Acta Mechanica Slovaca 2020, 24(1):26-32

Compressor Station Control Simulation on Variable Operating Modes

Dariia Budko, German Bondarenko
Sumy State University, Faculty of Technical Systems and Energy Efficient Technologies, Technical Thermophysics Department, 2 Rymskogo-Korsakova St., Sumy, 40007, Ukraine

The algorithm, mathematical model, and computer program of compressor station (CS) operation on variable modes are developed. The operation of the compressor station is controlled by turning on or off the compressors, according to the accepted criteria of rationality. The problem of the compressor station operation with any standard sizes of compressors according to the known schedule of loading is solved. Based on the created mathematical model of the compressor station functioning the numerical research and optimization of its work is carried out.

Keywords: compressor station, air supply system, variable operating modes, mathematical model, criteria of rationality, control scenarios.

Published: March 27, 2020  Show citation

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Budko, D., & Bondarenko, G. (2020). Compressor Station Control Simulation on Variable Operating Modes. Acta Mechanica Slovaca, 24(1), 26-32
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    References

    1. Pustovyi, O. Yu. (2018). Organizational and Economic Mechanism of Energy Efficiency Measures at Machine-Building Enterprises. Energy Saving. Power Engineering. Energy Audit, № 1(144), pp. 11-21 [in Russian].
    2. Eichhammer, W., Mannsbart, W. (1997). Industrial Energy Efficiency - Indicators for a European Cross-Country Comparison of Energy Efficiency in the Manufacturing Industry. Energy Policy, Vol. 5, iss. 7-9, pp. 759-772. Go to original source...
    3. Maliarenko, O. Ye. (2018). Methodical Approach to Determining the Forecast Structure of Energy Consumption Based on an Integrated Approach. The Problems of General Energy № 1(52), pp. 24-31 [in Ukrainian]. Go to original source...
    4. Price, L., Ke J., Ohshita, S. and other (2012). China's Industrial Energy Consumption Trends and Impacts of the Top-1000 Enterprises Energy-Saving Program and the Ten Key Energy-Saving Projects. Energy Policy. Vol. 50, pp. 562-569. Go to original source...
    5. Mohammadi, M., Esmaelian, M., Atighehchian, A. (2020). Design of Mathematical Models for the Integration of Purchase and Production Lot-Sizing and Scheduling Problems under Demand Uncertainty. Applied Mathematical Modelling, Vol. 84, pp. 1-18. Go to original source...
    6. Russell, A., Taghipour, Sh. (2020). Multi-Parallel Work Centers Scheduling Optimization with Shared or Dedicated Resources in Low-Volume Low-Variety Production Systems. Applied Mathematical Modelling, Vol. 80, pp. 472-505. Go to original source...
    7. Todorov, G.N., Volkova, E.E., Vlasov, A.I., Nikitina, N.I. (2019). Modeling Energy-Efficient Consumption at Industrial Enterprises. International Journal of Energy Economics and Policy, Vol. 9, No. 2, pp. 10-18. Go to original source...
    8. Yanrong Lu, Fucheng Liao, Jiamei Deng, Huiyang Liu. (2017).
    9. Trip, S., Cucuzzella, M., Claudio De Persis, Ferrara, A., Jacquelien M. A. Scherpena. (2020). Robust Load Frequency Control of Nonlinear Power Networks. International Journal of Control, Vol. 93, No. 2, pp. 346-359. Go to original source...
    10. Semenov, A. S., Semenova, M. N., Bebikhov, Yu. V. (2019). Development of Universal Mathematical Model of Electrical Power Supply System of Area of Industrial Enterprise. International Russian Automation Conference (RusAutoCon), 8867704. Go to original source...
    11. Panteleev, V., Zilberman, S., Pilyugin, G., Petukhov, R., Sizganova, E., Mashukov, M. (2018). Optimization model of power supply system of industrial enterprise. 9th International Multidisciplinary Scientific and Research Conference on Modern Issues in Science and Technology Workshop in Advanced Technologies in Aerospace, Mechanical and Automation Engineering, MISTAerospace 2018, Vol. 450, No. 7, 072011. Go to original source...
    12. Bondarenko, G. A., Budko, D. V. (2015). Mathematical modeling of air supply system of industrial enterprises. Compressor and Power Machine Industry, Vol. 42, No. 4, pp. 29-33.
    13. Lipchanskiy, A. Yu., Zhatkin, A. N. (2018). Analysis of Air Power Supply Efficiency in Terms of Compressed Air Consumers in Gaisky Gok Mine. Gornyy Informatsionno-Analiticheskiy Byulleten', № 1, pp. 76-82 [in Russian]. Go to original source...
    14. Mousavi, A., Siervo, H.R.A. (2017). Automatic Translation of Plant Data into Management Performance Metrics: A Case for Real-Time and Predictive Production Control. International Journal of Production Research, Vol. 55, No. 17, pp. 4862-4877. Go to original source...
    15. Guo, Y., Guo, L.Z., Billings, S.A., Coca, D., Lang, Z.Q. (2014). A Parametric Frequency Response Method for Non-Linear Time-Varying Systems, International Journal of Systems Science. Vol. 45, No. 10, pp. 2133-2144. Go to original source...
    16. Cormen, T. H., Leiserson, Ch. E., Rivest, R. L., Stein, C.(2013). Introduction to Algorithms, Third Edition. Williamspublishing, Moscow. P. 1328 [in Russian].
    17. Altendorfer, K. (2019). Effect of Limited Capacity on Optimal Planning Parameters for a Multi-Item Production System with Setup Times and Advance Demand Information. International Journal of Production Research, Vol. 57, No. 6, pp. 1892-1913. Go to original source...
    18. Trebuna, P., Pekarcikova, M., Kliment, M. (2019). Simulation Model Creation for Verifying the Production Capacity of a Production Line and Sizing In-Process Inventory. Acta Mechanica Slovaca, Vol. 23, No. 1, pp. 38-43. Go to original source...
    19. Seow, Y., Rahimifard, S. (2011). A Framework for Modelling Energy Consumption within Manufacturing Systems. CIRP Journal of Manufacturing Science and Technology, Vol. 4, No. 3, pp. 258-264. Go to original source...
    20. Mukhoid, O. V., Kostornoi, O.S., Shyfrin, D. M. (2018). Selection of the Optimal Software for Designing Expert Systems. Journal of Engineering Sciences, Vol. 5, No. 2, pp. 10-15. Go to original source...
    21. Dexiang Wu, Desheng Dash Wu, Roy H. Kwon (2017). Optimizing Data-Driven Network under Limited Resource: A Partial Diversification Approach. International Journal of Production Research, Vol. 57, No. 21, pp. 6875-6892.

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