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Modeling and Research of Gas Transportation Unit Operation Modes


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DOI: https://doi.org/10.15866/ireme.v13i4.17161

Abstract


The main objective of this paper is to describe the methodic and the software developed by authors, which has aimed to simulate operation modes of a centrifugal gas compressor and associated equipment. In this paper, the computer-based design and the analysis of control systems for centrifugal gas compressors are presented. The structure of the mathematical models and the solution methods are discussed. A Visual Basic-based application has been built to solve the system dynamics and to generate the model in installation executable format. The application allows simulating the behavior of a gas compression network through an individual simulation of each component in the network like gas compressor, valve, heat-exchanger, gas-liquid separator, pipe line. Flow through the system elements is modeled as a multi-component compressible gas flow and the Redlich-Kwong Equation of State is used to solve the mass conservation equation. Based on the centrifugal gas compressor theory an anti-surge control strategy has been applied to the model in order to predict and control surge cases. The one-stage gas compression model has been used to verify how the software responds to changes in the suction and discharge conditions.
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Keywords


Centrifugal Gas Compressor; Anti-Surge Control; Mass Flow Rate; Polytropic Head; Dynamic Simulation; Gas Transportation Unit

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References


G. A. van Zee. Control System Design for a Centrifugal Compressor Using Dynamic Simulation, IFAC Proc. Vol., vol. 20, no. 5, pp. 363–368, 2017.
https://doi.org/10.1016/s1474-6670(17)55464-6

K. H. Fasol and H. P. Jörgl. Principles of model building and identification, Automatica, vol. 16, no. 5, pp. 505–518, 1980.
https://doi.org/10.1016/0005-1098(80)90074-6

V. V. N. K. S. Koyyalamudi and Q. H. Nagpurwala. Stall Margin Improvement in a Centrifugal Compressor through Inducer Casing Treatment, Int. J. Rotating Mach., vol. 2016, pp. 1–19, 2016.
https://doi.org/10.1155/2016/2371524

H.-K. Li, X.-F. Wang, J.-G. Yang, P.-F. Zhao, and Y. Liu. The effect of impeller–diffuser interactions on diffuser performance in a centrifugal compressor, Eng. Appl. Comput. Fluid Mech., vol. 10, no. 1, pp. 565–577, 2016.
https://doi.org/10.1080/19942060.2016.1210028

M. Ebrahimi, Q. Huang, X. He, and X. Zheng. Effects of variable diffuser vanes on performance of a centrifugal compressor with pressure ratio of 8.0, Energies, vol. 10, no. 5, 2017.
https://doi.org/10.3390/en10050682

S. Budinis and N. F. Thornhill. Supercritical fluid recycle for surge control of CO2 centrifugal compressors, Comput. Chem. Eng., vol. 91, pp. 329–342, 2016.
https://doi.org/10.1016/j.compchemeng.2016.03.012

A. Marsan, I. Trébinjac, S. Moreau, and S. Coste. Active Flow Control in a Radial Vaned Diffuser for Surge Margin Improvement: A Multislot Suction Strategy, Int. J. Rotating Mach., vol. 2017, pp. 1–12, 2017.
https://doi.org/10.1155/2017/4120862

R. Matas, T. Syka, and O. Luňáček. Numerical and experimental modelling of the centrifugal compressor stage – setting the model of impellers with 2D blades, EPJ Web Conf., vol. 143, p. 02073, 2017.
https://doi.org/10.1051/epjconf/201714302073

K. Jones, A. Cortinovis, M. Mercangoez, and H. J. Ferreau. Distributed Model Predictive Control of Centrifugal Compressor Systems, IFAC-PapersOnLine, vol. 50, no. 1, pp. 10796–10801, 2017.
https://doi.org/10.1016/j.ifacol.2017.08.2343

H. Imani, M. R. Jahed-Motlagh, K. Salahshoor, A. Ramezani, and A. Moarefianpur. Robust decentralized model predictive control approach for a multi-compressor system surge instability including piping acoustic, Cogent Eng., vol. 5, no. 1, pp. 1–14, 2018.
https://doi.org/10.1080/23311916.2018.1483811

W. Du, Y. Li, L. Li, and G. Lorenzini. A quasi-one-dimensional model for the centrifugal compressors performance simulations, Int. J. Heat Technol., vol. 36, no. 2, pp. 391–396, 2018.
https://doi.org/10.18280/ijht.360202

N. Tengesdal, T. T. Kristoffersen, and C. Holden. Applied Nonlinear Compressor Control with Gain Scheduling and State Estimation, IFAC-PapersOnLine, vol. 51, no. 8, pp. 151–157, 2018.
https://doi.org/10.1016/j.ifacol.2018.06.370

C. Hansen, Dynamic Simulation of Compressor Control Systems, Aalborg University Esbjerg, 2008.

J. Liedman, Dynamic simulation of a centrifugal compressor system, Chalmers University of Technology, 2013.

L. Carlui and P. Aguilera, Subsea Wet Gas Compressor Dynamics, Norwegian University of Science and Technology, 2013.

A. Sedliak and T. Žáčik. Optimization of the gas transport in pipeline systems, Tatra Mt. Math. Publ., vol. 66, no. 1, pp. 103-120, 2016.
https://doi.org/10.1515/tmmp-2016-0024

D. De Wolf and Y. Smeers. The Gas Transmission Problem Solved by an Extension of the Simplex Algorithm, Manage. Sci., vol. 46, no. 11, pp. 1454–1465, 2003.
https://doi.org/10.1287/mnsc.46.11.1454.12087

C. Blum and G. R. Raidl. A Hybrid Meta-heuristic Approach for Natural Gas Pipeline Network Optimization, in Lecture Notes in Computer Science, 2016, no. August 2005.

P. J. Wong and R. E. Larson. Optimization of tree-structured natural-gas transmission networks, J. Math. Anal. Appl., vol. 24, no. 3, pp. 613–626, 1968.
https://doi.org/10.1016/0022-247X(68)90014-0

Kartashev, A., Martynov, A., Mashkov, O., Numerical and Experimental Studies of a Turbocharger Centrifugal Compressor for Combustion Engine Boost, (2018) International Review of Aerospace Engineering (IREASE), 11 (1), pp. 27-38.
https://doi.org/10.15866/irease.v11i1.13466

Solovyov, N., Valeev, A., Salikhov, A., Automated System for Substantiation of Commercial Production Recovery from Water-Flooded Gas Wells, (2018) International Review of Automatic Control (IREACO), 11 (3), pp. 107-112.
https://doi.org/10.15866/ireaco.v11i3.13670

Uman Putra, D., Penangsang, O., Soeprijanto, A., Non-Intrusive Load Monitoring Design Using K-Means Clustering Extreme Learning Machine, (2018) International Review on Modelling and Simulations (IREMOS), 11 (4), pp. 215-220.
https://doi.org/10.15866/iremos.v11i4.13969

Pamuji, F., Miyauchi, H., Maximum Power Point Tracking of Multi-Input Inverter for Connected Hybrid PV/Wind Power System Considering Voltage Limitation in Grid, (2018) International Review on Modelling and Simulations (IREMOS), 11 (3), pp. 143-150.
https://doi.org/10.15866/iremos.v11i3.14003

Dehnavifard, H., Radman, G., Kalyan, M., Design and Comparison of High-Speed Induction Machine and High-Speed Interior Permanent Magnet Machine, (2018) International Review on Modelling and Simulations (IREMOS), 11 (3), pp. 151-157.
https://doi.org/10.15866/iremos.v11i3.14112

N. Batayev. Prediction of Gas Turbine Parameters based on Machine Learning Regression Methods, Proc. 6th Int. Virtual Conf. Adv. Sci. Results, vol. 6, pp. 217–221, 2018.
https://doi.org/10.18638/scieconf.2018.6.1.495

N. Batayev. Gas turbine fault classification based on machine learning supervised techniques, in 14th International Conference on Electronics Computer and Computation, ICECCO 2018.
https://doi.org/10.1109/icecco.2018.8634719

R. A. Stanley and W. R. Bohannan. Dynamic Simulation of Centrifugal Compressor Systems, Proc. sixth Turbomach. Symp., pp. 123-132, 1977.

K. K. Botros, P. J. Campbell, and D. B. Mah. Dynamic Simulation of Compressor Station Operation Including Centrifugal Compressor and Gas Turbine, J. Eng. Gas Turbines Power, vol. 113, no. 2, p. 300, 2008.
https://doi.org/10.1115/1.2906563


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