The use of accumulation elements with lumped parameters to model the operation of heat exchange installations under randomly changing temperatures

• Autorzy: Kropiwnicki Jacek , Dawidowicz Bartosz , Wojewódka Przemysław , Rogala Andrzej

Identyfikatory

DOI

10.24425/cpe.2024.149461

• Języki publikacji: EN

Abstrakty

EN

At the design stage of heat exchange installation used for gas conversion it is required to test the stability of the installation operation for the expected variable heat loads. For this purpose, a numerical model of the installation can be used. The paper presents an original concept of modelling the operation of heat exchange installations for randomly changing temperatures. Accumulation elements with lumped parameters were used in the model, which significantly facilitates the definition of model parameters and the calculation itself at the design stage. Due to the randomly changing temperatures supplying the accumulation element by the heating medium and the non-linear nature of the functions used in the calculation model, the iterative procedure was used for calculations. The process of validation of the proposed computational model of the accumulation element with lumped parameters was carried out for a water installation powered by a natural gas-fired boiler. The obtained results showed very good accuracy of the applied approach, the root mean square error for tested data has reached 1°C to 3°C, depending on the analysed case.

Słowa kluczowe

EN

heat transfer modelling; warming-up process; chemical installation temperature control; randomly modified; conditions

PL

modelowanie wymiany ciepła; proces rozgrzewania; kontrola temperatury instalacji chemicznej

• Wydawca: Komitet Inżynierii Chemicznej i Procesowej Polskiej Akademii Nauk
• Czasopismo: Chemical and Process Engineering : New Frontiers
• Rocznik: 2024
• Tom: Vol. 45, nr 3
• Strony: art. no. e66
• Opis fizyczny: Bibliogr. 38 poz., rys., tab.

Twórcy

autor

Kropiwnicki Jacek

• Gdansk University of Technology, Faculty of Mechanical Engineering and Ship Technology, Narutowicza 11/12, 80-223, Gdansk, Poland

• https://orcid.org/0000-0001-7412-7424

autor

Dawidowicz Bartosz

• Gdansk University of Technology, Faculty of Mechanical Engineering and Ship Technology, Narutowicza 11/12, 80-223, Gdansk, Poland

autor

Wojewódka Przemysław

• Gdansk University of Technology, Faculty of Chemistry, Narutowicza 11/12, 80-223 Gdansk, Poland

autor

Rogala Andrzej

• Gdansk University of Technology, Faculty of Chemistry, Narutowicza 11/12, 80-223 Gdansk, Poland

• https://orcid.org/0000-0002-5747-9425

Bibliografia

• 1. Alsanousie A.A., Elsamni O.A., Attia A.E., Elhelw M., 2021. Transient and troubleshoots management of aged small-scale steam power plants using Aspen Plus Dynamics. Energy, 223, 120079. DOI: 10.1016/j.energy.2021.120079.
• 2. Aminmahalati A., Fazlali A., Safikhani H., 2021. Multi-objective optimization of CO boiler combustion chamber in the RFCC unit using NSGA II algorithm. Energy, 221, 119859. DOI: 10.1016/j.energy.2021.119859.
• 3. Barone G., Buonomano A., Forzano C., Palombo A., 2020. A novel dynamic simulation model for the thermo-economic analysis and optimisation of district heating systems. Energy Convers. Manage., 220, 113052. DOI: 10.1016/j.enconman.2020.113052.
• 4. Bhutta M.M.A., Hayat N., Bashir M.H., Khan A.R., Ahmad K.N., Khan S., 2012. CFD applications in various heat exchangers design: a review. Appl. Therm. Eng., 32; 1–12. DOI: 10.1016/j.applthermaleng.2011.09.001.
• 5. Bird T.J., Jain N., 2020. Dynamic modelling and validation of a micro-combined heat and power system with integrated thermal energy storage. Appl. Energy, 271, 114955. DOI: 10.1016/j.apenergy.2020.114955.
• 6. Brown D.M., Bhatt B.L., Hsiung T.H., Lewnard J.J., Waller F.J., 1991. Novel technology for the synthesis of dimethyl ether from syngas. Catal. Today, 8, 279–304. DOI: 10.1016/0920- 5861(91)80055-E.
• 7. Dahash A., Mieck S., Ochs F., Krautz H.J., 2019. A comparative study of two simulation tools for the technical feasibility in terms of modelling district heating systems: an optimization case study. Simul. Modell. Pract. Theory, 91, 48–68. DOI: 10.1016/j.simpat.2018.11.008.
• 8. Dehghan A.A., Barzegar A., 2011. Thermal performance behaviour of a domestic hot water solar storage tank during consumption operation. Energy Convers. Manage., 52; 468–476. DOI: 10.1016/j.enconman.2010.06.075.
• 9. ElAzab H.-A.I., Swief R.A., El-Amary N.H., Temraz H.K., 2018. Unit commitment towards decarbonized network facing fixed and stochastic resources applying water cycle optimization. Energies, 11, 1140. DOI: 10.3390/en11051140.
• 10. Gabrielaitiene I., Bøhm B., Sunden B., 2007. Modelling temperature dynamics of a district heating system in Naestved, Denmark – a case study. Energy Convers. Manage., 48, 78–86. DOI: 10.1016/j.enconman.2006.05.011.
• 11. Gao H., Liu Y., Song X., Zheng B., Sun P., Lu M., Ma Y., Gao Z., 2019. Numerical study of heat transfer characteristics of semi-coke and steam in waste heat recovery steam generator for hydrogen production. Int. J. Hydrogen Energy, 44, 25160– 25168. DOI: 10.1016/j.ijhydene.2019.05.155.
• 12. Giraud L., Merabet M., Baviere R., Vallée M., 2017. Optimal control of district heating systems using dynamic simulation and mixed integer linear programming. Proceedings of the 12th International Modelica Conference, Prague, Czech Republic, 15–17 March 2017, 141–150. DOI: 10.3384/ecp17132141.
• 13. Han Y., Sun Y., Wu J., 2020. An efficient solar/lignite hy- brid power generation system based on solar-driven waste heat recovery and energy cascade utilization in lignite predrying. Energy Convers. Manage., 205, 112406. DOI: 10.1016/j.enconman.2019.112406.
• 14. Jiang B., Xia D., Guo H., Xiao L., Qu H., Liu X., 2019. Efficient waste heat recovery system for high-temperature solid particles based on heat transfer enhancement. App. Therm. Eng., 155, 166–174. DOI: 10.1016/j.applthermaleng.2019.03.101.
• 15. Kamal R., Moloney F., Wickramaratne C., Narasimhan A., Goswami D.Y., 2019. Strategic control and cost optimization of thermal energy storage in buildings using EnergyPlus. Appl. Energy, 246, 77–90. DOI: 10.1016/j.apenergy.2019.04.017.
• 16. Kang J.O., Kim S.C., 2019. Heat transfer characteristics of heat exchangers for waste heat recovery from a billet casting process. Energies, 12, 2695. DOI: 10.3390/en12142695
• 17. Kluba A., Field R., 2019. Optimization and exergy analysis of nuclear heat storage and recovery. Energies, 12, 4205. DOI: 10.3390/en12214205.
• 18. Knabner P., Angermann L., 2003. Numerical methods for elliptic and parabolic partial differential equations. Springer, New York, 7–13. DOI: 10.1007/b97419.
• 19. Kropiwnicki J., Furmanek M., Rogala A., 2021. Modular approach for modelling warming up process in water installations with flow-regulating elements. Energies, 14, 4599. DOI: 10.3390/en14154599.
• 20. Kuang J., Zhang C., Li F., Sun B., 2018. Dynamic optimization of combined cooling, heating, and power systems with Energy storage units. Energies, 11, 2288. DOI: 10.3390/en11092288.
• 21. Li D., Wang J., Ding Y., Yao H., Huang Y., 2019. Dynamic thermal management for industrial waste heat recovery based on phase change material thermal storage. Appl. Energy, 236, 1168–1182. DOI: 10.1016/j.apenergy.2018.12.040.
• 22. Li P., Wang H., Lv Q., Li W., 2017. Combined heat and power dispatch considering heat storage of both buildings and pipelines in district heating system for wind power integration. Energies, 10, 893. DOI: 10.3390/en10070893.
• 23. Li S., Deng Z., Liu J., Liu D., 2022. Multi-objective optimization of plate-fin heat exchangers via non-dominated sequencing genetic algorithm (NSGA-II). Appl. Sci., 12, 11792. DOI: 10.3390/app122211792.
• 24. Oliveira A.V.S., Avrit A., Gradeck M., 2022. Thermocouple response time estimation and temperature signal correction for an accurate heat flux calculation in inverse heat conduction problems. Int. J. Heat Mass Transfer, 185, 122398. DOI: 10.1016/j.ijheatmasstransfer.2021.122398.
• 25. Oppelt T., Urbaneck T., Gross U., Platzer B., 2016. Dynamic thermo-hydraulic model of district cooling networks. Appl. Therm. Eng., 102, 336–345. DOI: 10.1016/j.applthermaleng. 2016.03.168.
• 26. Pal E., Kumar I., Joshi J.B., Maheshwari N.K., 2016. CFD simulations of shell-side flow in a shell-and-tube type heat exchanger with and without baffles. Chem. Eng. Sci., 143, 314–340. DOI: 10.1016/j.ces.2016.01.011.
• 27. Pudlik W., 2012. Wymiana i wymienniki ciepła. Wydawnictwo Politechniki Gdańskiej, 143. Available at: https://press.pg.edu.pl/book/428.
• 28. Shi Y., Zhong W., Chen X., Yu A.B., Li J., 2019. Combustion optimization of ultrasupercritical boiler based on artificial intelligence. Energy, 170, 804–817. DOI: 10.1016/j.energy. 2018.12.172.
• 29. Shu J., Fu J., Ren C., Liu J., Wang S., Feng S., 2020. Nu- merical investigation on flow and heat transfer processes of novel methanol cracking device for internal combustion engine exhaust heat recovery. Energy, 195, 116954. DOI: 10.1016/ j.energy.2020.116954.
• 30. Skoglund T., Årzén K.-E., Dejmek P., 2006. Dynamic object-oriented heat exchanger models for simulation of fluid property transitions. Int. J. Heat Mass Transfer, 49, 2291–2303. DOI: 10.1016/j.ijheatmasstransfer.2005.12.005.
• 31. Sobczak J., Wysocka I., Murgrabia S., Rogala A., 2022. A review on deactivation and regeneration of catalysts for dimethyl ether synthesis. Energies, 15, 5420. DOI: 10.3390/en15155420.
• 32. Vesterlund M., Dahl J., 2015. A method for the simulation and optimization of district heating systems with meshed networks. Energy Convers. Manage., 89, 555–567. DOI: 10.1016/j.enconman.2014.10.002.
• 33. Wang J., Bian H., Cao X., Ding M., 2021. Numerical performance analysis of a novel shell-and-tube oil cooler with wire-wound and crescent baffles. Appl. Therm. Eng., 184, 116298. DOI: 10.1016/j.applthermaleng.2020.116298.
• 34. Wysocka I., Hupka J., Rogala A., 2019. Catalytic activity of nickel and ruthenium–nickel catalysts supported on SiO2, ZrO2, Al2O3, and MgAl2O4 in a dry reforming process. Catalysts, 9, 540. DOI: 10.3390/catal9060540.
• 35. Yusuf A., Bayhan N., Tiryaki H., Hamawandi B., Toprak M.S., Ballikaya S., 2021. Multi-objective optimization of concentrated Photovoltaic-Thermoelectric hybrid system via non-dominated sorting genetic algorithm (NSGA II). Energy Convers. Manage., 236, 114065. DOI: 10.1016/j.enconman.2021.114065.
• 36. Zheng T., Zhao Li.-P., 2022a. Dynamic flow optimization for a three-loop fluid heat dissipation system in spacecraft. Case Stud. Therm. Eng., 40, 102496. DOI: 10.1016/j.csite.2022.102496.
• 37. Zheng T., Zhao Li.-P., 2022b. Dynamic optimization analyses and algorithm design for the parallel heat exchange system in spacecraft. Appl. Therm. Eng., 212, 118519. DOI: 10.1016/j.applthermaleng.2022.118519.
• 38. Zhou H., Xinping Q., Kefa C., Fan J., 2004. Optimizing pulverized coal combustion performance based on ANN and GA. Fuel Process. Technol., 85, 113–124. DOI: 10.1016/S0378-3820(03)00155-3.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025)