Implementation of block artificial cooling units for gas treatment

Authors

DOI:

https://doi.org/10.15587/2706-5448.2024.317589

Keywords:

block artificial cooling units, low-temperature separation, unit performance, gas recovery

Abstract

The object of research is the process of implementation and use of block artificial cooling units in the technology of natural gas preparation.

The research has confirmed the high efficiency of using artificial cooling units. Due to deep gas cooling, it is possible to achieve a significant increase in condensate production and improve gas quality. In addition, modern units are characterized by high energy efficiency and compactness.

A comprehensive analysis of existing gas preparation technologies and a comparative assessment using block artificial cooling units revealed a number of significant advantages of the proposed system, namely:

– block units provide deeper removal of heavy hydrocarbons, water and other impurities, which improves the quality of the final product;

– due to lower gas temperature, more intensive condensation of heavy hydrocarbons is achieved, which leads to additional extraction of valuable components;

– modern block units are equipped with energy-efficient equipment, which reduces energy costs;

– the units have a modular design, which facilitates their transportation, unit and maintenance;

– the use of block units allows to reduce emissions of harmful substances into the atmosphere;

– the ability to adapt to different operating conditions and product quality requirements.

The study found that existing natural gas preparation technologies have a number of disadvantages, such as:

– low efficiency of gas purification;

– high energy consumption;

– complexity of maintenance;

– large dimensions of the equipment.

Despite some drawbacks, the introduction of block artificial cooling units is a promising direction for the development of the gas industry. The results of the study indicate the high efficiency of this technology and its economic feasibility in the long term.

Author Biographies

Victoriia Rubel, National University “Yuri Kondratyuk Poltava Polytechnic”

PhD, Associate Professor

Department of Oil and Gas Engineering and Technology

Maksym Maslenko, National University “Yuri Kondratyuk Poltava Polytechnic”

PhD Student

Department of Oil and Gas Engineering and Technology

References

  1. Maliarenko, V. A., Senetskyi, O. V. (2021). Teplomasoobmin v obiektakh alternatyvnoi enerhetyky. Kharkiv: KhNUMH im. O. M. Beketova, 311.
  2. Pro zatverdzhennia Kodeksu hazorozpodilnykh system (2015). Postanova Natsionalnoi komisii, shcho zdiisniuie derzhavne rehuliuvannia u sferakh enerhetyky ta komunalnykh posluh No. 2494. 30.09.2015. Available at: https://zakon.rada.gov.ua/laws/show/z1379-15#Text
  3. Asadullah, M. (2014). Biomass gasification gas cleaning for downstream applications: A comparative critical review. Renewable and Sustainable Energy Reviews, 40, 118–132. https://doi.org/10.1016/j.rser.2014.07.132
  4. Koyuncu, I., Yilmaz, C., Alcin, M., Tuna, M. (2020). Design and implementation of hydrogen economy using artificial neural network on field programmable gate array. International Journal of Hydrogen Energy, 45 (41), 20709–20720. https://doi.org/10.1016/j.ijhydene.2020.05.181
  5. Kopyscinski, J., Schildhauer, T. J., Biollaz, S. M. A. (2010). Production of synthetic natural gas (SNG) from coal and dry biomass – A technology review from 1950 to 2009. Fuel, 89 (8), 1763–1783. https://doi.org/10.1016/j.fuel.2010.01.027
  6. Akolaş, H. İ., Kaleli, A., Bakirci, K. (2020). Design and implementation of an autonomous EGR cooling system using deep neural network prediction to reduce NOx emission and fuel consumption of diesel engine. Neural Computing and Applications, 33 (5), 1655–1670. https://doi.org/10.1007/s00521-020-05104-1
  7. Shao, G., Hanaor, D. A. H., Shen, X., Gurlo, A. (2020). Freeze Casting: From Low‐Dimensional Building Blocks to Aligned Porous Structures – A Review of Novel Materials, Methods, and Applications. Advanced Materials, 32 (17). https://doi.org/10.1002/adma.201907176
  8. Petruniak, M., Rubel, V., Chevhanova, V., Kulakova, S. (2021). Application of grout slurries with the defecate addition for effective well cementing. Mining of Mineral Deposits, 15 (1), 59–65. https://doi.org/10.33271/mining15.01.059
  9. Wan, K., Barnaud, C., Vervisch, L., Domingo, P. (2020). Chemistry reduction using machine learning trained from non-premixed micro-mixing modeling: Application to DNS of a syngas turbulent oxy-flame with side-wall effects. Combustion and Flame, 220, 119–129. https://doi.org/10.1016/j.combustflame.2020.06.008
  10. Hadian, M., Saryazdi, S. M. E., Mohammadzadeh, A., Babaei, M. (2021). Application of artificial intelligence in modeling, control, and fault diagnosis. Applications of Artificial Intelligence in Process Systems Engineering. Elsevier, 255–323. https://doi.org/10.1016/b978-0-12-821092-5.00006-1
  11. Shevchuk, L. V. (2020). Quality management of business processes in the supply chain of refrigeration equipment. Natsionalnyi Aviatsiinyi Universytet. Available at: https://er.nau.edu.ua/handle/NAU/45524
Implementation of block artificial cooling units for gas treatment

Downloads

Published

2024-12-19

How to Cite

Rubel, V., & Maslenko, M. (2024). Implementation of block artificial cooling units for gas treatment. Technology Audit and Production Reserves, 6(80). https://doi.org/10.15587/2706-5448.2024.317589

Issue

Section

Technology and System of Power Supply