capacity low-grade thermal energy artificial neural networks soft computing
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Abstract

Adsorption cooling and desalination technologies have recently received more attention. Adsorption chillers, using eco-friendly refrigerants, provide promising abilities for low-grade waste heat recovery and utilization, especially renewable and waste heat of the near ambient temperature. However, due to the low coefficient of performance (COP) and cooling capacity (CC) of the chillers, they have not been widely commercialized. Although operating in combined heating and cooling (HC) systems, adsorption chillers allow more efficient conversion and management of low-grade sources of thermal energy, their operation is still not sufficiently recognized, and the improvement of their performance is still a challenging task. The paper introduces an artificial intelligence (AI) approach for the optimization study of a two-bed adsorption chiller operating in an existing combined HC system, driven by low-temperature heat from cogeneration. Artificial neural networks are employed to develop a model that allows estimating the behavior of the chiller. Two crucial energy efficiency and performance indicators of the adsorption chiller, i.e., CC and the COP, are examined during the study for different operating sceneries and a wide range of operating conditions. Thus this work provides useful guidance for the operating conditions of the adsorption chiller integrated into the HC system. For the considered range of input parameters, the highest CC and COP are equal to 12.7 and 0.65 kW, respectively. The developed model, based on the neurocomputing approach, constitutes an easy-to-use and powerful optimization tool for the adsorption chiller operating in the complex HC system.
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Bibliography

  1.  S. Moser and S. Lassacher, “External use of industrial waste heat – An analysis of existing implementations in Austria”, J. Clean Prod. 264, 121531 (2020).
  2.  J. Krzywanski, K. Grabowska, F. Herman, P. Pyrka, M. Sosnowski, T. Prauzner, and W. Nowak, “Optimization of a three-bed adsorption chiller by genetic algorithms and neural networks”, Energy Conv. Manag. 153, 313‒322 (2017).
  3.  B. Rezaie and M.A. Rosen, “District heating and cooling: Review of technology and potential enhancements”, Appl. Energy 93, 2‒10 (2012).
  4.  A.P. Roskilly and M. Ahmad Al-Nimr, “Sustainable Thermal Energy Management”, Energy Conv. Manag. 159, 396‒397 (2018).
  5.  H. Lund, S. Werner, R. Wiltshire, S. Svendsen, J.E. Thorsen, F. Hvelplund, and B.V. Mathiesen, “4th Generation District Heating (4GDH): Integrating smart thermal grids into future sustainable energy systems”, Energy 68, 1‒11 (2014).
  6.  M. Widziński, P. Chaja, A. Andersen, M. Jaroszewska, S. Bykuć, and J. Sawicki, “Simulation of an alternative energy system for district heating company in the light of changes in regulations of the emission of harmful substances into the atmosphere”, Int. J. Sustain. Energy Plan. Manag. 24, 43‒56 (2019).
  7.  M. Chorowski and P. Pyrka, “Modelling and experimental investigation of an adsorption chiller using low-temperature heat from cogeneration”, Energy 92, 221‒229 (2015).
  8.  R. AL-Dadah, S. Mahmoud, E. Elsayed, P. Youssef, and F. Al-Mousawi, “Metal-organic framework materials for adsorption heat pumps”, Energy 190, 116356 (2020).
  9.  M. Sosnowski, “Evaluation of Heat Transfer Performance of a Multi-Disc Sorption Bed Dedicated for Adsorption Cooling Technology”, Energies 12, 4660 (2019).
  10.  A.S. Alsaman, A.A. Askalany, K. Harby, and M.S. Ahmed, “Performance evaluation of a solar-driven adsorption desalination-cooling system”, Energy 128, 196‒207 (2017).
  11.  A. Kulakowska, A. Pajdak, J. Krzywanski, K. Grabowska, A. Zylka, M. Sosnowski, M. Wesolowska, K. Sztekler, and W. Nowak, “Effect of Metal and Carbon Nanotube Additives on the Thermal Diffusivity of a Silica Gel-Based Adsorption Bed”, Energies 13, 1391 (2020).
  12.  J. Ling-Chin, H. Bao, Z. Ma, W. Taylor, and A. Paul Roskilly, “State-of-the-Art Technologies on Low-Grade Heat Recovery and Utilization in Industry”, in Energy Conversion – Current Technologies and Future Trends, eds. I.H. Al-Bahadly, IntechOpen, 2019.
  13.  K. Grabowska, J. Krzywanski, W. Nowak, and M. Wesolowska, “Construction of an innovative adsorbent bed configuration in the adsorption chiller – Selection criteria for effective sorbent-glue pair”, Energy 151, 317‒323 (2018).
  14.  K. Grabowska, M. Sosnowski, J. Krzywanski, K. Sztekler, W. Kalawa, A. Zylka, and W. Nowak, “The Numerical Comparison of Heat Transfer in a Coated and Fixed Bed of an Adsorption Chiller”, J. Therm. Sci. 27, 421‒426 (2018).
  15.  I.H. Al-Bahadly, Energy Conversion – Current Technologies and Future Trends, London, 2019.
  16.  J. Krzywanski, K. Grabowska, M. Sosnowski, A. Zylka, K. Sztekler, W. Kalawa, T. Wójcik, and W. Nowak, “An Adaptive Neuro-Fuzzy model of a Re-Heat Two-Stage Adsorption Chiller”, Therm. Sci. 23, 1053‒1063 (2019).
  17.  K.J. Chua, S.K. Chou, W.M. Yang, and J. Yan, “Achieving better energy-efficient air conditioning – A review of technologies and strategies”, Appl. Energy 104, 87‒104 (2013).
  18.  X.H. Li, X.H. Hou, X. Zhang, and Z.X. Yuan, “A review on development of adsorption cooling—Novel beds and advanced cycles”, Energy Conv. Manag. 94, 221‒232 (2015).
  19.  K. Sztekler, W. Kalawa, L. Mika, J. Krzywanski, K. Grabowska, M. Sosnowski, W. Nowak, T. Siwek, and A. Bieniek, “Modeling of a Combined Cycle Gas Turbine Integrated with an Adsorption Chiller”, Energies 13, 515 (2020).
  20.  Y.I. Aristov, I.S. Glaznev, and I.S. Girnik, “Optimization of adsorption dynamics in adsorptive chillers: Loose grains configuration”, Energy 46, 484‒492 (2012).
  21.  I.S. Girnik, A.D. Grekova, L.G. Gordeeva, and Yu.I. Aristov, “Dynamic optimization of adsorptive chillers: Compact layer vs. bed of loose grains”, Appl. Therm. Eng. 125, 823‒829 (2017).
  22.  U. Bau, N. Baumgärtner, J. Seiler, F. Lanzerath, C. Kirches, and A. Bardow, “Optimal operation of adsorption chillers: First implementation and experimental evaluation of a nonlinear model-predictive-control strategy”, Appl. Therm. Eng. 149, 1503‒1521 (2019).
  23.  M.B. Elsheniti, M.A. Hassab, and A.-E. Attia, “Examination of effects of operating and geometric parameters on the performance of a two-bed adsorption chiller”, Appl. Therm. Eng. 146, 674‒687 (2019).
  24.  J. Krzywanski, K. Grabowska, M. Sosnowski, A. Żyłka, K. Sztekler, W. Kalawa, T. Wójcik, and W. Nowak, “Modeling of a re-heat two- stage adsorption chiller by AI approach”, MATEC Web Conf. 240, 1‒3 (2018).
  25.  S. Narayanan, S. Yang, H. Kim, and E.N. Wang, “Optimization of adsorption processes for climate control and thermal energy storage”, Int. J. Heat Mass Transf. 77, 288‒300 (2014).
  26.  I.I. El-Sharkawy, H. AbdelMeguid, and B.B. Saha, “Towards an optimal performance of adsorption chillers: Reallocation of adsorption/ desorption cycle times”, Int. J. Heat Mass Transf. 63, 171‒182 (2013).
  27.  Q.W. Pan, R.Z. Wang, and L.W. Wang, “Comparison of different kinds of heat recoveries applied in adsorption refrigeration system”, Int. J. Refrig. 55, 37‒48 (2015).
  28.  R.P. Sah, B. Choudhury, R.K. Das, and A. Sur, “An overview of modelling techniques employed for performance simulation of low–grade heat operated adsorption cooling systems”, Renew. Sust. Energ. Rev. 74, 364‒376 (2017).
  29.  L. Rutkowski, Computational Intelligence: Methods and Techniques, Springer Science & Business Media (2008).
  30.  J. Szczepański, J. Klamka, K.M. Węgrzyn-Wolska, I. Rojek, and P. Prokopowicz, “Computational Intelligence and Optimization Techniques in Communications and Control”, Bull. Pol. Acad. Sci. Tech. Sci. 68(2), 181‒184 (2020).
  31.  B. Paprocki, A. Pregowska, and J. Szczepanski, “Optimizing information processing in brain-inspired neural networks”, Bull. Pol. Acad. Sci. Tech. Sci. 68(2), 225‒233 (2020).
  32.  A. Cichocki, T. Poggio, S. Osowski, and V. Lempitsky, “Deep Learning: Theory and Practice”, Bull. Pol. Acad. Sci. Tech. Sci. 66(6), 757‒759 (2018).
  33.  T. Poggio and Q. Liao, “Theory I: Deep networks and the curse of dimensionality”, Bull. Pol. Acad. Sci. Tech. Sci. 66(6), 761‒773 (2018).
  34.  T. Poggio and Q. Liao, “Theory II: Deep learning and optimization”, Bull. Pol. Acad. Sci. Tech. Sci. 66(6), 775‒787 (2018).
  35.  M. Figurnov, A. Sobolev, and D. Vetrov, “Probabilistic adaptive computation time”, Bull. Pol. Acad. Sci. Tech. Sci. 66(6), 811‒820 (2018).
  36.  V. Lebedev and V. Lempitsky, “Speeding-up convolutional neural networks: A survey”, Bull. Pol. Acad. Sci. Tech. Sci. 66(6), 799‒810 (2018).
  37.  S.C. Cagan, M. Aci, B.B. Buldum, and C. Aci, “Artificial neural networks in mechanical surface enhancement technique for the prediction of surface roughness and microhardness of magnesium alloy”, Bull. Pol. Acad. Sci. Tech. Sci. 67(4), 729‒739 (2019).
  38.  I. Rojek and E. Dostatni, “Machine learning methods for optimal compatibility of materials in ecodesign”, Bull. Pol. Acad. Sci. Tech. Sci. 68(2), 199‒206 (2020).
  39.  S. Osowski and K. Siwek, “Local dynamic integration of ensemble in prediction of time series”, Bull. Pol. Acad. Sci. Tech. Sci. 67(3), 517‒525 (2019).
  40.  J. Kurek, B. Świderski, S. Osowski, M. Kruk, and W. Barhoumi, “Deep learning versus classical neural approach to mammogram recognition”, Bull. Pol. Acad. Sci. Tech. Sci. 66(6), 831‒840 (2018).
  41.  Q. Zhao, Y. Qiu, G. Zhou, and A. Cichocki, “Comparative study on the classification methods for breast cancer diagnosis”, Bull. Pol. Acad. Sci. Tech. Sci. 66(6), 841‒848 (2018).
  42.  V. Osin, A. Cichocki, and E. Burnaev, “Fast multispectral deep fusion networks”, Bull. Pol. Acad. Sci. Tech. Sci. 66(6), 875‒889 (2018).
  43.  J. Jakubowski and J. Chmielińska, “Detection of driver fatigue symptoms using transfer learning”, Bull. Pol. Acad. Sci. Tech. Sci. 66(6), 869‒874 (2018).
  44.  P. Prokopowicz, D. Mikołajewski, K. Tyburek, and E. Mikołajewska, “Computational gait analysis for post-stroke rehabilitation purposes using fuzzy numbers, fractal dimension and neural networks”, Bull. Pol. Acad. Sci. Tech. Sci. 68(2), 191‒198 (2020).
  45.  B. Cieniawska, K. Pentoś, and D. Łuczycka, “Neural modeling and optimization of the coverage of the sprayed surface”, Bull. Pol. Acad. Sci. Tech. Sci. 68(3), 601‒608 (2020).
  46.  Y. Li, B. Zhang, and X. Xu, “Decoupling control for permanent magnet in-wheel motor using internal model control based on back- propagation neural network inverse system”, Bulletin of the Polish Academy of Sciences: Technical Science 66(6), 961‒972 (2018).
  47.  R. Korupczyński and J. Trajer, “Assessment of wind energy resources using artificial neural networks – case study at Łódź Hills”, Bull. Pol. Acad. Sci. Tech. Sci. 67, 115‒124 (2019).
  48.  J. Krzywanski, H. Fan, Y. Feng, A.R. Shaikh, M. Fang, and Q. Wang, “Genetic algorithms and neural networks in optimization of sorbent enhanced H2 production in FB and CFB gasifiers”, Energy Conv. Manag. 171, 1651‒1661 (2018).
  49.  J. Krzywanski, M. Wesolowska, A. Blaszczuk, A. Majchrzak, M. Komorowski, and W. Nowak, “The Non-Iterative Estimation of Bed- to-Wall Heat Transfer Coefficient in a CFBC by Fuzzy Logic Methods”, Procedia Eng. 157, 66‒71 (2016).
  50.  W. Muskała, J. Krzywański, R. Rajczyk, M. Cecerko, B. Kierzkowski, W. Nowak, and W. Gajewski, “Investigation of erosion in CFB boilers”, Rynek Energii 87, 97‒102 (2010).
  51.  W. Muskała, J. Krzywański, R. Sekret, and W. Nowak, “Model research of coal combustion in circulating fluidized bed boilers” Chem. Process Eng. 29, 473‒492 (2008).
  52.  A. Zylka, J. Krzywanski, T. Czakiert, K. Idziak, M. Sosnowski, K. Grabowska, T. Prauzner, and W. Nowak, “The 4th Generation of CeSFaMB in numerical simulations for CuO-based oxygen carrier in CLC system”, Fuel 255, 115776 (2019).
  53.  A. Błaszczuk and J. Krzywański, “A comparison of fuzzy logic and cluster renewal approaches for heat transfer modeling in a 1296 t/h CFB boiler with low level of flue gas recirculation”, Arch. Thermodyn. 38, 91‒122 (2017).
  54.  J. Krzywanski, M. Wesolowska, A. Blaszczuk, A. Majchrzak, M. Komorowski, and W. Nowak, “Fuzzy logic and bed-to-wall heat transfer in a large-scale CFBC”, Nt. J. Numer. Methods Heat Fluid Flow 28, 254‒266 (2018).
  55.  Machine learning software, Neural Designer. [Online] https://www.neuraldesigner.com/ (accessed on Jun 11, 2019).
  56.  J. Krzywanski, A. Blaszczuk, T. Czakiert, R. Rajczyk, and W. Nowak, “Artificial intelligence treatment of NOX emissions from CFBC in air and oxy-fuel conditions”, CFB-11: Proceedings of the 11th International Conference on Fluidized Bed Technology, 2014, pp. 619‒624.
  57.  J. Krzywański and W. Nowak, “Neurocomputing approach for the prediction of NOx emissions from CFBC in air-fired and oxygen-enriched atmospheres”, J. Power Technol.97, 75‒84 (2017).
  58.  Z. Salam, J. Ahmed, and B.S. Merugu, “The application of soft computing methods for MPPT of PV system: A technological and status review”, Appl. Energy107, 135‒148 (2013).
  59.  J. Krzywanski, “A General Approach in Optimization of Heat Exchangers by Bio-Inspired Artificial Intelligence Methods”, Energies 12, 4441 (2019).
  60.  J. Krzywanski and W. Nowak, “Modeling of heat transfer coefficient in the furnace of CFB boilers by artificial neural network approach”, Int. J. Heat Mass Transf. 55, 4246‒4253 (2012).
  61.  J. Krzywanski and W. Nowak, “Modeling of bed-to-wall heat transfer coefficient in a large-scale CFBC by fuzzy logic approach”, Int. J. Heat Mass Transf. 94, 327‒334 (2016).
  62.  A.K. Kar, “Bio inspired computing – A review of algorithms and scope of applications”, Expert Syst. Appl.59, 20‒32 (2016).
  63.  C.Y. Tso, C.Y.H. Chao, and S.C. Fu, “Performance analysis of a waste heat driven activated carbon based composite adsorbent – Water adsorption chiller using simulation model”, Int. J. Heat Mass Transf. 55, 7596‒7610 (2012).
  64.  L. Yang and W. Wang, “The heat transfer performance of horizontal tube bundles in large falling film evaporators”, Int. J. Refrig. 34, 303‒316 (2011).
  65.  W. Kalawa, K. Grabowska, K. Sztekler, J. Krzywański, M. Sosnowski, S. Stefański, T. Siwek, and W. Nowak, “Progress in design of adsorption refrigeration systems. Evaporators”, EPJ Web Conf. 213, 02035 (2019).
  66.  B.B. Saha, S. Koyama, J.B. Lee, K. Kuwahara, K.C.A. Alam, Y. Hamamoto, A. Akisawa, and T. Kashiwagi, “Performance evaluation of a low-temperature waste heat driven multi-bed adsorption chiller”, Int. J. Multiph. Flow 29, 1249‒1263 (2003).
  67.  J. Jeon, S. Lee, D. Hong, and Y. Kim, “Performance evaluation and modeling of a hybrid cooling system combining a screw water chiller with a ground source heat pump in a building”, Energy 35, 2006‒2012 (2010).
  68.  B.B. Saha, E.C. Boelman, and T. Kashiwagi, “Computer simulation of a silica gel-water adsorption refrigeration cycle – the influence of operating conditions on cooling output and COP”, ASHRAE Trans.: Res. 101, 348‒357 (1995).
  69.  K. Habib, B.B. Saha, A. Chakraborty, S. Koyama, and K. Srinivasan, “Performance evaluation of combined adsorption refrigeration cycles”, Int. J. Refrig. 34, 129‒137 (2011).
  70.  B.B. Saha, S. Koyama, T. Kashiwagi, A. Akisawa, K.C. Ng, and H.T. Chua, “Waste heat driven dual-mode, multi-stage, multi-bed regenerative adsorption system”, Int. J. Refrig. 26, 749‒757 (2003).
  71.  A. Li, A.B. Ismail, K. Thu, K.C. Ng, and W.S. Loh, “Performance evaluation of a zeolite–water adsorption chiller with entropy analysis of thermodynamic insight”, Appl. Energy 130, 702‒711 (2014).
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Authors and Affiliations

Jarosław Krzywanski
1
ORCID: ORCID
Karol Sztekler
2
ORCID: ORCID
Marcin Bugaj
3
ORCID: ORCID
Wojciech Kalawa
2
ORCID: ORCID
Karolina Grabowska
1
ORCID: ORCID
Patryk Robert Chaja
4
ORCID: ORCID
Marcin Sosnowski
1
ORCID: ORCID
Wojciech Nowak
2
ORCID: ORCID
Łukasz Mika
2
ORCID: ORCID
Sebastian Bykuć
4
ORCID: ORCID
  1. Jan Dlugosz University in Czestochowa, Faculty of Science and Technology, ul. A. Krajowej 13/15, 42-200 Czestochowa, Poland
  2. AGH University of Science and Technology, Faculty of Energy and Fuels, ul. A. Mickiewicza 30, 30-059 Cracow, Poland
  3. Warsaw University of Technology, Faculty of Power and Aeronautical Engineering, ul. Nowowiejska 24, 00-665 Warsaw, Poland
  4. Institute of Fluid-Flow Machinery Polish Academy of Sciences, Department of Distributed Energy, ul. Fiszera 14, 80-952 Gdansk, Poland

Increase of energy self-consumption in hybrid RES installations 1 with PV panels and air-source heat pumps

Sebastian Pater
Chemical and Process Engineering: New Frontiers | 2023 | vol. 44 | No 4 (24th Polish Conference of Chemical and Process Engineering, 13-16 June 2023, Szczecin, Poland. Guest editor: Prof. Rafał Rakoczy) | e43 | DOI: 10.24425/cpe.2023.147402
Keywords PV systems heat pumps renewable energy sources on-grid systems transient simulations
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Abstract

In recent years, European countries have experienced a noteworthy surge in the interest surrounding renewable energy sources, particularly the integration of photovoltaic (PV) panels with various types of heat pumps. This study aims to evaluate the energy performance of a grid19 connected hybrid installation, combining a PV array with an air-source heat pump (AHP), for domestic hot water preparation in a residential building located in Cracow, Poland. The primary focus of this evaluation is to assess the extent to which self-consumption (SC) of energy can be increased. The study utilizes Transient System Simulation Tool 18 software to construct and simulate various system models under different scenarios. These scenarios include building electricity consumption profiles, PV power systems, and the specified management of AHP. Analyses were conducted over a period of 1 year to assess the operational performance of the systems. In the considered installations, the differences in SC values between PV installation ranged from 9 to 25%. Notably, the highest SC values were observed during the winter months. AHP with operation control allows to obtain in some months of the year up to 35% higher value the SC parameter compared to systems without AHP. The highest annual 29 SC value recorded reached 83.9%. These findings highlight the crucial role of selecting an appropriate PV system size to maximize the SC parameter.
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Authors and Affiliations

Sebastian Pater
1
ORCID: ORCID
  1. Cracow University of Technology, Faculty of Chemical Engineering and Technology, Warszawska 24, 31-155 Cracow, Poland

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