A Sub-Optimal Look-Up Table Based on Fuzzy System to Enhance the Reliability of Coriolis Mass Flow Meter

Document Type : Research Article


1 PHD student of Electrical Engineering, Tehran University, Tehran, Iran.

2 Assistant Professor of Mechanical Engineering, Amirkabir University of Technology, Tehran, Iran


Coriolis mass flow meters are one of the most accurate tools to measure the mass flow in the industry. However, two-phase mode (gas-liquid) may cause severe operating difficulties as well as decreasing certitude in measurement. This paper presents a method based on fuzzy systems to correct the error and improve the reliability of these sensors in the presence of two-phase model fluid. Definite available flow meter parameters are given to designed fuzzy system as inputs, and error is estimated as its output. In the proposed method, to decrease the number of rules, data are clustered using K-means clustering algorithm. The ability of this method in error correction is shown by testing it on real experimental data and compared with the least square method.


[1] R. C. Baker, "Coriolis flowmeters: industrial practice and published information," Flow Measurement and Instrumentation, vol. 5, pp. 229-246, 1994.
[2] G. Oddie and J. R. A. Pearson, "Flow-rate measurement in two-phase flow," Annu. Rev. Fluid Mech., vol. 36, pp. 149-172, 2004.
[3] R. P. Evans, J. G. Keller, A. Stephens, and J. Blotter, "Two-phase mass flow measurement using noise analysis," Idaho National Laboratory (INL)1999.
[4] Y. Mi, M. Ishii, and L. Tsoukalas, "Flow regime identification methodology with neural networks and two-phase flow models," Nuclear Engineering and Design, vol. 204, pp. 87-100, 2001.
[5] J. Reimann, H. John, and U. Müller, "Measurements of two-phase mass flow rate: a comparison of different techniques," International Journal of Multiphase Flow, vol. 8, pp. 33-46, 1982.
[6] M. Meribout, N. Z. Al-Rawahi, A. M. Al-Naamany, A. Al-Bimani, K. Al Busaidi, and A. Meribout, "An Accurate Machine for Real-Time Two-Phase Flowmetering in a Laboratory-Scale Flow Loop," Instrumentation and Measurement, IEEE Transactions on, vol. 58, pp. 2686-2696, 2009.
[7] R. Liu, M. Fuent, M. Henry, and M. Duta, "A neural network to correct mass flow errors caused by two-phase flow in a digital coriolis mass flowmeter," Flow Measurement and Instrumentation, vol. 12, pp. 53-63, 2001.
[8] A. Skea and A. Hall, "Effects of gas leaks in oil flow on single-phase flowmeters," Flow Measurement and Instrumentation, vol. 10, pp. 145-150, 1999.
[9] V. A. Lari and F. Shabaninia, "Error correction of a coriolis mass flow meter in two-phase flow measurment using Neuro-Fuzzy," in Artificial Intelligence and Signal Processing (AISP), 2012 16th CSI International Symposium on, 2012, pp. 611-616.
[10] M. Henry, D. Clarke, N. Archer, J. Bowles, M. Leahy, R. Liu, et al., "A self-validating digital Coriolis mass-flow meter: an overview," Control engineering practice, vol. 8, pp. 487-506, 2000.
[11] Z. Feng, Q. Wang, and K. Shida, "A review of self-validating sensor technology," Sensor Review, vol. 27, pp. 48-56, 2007.
[12] M. N. Al-Khamis, A. A. Al-Nojaim, and M. A. Al-Marhoun, "Performance evaluation of coriolis mass flowmeters," Journal of energy resources technology, vol. 124, pp. 90-94, 2002.
[13] M. N. Al-Khamis, A. A. Al-Nojaim, and M. A. Al-Marhoun, "Performance evaluation of coriolis mass flowmeters," Journal of energy resources technology, vol. 124, p. 90, 2002.
[14] B. Safarinejadian, M. A. Tajeddini, and L. Mahmoodi, "A New Fuzzy Based Method for Error Correction of Coriolis Mass Flow Meter in Presence of Two-phase Fluid."
[15] J. Zarei, M. A. Tajeddini, and H. R. Karimi, "Vibration analysis for bearing fault detection and classification using an intelligent filter," Mechatronics, vol. 24, pp. 151-157, 2014.
[16] M. Anklin, W. Drahm, and A. Rieder, "Coriolis mass flowmeters: Overview of the current state of the art and latest research," Flow Measurement and Instrumentation, vol. 17, pp. 317-323, 2006.
[17] L. A. Zadeh, "Fuzzy sets," Information and control, vol. 8, pp. 338-353, 1965.
[18] L.-X. Wang, A Course in Fuzzy Systems: Prentice-Hall press, USA, 1999.
[19] M. Tang, H. W. Yang, W. D. Hu, and W. X. Yu, "Construction of Mamdani type probabilistic fuzzy system," Systems Engineering and Electronics, vol. 34, pp. 323-327, 2012.
[20] L. X. Wang and J. M. Mendel, "Generating fuzzy rules by learning from examples," Systems, Man and Cybernetics, IEEE Transactions on, vol. 22, pp. 1414-1427, 1992.
[21] L. X. Wang and J. M. Mendel, "Fuzzy basis functions, universal approximation, and orthogonal least-squares learning," Neural Networks, IEEE Transactions on, vol. 3, pp. 807-814, 1992.
[22] A. K. Jain, "Data clustering: 50 years beyond K-means," Pattern Recognition Letters, vol. 31, pp. 651-666, 2010.
[23] V. Patel and R. Mehta, "Data Clustering: Integrating Different Distance Measures with Modified k-Means Algorithm," 2012, pp. 691-700.
[24] S. Chiu, "Method and software for extracting fuzzy classification rules by subtractive clustering," in Fuzzy Information Processing Society, 1996. NAFIPS., 1996 Biennial Conference of the North American, 1996, pp. 461-465.
[25] N. R. Pal and D. Chakraborty, "Mountain and subtractive clustering method: improvements and generalizations," International Journal of Intelligent Systems, vol. 15, pp. 329-341, 2000.
[26] D.-W. Kim, K. Lee, D. Lee, and K. H. Lee, "A kernel-based subtractive clustering method," Pattern Recognition Letters, vol. 26, pp. 879-891, 2005.
[27] J. A. Hartigan, Clustering algorithms: John Wiley & Sons, Inc., 1975.
[28] A. K. Jain, M. N. Murty, and P. J. Flynn, "Data clustering: a review," ACM computing surveys (CSUR), vol. 31, pp. 264-323, 1999.
[29] K. Mao, "Fast orthogonal forward selection algorithm for feature subset selection," Neural Networks, IEEE Transactions on, vol. 13, pp. 1218-1224, 2002.
[30] O. Nelles, Nonlinear system identification: from classical approaches to neural networks and fuzzy models: Springer, 2001.
[31] L. X. Wang, A Course on Fuzzy Systems: Prentice-Hall press, USA, 1999.