Науковий вісник НГУ

Gas flow measuring system using signal processing on the basis of entropy estimations

• 
• 

User Rating:  / 0

PoorBest 

Details

Category: Content №5 2021

Last Updated on 29 October 2021

Published on 30 November -0001

Hits: 4726

Authors:

I.Z.Manuliak, orcid.org/0000-0002-0072-1532, Ivano-Frankivsk National Technical University of Oil and Gas, Ivano-Frankivsk, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

S.I.Melnychuk, orcid.org/0000-0002-6973-4235, Ivano-Frankivsk National Technical University of Oil and Gas, Ivano-Frankivsk, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Yu.Yo.Striletskyi, orcid.org/0000-0002-0105-8306, Ivano-Frankivsk National Technical University of Oil and Gas, Ivano-Frankivsk, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

V.M.Harasymiv, orcid.org/0000-0002-6613-3549, Ivano-Frankivsk National Technical University of Oil and Gas, Ivano-Frankivsk, Ukraine, 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. 2021, (5): 125 - 130

https://doi.org/10.33271/nvngu/2021-5/125

Abstract:

Purpose. To increase the accuracy of gas flow measurement in tachometric transducers based on the improvement of structural, hardware and algorithmic support of information and measuring systems.

Methodology. The gas consumption value is determined by the parameters of information and measurement signals. Sensor signals interacting with the environment are traditionally processed on the basis of amplitude and frequency methods. The research methodology is based on the information theory, methods of statistical and spectral analysis, digital signal processing, the theory of gas dynamics, based on mathematical modeling in a computational experiment, as well as the theory of errors and measurement results uncertainty. The statistical characteristics of the measuring signals of the converter presented in the unitary basis are studied.

Findings. The conducted research resulted in development of an information-measuring system to control the sensitivity threshold of the transducers of the primary volume and the volume of gas consumption based on the developed primary transducer, which allows providing relative standard uncertainty of cost determination within 0.5%. A special processor has been developed to calculate the entropy estimates of signal information.

Originality. For the first time, a method for the formation and processing of information-measuring signals, which is based on the use of pressure pulsations due to the movement of the measuring element of the converter in the toroidal measuring cell, is proposed. Implementation of the measuring element of a spherical converter, whose density is almost commensurable with the density of the controlled medium is offered.

Practical value. The proposed method allows providing a lower sensitivity threshold compared to the industrial implementation of tachometric type transducers.

Keywords: information measuring system, information entropy estimation, measuring signals, gas flow, primary converter

References.

1. Thapa, P. (2016). Measurement and Control system in oil gas industry. Centre for Risk, Integrity and Safety Engineering (C-RISE). Retrieved from https://www.researchgate.net/publication/ 311949744.

2. Andersson, A. (2017). Measurement Technology for Process Automation. CRC Press. https://doi.org/10.4324/9781315267913.

3. Ropyak, L.Y., Pryhorovska, T.O., & Levchuk, K.H. (2020). Analysis of materials and modern technologies for PDC drill bit manufacturing. Progress in Physics of Metals, 21(2), 274-301. https://doi.org/10.15407/ufm.21.02.274.

4. Ilin, S., Adorska, L., Samusia, V., Kolosov, D., & Ilina,I. (2019). Conceptual bases of intensification of mining operations in mines of Ukraine based on monitoring and condition management of mine hoisting systems. E3S Web of Conferences, 109, art. no. 00030. https://doi.org/10.1051/e3sconf/201910900030.

5. Velychkovych, A.S., Andrusyak, A.V., Pryhorovska, T.O., & Ropyak, L.Y. (2019). Analytical model of oil pipeline overground transitions, laid in mountain areas. Oil and Gas Science and Technology, 74, art. no. 2019039. https://doi.org/10.2516/ogst/2019039.

6. Babak, V.P., Babak, S.V., Myslovych, M.V., Zaporozhets, A.O., & Zvaritch, V.M. (2020). Simulation and software for diagnostic systems. Studies in Systems, Decision and Control, 281, 71-90. https://doi.org/10.1007/978-3-030-44443-3_3.

7. Dunets, R., Klym, H., & Kochan, R. (2015). Multi-functional nanostructured sensors and their adaptation into cyber-physical systems. Proceedings of the International Conference on Computer Sciences and Information Technologies, CSIT 2015, art. no. 7325455, 154-157. https://doi.org/10.1109/STC-CSIT.2015.7325455.

8. Tan, L., & Jiang, J. (2018). Digital Signal Processing: Fundamentals and Applications. Academic Press. ISBN: 978-0-12-374090-8.

9. wisulski, D., Hanus, R., Zych, M., & Petryka, L. (2017). Methods of measurement signal acquisition from the rotational flow meter for frequency analysis. EPJ Web of Conferences 143. 02124.
https://doi.org/10.1051/epjconf/201714302124.

10. Andriishyn, M.P., Chernyshenko, O.M., & Edel,A.V. (2015). The peculiarities of using gas-dynamic similarity theory during calibration and verification of natural gas meters. Naftohazova haluz Ukrainy, 6, 33-36.

11. Manuliak, I.Z. (2016). Schematic solutions for the processing of impulse signals of volume converters and volumetric gas consumption. Visnyk of Kherson National Technical University, 4(59), 169-174.

12. Nykolaichuk, Ya.M., Pastukh, T.I., & Voronych, A.R. (2015). Theory and methods of evaluating the entropy of discrete manipulated signals. Optoelectronic Information-Energy Technologies, (1), 18-29.

13. Nykolaichuk, Ya.M., & Voronych, A.R. (2010). Theoretical fundamentals of entropy measures and their application in information technologies of signal formation and processing. Optoelectronic Information-Energy Technologies, 19(1), 50-64.

14. Manuliak, I.Z., & Melnychuk, S.I. (2017). The algorithmic support implementation for sliding value calculation of the information entropy estimation of measuring signals. Herald of Khmelnytskyi National University. Technical sciences, 5(253), 182-186.

15. Manulyak, I., Melnychuk, S., Voronych, A., & Nykolaychuk, L. (2018). Special processor of information entropy estimates calculation of fixed-size signals binary realizations. Proceedings of the XIV International Conference Perspective Technologies and Methods in MEMS Design, 18-22 April, (pp. 200-203). Lviv.

16. Xin, Zhang (2015). Digital Signal Processing System Research and Design Based on FPGA. International Conference on Manufacturing Science and Engineering (ICMSE-2015), 28-29 November, (pp. 103-106).

17. Czaja, Z. (2018). Time-domain measurement methods for R, L and C sensors based on a versatile direct sensor-to-microcontroller interface circuit. Sensors And Actuators A-Physical, 274, 199-210. https://doi.org/10.1016/j.sna.2018.03.029.

18. Shatskyi, I., Ropyak, L., & Velychkovych, A. (2020). Model of contact interaction in threaded joint equipped with spring-loaded collet. Engineering Solid Mechanics, 8(4), 301-312. https://doi.org/10.5267/ j.esm.2020.4.002.

• < Prev
• Next >