DIGITAL TRANSMISSION SYSTEM

  • R. Malikov Ufa State Aviation Technical University
Keywords: gas transmission enterprise integration, knowledge discovery, data mining, continuous monitoring, complex event processing

Abstract

This article examines the process models of business processes of gas distribution stations (GDS), which would be a symbiosis of digital twins and artificial intelligence. The basis of the system could be a mathematical model of the plant, taking into account the operating parameters, which is able to determine the response of the equipment to various operating loads and predict the periods of trouble-free operation. The system could also provide for methods of simulation of physical processes to determine the optimal operating modes and boundary conditions for their operation.

Reengineering means that we are moving to the operation of the main GDS equipment according to the actual technical condition, and not according to the operating time, as is happening now.

References

Kantyukov R.R., Tahaviev M.S., Romanov S.V. Improvement Of Work With Infrastructure Facilities Of Gas Transportation And Gas Distribution Using Well Logging. Gazovaya promyshlennost' = Gas Industry, 2015, No. 9 (727), P. 40-41. (In Russian)
Kantyukov R.R, Tahaviev M.S., Gilyaziev M.G., Shenkarenko S.V., Lebedev R.V., Varsegov V.L. Development Of A Mathematical Model Of Section Of The Gas Transportation System. Transport i hranenie nefteproduktov i uglevodorodnogo syr'ya = Transport And Storage Of Petroleum Products And Hydrocarbons, 2015, No. 2, P. 3-7. (In Russian)
Grauer M., Metz D., Karadgi S.S. and Schafer W. Identification and Assimilation of Knowledge for Real-Time Control of Enterprise Processes in Manufacturing. Proc. 2nd International Conference on Information, Process and Knowledge Management (eKNOW 2010), Feb. 2010, P. 13-16.
Linthicum D. Enterprise Application Integration. Addison-Wesley Longman, Amsterdam, 2000.
Lee J., Siau K., Hong S. Enterprise Integration with ERP and EAI. Comm. of the ACM, Feb. 2003, Vol. 46, No. 2, P. 54-60.
Drobik A., Raskino M., Flint D. at all. The Gartner Definition of Real-Time Enterprise, tech. report. Gartner Inc., 2002.
Karnouskos S., Guinard D., Savio D. at all. Towards the Real-Time Enterprise: Service-based Integration of Heterogeneous SOA-ready Industrial Devices with Enterprise Applications. Proc. 13th IFAC Symposium on Information Control Problems in Manufacturing (INCOM '09), June 2009,
P. 2127-2132.
Kjaer A. The Integration of Business and Production Processes. IEEE Control Systems Magazine, 2003, Vol. 23, No. 6, P. 50-58.
Panetto H., Molina A. Enterprise Integration and Interoperability in Manufacturing Systems: Trends and Issues. Computers in Industry, Sept. 2008, Vol. 59, No. 7, P. 641-646 .
Grauer M., Metz D., Karadgi S. at all. Towards an IT-Framework for Digital Enterprise Integration. Proc. 6th International Conference on Digital Enterprise Technology (DET 2009), AISC, Springer, Berlin, Dec. 2009, vol. 66, P. 1467-1482.
Grauer M., Karadgi S.S., Metz D., Schafer W. An Approach for Real-Time Control of Enterprise Processes in Manufacturing using a Rule-Based System. Proc. Multikonferenz Wirtschaftsinformatik, Feb. 2010, P. 1511-1522.
Scheer A. Business Process Engineering. Reference Model for Industrial Enterprise, 2nd Edition, Springer, 1994.
Hammer M., Champy J. Reengineering the Corporation: A Manifesto for Business Revolution. Harper Business, 1994.
Cammert M., Heinz C., Kramer J. at all. Stream Processing in Production-to-Business Software. Proc. of the IEEE Int. Conf. on Data Eng., 2006, P. 168-169.
Published
2021-12-16
How to Cite
Malikov , R. 2021. “DIGITAL TRANSMISSION SYSTEM ”. EurasianUnionScientists, December, 23-27. https://doi.org/10.31618/ESU.2413-9335.2021.1.92.1508.