Microcontroller System for Oil Refinery Parameters Measurements Based on Piezoresistive and Strain Gauge Pressure Sensors

Abstract:

Article Preview

In oil refinery there is a variety of physical parameters such as pressure, flow rate and level that need to be measured. A microcontroller system is built based on PIC 16F877A, piezoresistive differential pressure DP sensor (24PC series) and strain gauge DP sensor (IDP-10) with ranges from 0 to 15psi. The results of the microcontroller system showed that; the percentage error for piezoresistive sensor in pressure from 0.43808% to 8.613 %, in flow rate from 0.21929% to 20.340%, and in level from 0.43808% to 2.5789%. While the percentage error for strain gauge sensor from 0.846% to 1.946% for pressure measurement, from 0.1% to 0.4% for flow rate measurement and from 0% to 0.64% for level measurement. The percentage error of the piezoresistive sensor is more than the percentage error of the strain gauge sensor: for pressure measurement by about 6.667%, for flow rate measurement by about 19.94% and for level measurement by about 1.9389%. Fuzzy logic is used to predict the output surface of pressure, flow rate, and level measurements.

Info:

Periodical:

Edited by:

Xiong Zhou and Honghua Tan

Pages:

1133-1138

Citation:

I. Morsi and L. M. E. D. Rasheed, "Microcontroller System for Oil Refinery Parameters Measurements Based on Piezoresistive and Strain Gauge Pressure Sensors", Applied Mechanics and Materials, Vols. 249-250, pp. 1133-1138, 2013

Online since:

December 2012

Export:

Price:

$38.00

[1] Zhangwei Ling, Hongliang Zhou, and Hongjian Zhang, Nondestructive pressure measurement in vessels using Rayleigh waves and LCR waves, IEEE transactions on instrumentation measurement, vol. 58, no. 5, May (2009).

DOI: https://doi.org/10.1109/imtc.2008.4547123

[2] M. A. Atmanand and M. S. Konnur, A novel method of using a control valve for measurement and control of flow, IEEE transactions on instrumentation measurement, vol. 48, no. 6, December (1999).

DOI: https://doi.org/10.1109/19.816140

[3] Hüseyin Canbolat, A novel level measurement technique using three capacitive sensors for liquids, IEEE transactions on instrumentation measurement, vol. 58, no. 10, October (2009).

DOI: https://doi.org/10.1109/tim.2009.2019715

[4] Zhan Mei; Jihong Liu and Jinlin Xu, Design of temperature measure system for variable sensitive temperature range", IEEE transactions on control and decision conference, CCDC , 09, Chinese. 19, June (2009).

DOI: https://doi.org/10.1109/ccdc.2009.5192621

[5] Harprit Singh Sandhu, Making PIC microcontroller instruments and controllers, (2009).

[6] Iman Morsi, A microcontroller based on multi sensors data fusion and artificial intelligent technique for gas identification, the 33rd annual conference of the IEEE industrial electronics society (IECON), November 5-8/2007, Taipei, Taiwan.

DOI: https://doi.org/10.1109/iecon.2007.4460098

[7] J. Wesley Hines, Fuzzy and neural approaches in engineering,. John Wiley & sons, INC, (1997).

[8] Timothy J. Ross, Fuzzy logic with engineering applications, McGraw-Hill, Inc, (1995).

[9] Lotfi A. Zadeh, The role of fuzzy logic modeling, identification and control,. Modeling, identification and control, 1994, vol. 15, no. 3, 191-203.

DOI: https://doi.org/10.4173/mic.1994.3.9

[10] Thomas A. Hughes, ‏Measurement and control basics, 3rd Edition‏, (2002).

[11] William C. Dunn, ‏Fundamentals of industrial instrumentation and process control‏, (2005).