An adaptive electrical resistance tomography sensor with flow pattern recognition capability

Abstract

The all traditional electrical resistance tomography (ERT) sensors have a static structure, which cannot satisfy the intelligent requirements for adaptive optimization to ERT sensors that is subject to flow pattern changes during the real-time detection of two-phase flow. In view of this problem, an adaptive ERT sensor with a dynamic structure is proposed. The electrodes of the ERT sensor are arranged in an array structure, the flow pattern recognition technique is introduced into the ERT sensor design and accordingly an ERT flow pattern recognition method based on signal sparsity is proposed. This method uses the sparse representation of the signal to express the sampling voltage of the ERT system as a sparse combination and find its sparse solution to achieve the classification of different flow patterns. With the introduction of flow identification information, the sensor has an intelligent function of adaptively and dynamically adapting the sensor structure according to the real-time flow pattern change. The experimental results show that the sensor can automatically identify four typical flow patterns: core flow, bubble flow, laminar flow and circulation flow with recognition rates of 91%, 93%, 90% and 88% respectively. For different flow patterns, the dynamically optimized sensor can significantly improve the quality of ERT image reconstruction.

Keywords

electrical resistance tomography / adaptive sensor / sparse representation / flow pattern identification

References

[1] ZhaiQ, YanK, ZhangY-y, ZhuY-s, WangY-tai. Air flow patterns and noise analysis inside high speed angular contact ball bearings [J]. Journal of Central South University, 2015, 22(9): 3358-3366

[2] WangM, DickinF J, MannR. Electrical resistance tomographic sensing systems for industrial applications [J]. Chemical Engineering Communications, 1999, 175(1): 49-70

[3] TanC, WangN-n, DongFeng. Oil-water two-phase pattern analysis with ERT based measurement and multivariate maximum Lyapunov exponent [J]. Journal of Central South University, 2016, 23(1): 240-248

[4] SardeshpandeM V, KumarG, AdityaT, RanadeV V. Mixing studies in unbaffled stirred tank reactor using electrical resistance tomography [J]. Flow Measurement & Instrumentation, 2016, 47: 110-121

[5] PolanskyJ, WangMi. Proper orthogonal decomposition as a technique for identifying two-phase flow pattern based on electrical impedance tomography [J]. Flow Measurement & Instrumentation, 2016, 53: 126-132

[6] WangP, GuoB-l, LiNan. Multi-index optimization design for electrical resistance tomography sensor [J]. Measurement, 2013, 46(8): 2845-2853

[7] FransoletE, CrineM, L’HommeG, ToyeD, MarchotP. Electrical resistance tomography sensor simulations: Comparison with experiments [J]. Measurement Science & Technology, 2002, 13(8): 1239-1247

[8] MaY-x, WangH, XuL-a, JiangC-zhen. Simulation study of the electrode array used in an ERT system [J]. Chemical Engineering Science, 1997, 52(13): 2197-2203

[9] GuJ, YinW-l, WangC, WangH-xiang. Modeling of the conductive ring electrical impedance tomography sensor [C]. International Conference on Electronic Measurement & Instruments, 2009496499

[10] DickinF, WangMi. Electrical resistance tomography for process applications [J]. Measurement Science & Technology, 1999, 7(3): 247-260

[11] WangH-x, WangJ, HuL, JiangW-w, HaoK-h, SongZ-jian. Optimal design of ERT/ECT dual-modality sensing electode array [J]. Journal of Tianjin University: Science and Technology, 2008, 41(8): 911-918(in Chinese)

[12] MeriboutM, Al-rawahiN Z, Al-naamanyA M, Ai-bemaniA, Ai-busaidiK, MeriboutA. A multisensor intelligent device for real-time multiphase flow metering in oil fields [J]. IEEE Transactions on Instrumentation & Measurement, 2010, 59(6): 1507-1519

[13] TeniouS, MeriboutM. A multimodal image reconstruction method using ultrasonic waves and electrical resistance tomography [J]. IEEE Transactions on Image Processing-A Publication of the IEEE Signal Processing Society, 2015, 24(11): 3512-3521

[14] KarkiB, FarajY, WangMi. Electrical conductivity based flow regime recognition of two-phase flows in horizontal pipeline [C]. World Congress on Industrial Process Tomography, 201618

[15] MengZ-z, HuangZ-y, WangB-l, JiH-f, LiH-q, YanYong. Air-water two-phase flow measurement using a Venturi meter and an electrical resistance tomography sensor [J]. Flow Measurement and Instrumentation, 2010, 21(3): 268-276

[16] WangPai. Analysis of sensitivity field of electrical resistance tomography system [C]. International Symposium on Computer, Consumer and Control, 201611061110

[17] MuraiT, KagawaY. Electrical impedance computed tomography based on a finite element model [J]. IEEE Transactions on Biomedical Engineering, 1985, 32(3): 177-184

[18] MairalJ, EladM, SapiroG. Sparse representation for color image restoration [J]. IEEE Transactions on Image Processing, 2008, 17(1): 53-69

[19] OliverosE R, CoelloG, Marrero-fernÁndezP, BuadesJ M, Jaume-i-capÓA. Evaluation of K-SVD method in facial expression recognition based on sparse representation problems [M]. Articulated Motion and Deformable Objects, 2016

[20] XiaoL-qing. A modified newton-raphson absolute image reconstruction algorithm for ERT [J]. Journal of Tianjin University: Science and Technology, 2015, 48(8): 734-741(in Chinese)

[21] LiX-y, HanQ, WangJ-m, WangQ, DuanX-jie. ERT image reconstruction based on improved CG method [J]. Chinese Journal of Scientific Instrument, 2016, 37(7): 1673-1679(in Chinese)

[22] YorkeyT J, WebsterJ G, TompkinsW J. Comparing reconstruction algorithms for electrical impedance tomography [J]. Biomedical Engineering IEEE Transactions, 1987, BME-34(11): 843-852