Peculiarities of thermomodernization of the heating system of military infrastructure complexes

User Rating:  / 0
PoorBest 

Authors:


R.V.Bulhakov*, orcid.org/0000-0002-8825-718X, Odesa Military Academy, Odesa, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

T.V.Rabocha, orcid.org/0000-0002-9475-334X, Odesa Military Academy, Odesa, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

O.S.Frolov, orcid.org/0000-0002-0941-4299, Odesa Military Academy, Odesa, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

A.V.Ventsyuk, orcid.org/0000-0003-1460-1619, Odesa Military Academy, Odesa, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

* Corresponding author e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.


повний текст / full article



Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2023, (2): 084 - 090

https://doi.org/10.33271/nvngu/2023-2/084



Abstract:



Purpose.
To develop a solution for modernization of heat supply systems (HSS) of infrastructure complexes including, in particular, in military towns (MT), the problem of both efficient and effective heating of which is especially important at present. To develop a mathematical model for finding an effective solution to the problem of modernizing heating and detecting leaks of the heat-conductor.


Methodology.
Special and general methods are used: mathematical formalization – to build mathematical model of the problem of heat supply of MT and detection of heat-conductor leaks; induction and deduction – for choosing and substantiating the expediency of using equipment for HSS of MT; analysis and synthesis – for the development of a schematic solution HSS of MT.


Findings.
Decision options regarding the choice of heat-generating equipment for the modernization of HSS of MT are substantiated. A combined system using boilers and solar collectors is proposed. For the combined system, a schematic solution for integrating equipment into a single autonomous HSS of MT and an economical option for automatic system control are developed. For boilers of the combined system, it is suggested to use cheap and affordable fuel – pellets. A mathematical model has been developed for finding an effective system solution for HSS of MT and detecting heat-conductor leaks.


Originality.
Special requirements for HSS of MT were formulated: autonomy; high level of reliability, survivability; flexibility in ensuring the volume of heat supply during the day, week, season; minimizing the cost of equipment and fuel for it; the possibility of inexpensive repair and renewal. Variants of modernization of HSS in MT have been analyzed and a schematic solution of an integrated HSS of MT with combined use of generating equipment has been developed.


Practical value.
The developed mathematical model and schematic solution of HSS in MT as well as proposed options for using equipment and fuel for it allow ensuring the proper level of fulfillment of special requirements for HSS in MT.



Keywords:
thermal modernization, military towns, military infrastructure complexes, heat networks, energy efficiency

References.


1. Touš, M., Máša, V., & Vondra, M. (2021). Energy and water savings in military base camps. Energy Systems, 12, 545-562. https://doi.org/10.1007/s12667-019-00354-y.

2. Makropoulos, C., Koutiva, I., Kossieris, P., & Rozos, E. (2019). Water management in the military: The SmartBlueCamp Profiling Tool. Science of The Total Environment, 651, 493-505. https://doi.org/10.1016/j.scitotenv.2018.09.056.

3. Teng, S. Y., Touš, M., Leong, W.D., How, B.S., Lam, H.L., & Máša, V. (2021). Recentadvances on industrial data-driven energy savings: Digital twins andinfrastructures. Renewable and Sustainable Energy Reviews, 135, 110208. https://doi.org/10.1016/j.rser.2020.110208.

4. Farid, A. M. (2019). Towards Multi-Modal Army Base Energy Management Systems: The Arctic Resilient Intelligent Integrated Energy System (ARIIES) Case. White paper: national academies power the U.S. army of the future study. Retrieved from https://www.nationalacademies.org/documents/embed/link/LF2255DA3DD1C41C0A42D3BEF0989ACAECE3053A6A9B/file/D21A1AEA3D9B3AF3AA694B96C0FE76BA6A49607858F7?noSaveAs=1.

5. Dyrelund, A., Neimeier, R. M., Margaryan, H., & Moller, A. B. (2021). Energy Master Planning for Resilient Public Communities Best Practices from Denmark. ASHRAE Transactions, 127(Pt 1), 629-646.

6. Engel, J. (2022). Could geothermal power U.S. military bases? DOD wants to find out. Power Engineering. Retrieved from https://www.power-eng.com/renewables/could-geothermal-power-u-s-military-bases-dod-wants-to-find-out/#gref.

7. Winfield, E. C., Rader, R. J., Zhivov, A. M., Adams, T. A., Dyrelund, A., Fredeen, C., Gudmundsson, O., & Goering, B. (n.d.). Best Practices for HVAC, Plumbing, and Heat Supply in Arctic Climates. ASHRAE, VC-21-007. Retrieved from https://annex73.iea-ebc.org/Data/Sites/4/media/papers/VC-21-007_Preprint.pdf.

8. Samaras, C., Nuttalla, W.J., & Bazilian, M. (2019). Energy and the military: Convergence of security, economic, and environmental decision-making. Energy Strategy Reviews, 26, 100409. https://doi.org/10.1016/j.esr.2019.100409.

9. Basok, B., Lysenko, O., Andreychuk, S., & Pryemchenko, V. (2018). Experimental research of an individual heat point with electric boilers. Energy-efficienсy in civil engineering and architecture, 10, 29-35. https://doi.org/10.32347/2310-0516.2018.10.29-35.

10. Máša, V., Stehlík, P., Touš, M., & Vondra, M. (2018). Key pillars of successful energy saving projects in small and medium industrial enterprises. Energy, 158, 293-304. https://doi.org/10.1016/j.energy.2018.06.018.

11. Guzek, M., Białek, J., Królikowski, B., Bielecki, A., Świrski, K., & Wojdan, K. (2019). Advanced algorithms for operational optimization and predictive maintenance of large district heating systems. In 2019 IEEE 6 th International Conference on Energy Smart Systems (ESS). https://doi.org/10.1109/ESS.2019.8764194.

12. Moustakidis, S., Meintanis, I., Halikias, G., & Karcanias, N. (2019). An innovative control framework for district heating systems: conceptualisation and preliminary results. Resources, 8, 27. https://doi.org/10.3390/resources8010027.

13. Johansson, C., Vanhoudt, D., Brage, J., & Geysen, D. (2018). Real-time grid optimisation through digitalisation–results of the STORM project. Energy Procedia, 149, 246-255. https://doi.org/10.1016/j.egypro.2018.08.189.

14. Ntakolia, C., Anagnostis, A., Moustakidis, S., & Karcanias, N. (2021). Machine learning applied on the district heating and cooling sector: a review. Energy Systems, 1, 1-30. https://doi.org/10.1007/s12667-020-00405-9.

15. Benalcazar, P., & Kaminski, J. (2019). Short-term heat load forecasting in district heating systems using artificial neural networks. In IOP Conference Series: Earth and Environmental Science, 214, 012023. https://doi.org/10.1088/1755-1315/214/1/012023.

16. Kim, Y.-G., Heo, K., You, G.-E., Lim, H.-S., Choi, J.-I., & Eom, J.-S. (2018). A study on the improvement of thermal energy efficiency for district thermal energy consumer facility based on reinforcement learning. Preprints, 2018050353. https://doi.org/10.20944/preprints201805.0353.v1.

17. Gong, M., Zhou, H., Wang, Q., Wang, S., & Yang, P. (2020). District heating systems load forecasting: a deep neural networks model based on similar day approach. Advances in Building Energy Research, 3(14), 372-388. https://doi.org/10.1080/17512549.2019.1607777.

18. Yuan, J., Wang, C., & Zhou, Z. (2019). Study on refined control and prediction model of district heating station based on support vector machine. Energy, 189, 116193. https://doi.org/10.1016/j.energy.2019.116193.

19. Winkler, D., Haltmeier, M., Kleidorfer, M., Rauch, W., & Tscheikner-Gratl, F. (2018). Pipe failure modelling for water distribution networks using boosted decision trees. Structure and Infrastructure Engineering, 14, 1402-1411. https://doi.org/10.1080/15732479.2018.1443145.

20. Reynolds, J., Ahmad, M. W., Rezgui, Y., & Hippolyte, J.-L. (2019). Operational supply and demand optimisation of a multi-vector district energy system using artificial neural networks and a genetic algorithm. Applied Energy, 235, 699-713. https://doi.org/10.1016/j.apenergy.2018.11.001.

21. Bilan, Y., Nitsenko, V., & Havrysh, V. (2017). Energy aspect of vertical integration in agriculture. Rynek Energii, 5(132), 98-110.

 

Visitors

6777300
Today
This Month
All days
171
57106
6777300

Guest Book

If you have questions, comments or suggestions, you can write them in our "Guest Book"

Registration data

ISSN (print) 2071-2227,
ISSN (online) 2223-2362.
Journal was registered by Ministry of Justice of Ukraine.
Registration number КВ No.17742-6592PR dated April 27, 2011.

Contacts

D.Yavornytskyi ave.,19, pavilion 3, room 24-а, Dnipro, 49005
Tel.: +38 (056) 746 32 79.
e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
You are here: Home About the journal editorial board EngCat Archive 2023 Content №2 2023 Peculiarities of thermomodernization of the heating system of military infrastructure complexes