Determination of multi-component flow process parameters based on electrical capacitance tomography data using artificial neural networks

Measurement Science and Technology

Volumn 13, Issue 12, 2002, Pages 1815-1821

  Mohamad Saleh, J ^a   Hoyle, B S ^a  

^a UNIVERSITY OF LEEDS   (United Kingdom)

Author keywords

Electrical capacitance tomography; Multi component flows; Neural networks; Process interpretation

Indexed keywords

CAPACITANCE; COMPUTER SIMULATION; NEURAL NETWORKS; TOMOGRAPHY;

ELECTRICAL CAPACITANCE TOMOGRAPHY;

FLOW MEASUREMENT;

ELECTRICAL TECHNIQUE; FLOW MEASUREMENT; MULTIPHASE FLOW; NEURAL NETWORK; TOMOGRAPHY;

EID: 0036994410     PISSN: 09570233     EISSN: None     Source Type: Journal    
DOI: 10.1088/0957-0233/13/12/303     Document Type: Article

References (24)

1. Beck M S, Byars M, Dyakowski T, Waterfall R, He R, Wang S J and Yang W Q 1997 Principles and industrial applications of electrical capacitance tomography Meas. Control 30 197-200
2. Reinecke N and Mewes D 1996 Recent developments and industrial/research applications of capacitance tomography Meas. Sci. Technol. 7 233-46
3. Williams R A and Beck M S (ed) 1995 Process Tomography: Principles, Techniques and Applications (London: Butterworth-Heinemann)
4. Yang W Q, Beck M S and Byars M 1995 Electrical capacitance tomography-from design to applications Meas. Control 28 261-6
5. Xie C G, Plaskowski A and Beck M S 1989 8-electrode capacitance system for two-component flow identification: tomographic flow imaging IEE Proc. 136 173-83
6. Isaksen Ø 1996 A review of reconstruction techniques for capacitance tomography Meas. Sci. Technol. 7 325-37
7. Chen Q, Hoyle B S and Strangeways H J 1992 Electrical field interaction and an enhanced reconstruction algorithm in capacitance process tomography Proc. 1st ECAPT Conf. (Manchester, 1994) pp 205-12
8. Reinecke N and Mewes D 1994 Resolution enhancement for multi-electrode capacitance sensors Reviewed Proc. 3rd ECAPT Conf. (Porto. 1994)
9. Isaksen Ø and Nordtvedt J E 1994 An implicit model based reconstruction algorithm for use with a capacitance tomography system Proc. 3rd ECAPT Conf. (Porto, 1994) pp 215-26
10. Isaksen Ø and Nordtvedt J E 1993 A new reconstruction algorithm for process tomography Meas. Sci. Technol. 4 1464-75
11. Haykin S 1999 Neural Networks: a Comprehensive Foundation (London: Macmillan)
12. Bishop C M 1994 Neural networks and their applications Rev. Sci. Instrum. 65 1803-32
13. Duggan P M and York T A 1995 Tomographic image reconstruction using RAM-based neural networks Process Tomography-Implementation for Industrial Processes: Proc. 4th ECAPT Conf. (Bergen, 1995) pp 411-19
14. Duggan P M 1997 Application of RAM-based neural networks to electrical capacitance tomography PhD Thesis University of Manchester Institute of Science and Technology
15. Nooralahiyan A Y, Hoyle B S and Bailey N J 1995 Performance of neural network in capacitance-based tomographic process measurement systems Meas. Control 28 109-12
16. Nooralahiyan A Y 1996 Artificial neural networks for image reconstruction and interpretation in electrical capacitance tomography PhD Thesis University of Leeds
17. Nooralahiyan A Y and Hoyle B S 1997 Three-component tomographic flow imaging using artificial neural network reconstruction Chem. Eng. 52 2139-48
18. Williams P and York T 1999 Hardware implentation of RAM-based neural networks for tomographic data processing IEE Proc. Comput. Digit. Tech. 146 114-18
19. Sun T D, Mudde R, Schouten J C, Scarlett B and van den Bleek C M 1999 Image reconstruction of an electrical capacitance tomography system using an artificial neural network Proc. 1st World Congr. on Industrial Process Tomography (Buxton, 1999) pp 174-80
20. Mohamad-Saleh J, Hoyle B S, Podd F J W and Spink D M 2001 Direct process estimation from tomographic data using artificial neural systems SPIE J. Electron. Imaging 10 646-52
21. Spink D M 1996 Direct finite element solution for the capacitance, conductance or inductance, and force in linear electrostatic and magnetostatic problems Int. J. Comput. Math. Electr. Electron. Eng. 15 70-84
22. Jolliffe I T 1986 Principal Components Analysis (New York: Springer)
23. MacKay D J C 1992 Bayesian interpolation Neural Comput. 4 415-47
24. Forsee F D and Hagan M T 1994 Gauss-Newton approximation to Bayesian learning Proc. Int. Joint Conf. on Neural Networks pp 1930-5