Abstract
Much research has already been devoted to various electrical capacitance techniques for determining the flow of material or to define the material distribution. Simple electrical capacitive throughput sensors, but also very complex electrical capacitance tomograph sensors were tested. An interesting compromise may be segmented capacitance sensor (SCS). In this paper, a mathematical model of SCS is verified. Two mathematical models are compared. The first model simplifies the problem by the use of the electrostatic field. Using the second model a variable electric field at low frequencies is described. It was found that, for an easier description of the SCS electric field, this field can be considered as the electrostatic field. Measurements carried out, for the most part supported the correctness of the mathematical model. Some deviations were probably caused by transferring the problem to 2D.
Similar content being viewed by others
References
Stafford JV, Ambler B, Lark RM, Catt J (1996) Mapping and interpreting the yield variation in cereal crops. Comput Electr Agric 14(2–3):101–119
Savoie P, Lemire P, Thériault R (2002) Evaluation of five sensors to estimate mass-flow rate and moisture of grass in a forage harvester. Appl Eng Agric 18(3):389–397
Martel H, Savoie P (1999) Sensors to measure forage mass flow and moisture continuously. ASAE paper No. 991050
Kumhála F, Kvíz Z, Kmoch J, Prošek V (2007) Dynamic laboratory measurement with dielectric sensor for forage mass flow determination. Res Agric Eng 53(4):149–154
Kumhála F, Prošek V, Blahovec J (2009) Capacitive throughputsensor for sugarbeets and potatoes. Biosyst Eng 102(1):36–43
Kumhála F, Prošek V, Kroulík M (2010) Capacitive sensor for chopped maize throughput measurement. Comput Electr Agric 70(1):234–238
Williams RA, Xie CG (1993) Tomographic techniques for characterizing particulate process. Part Part Syst Charact 10:252–261
Gamio JC (2002) A comparative analysis of single- and multiple-electrode excitation methods in electrical capacitance tomography. Meas Sci Technol 13:1799–1809
Waterfall RC, He R, White NB, Beck CB (1996) Combustion imaging from electrical impedance measurements. Meas Sci Technol 7:369–374
Williams RA, Luke SP, Ostrowski KL, Bennett MA (2000) Measurement of bulk particulates on belt conveyor using dielectric tomography. Chem Eng J 77(1–2):57–63
Yang WQ (2010) Design of electrical capacitance tomography sensors. Meas Sci Technol 21:1–13
Yang WQ, Liu S (1999) Electrical capacitance tomography with a square sensor. In: 1st World congress on industrial process tomography. Buxton, Greater Manchester, UK
Somerville A, Evans I, York T (1999) Preliminary studies of planar capacitance tomography. In: 1st World congress on industrial process tomography. Buxton, Greater Manchester, UK
Dong XY, Guo SQ (2009) Modelling planar array sensor for electrical capacitance tomography. In: Proceedings of second international conference on modelling and simulation. Manchester, UK
Lee KM, Foong S (2010) Lateral optical sensor with slip detection for locating live products on moving conveyor. IEEE Trans Autom Sci Eng 7(1):123–132
Schmittmann O, Schmitz S, Kromer K-H (2001) Heterogenity and site-specific yield-monitoring of sugar beets. I.I.R.B.-Meeting ‘Plant and Soil and Agricultural’, Lüttewitz
Konrad A, Graovac M (1996) The finite modeling of conductors and floating potentials. IEEE Trans Magn 32(5):4329–4331
Xie CG, Huang SM, Hoyle BS, Thorn R, Lenn C, Snowden D, Beck MS (1992) Electrical capacitance tomography for flow imaging-systemmodel for development of image reconstruction algorithms and design of primary sensors. In: IEE Proceedings-g, vol 139. No I, pp 89–98
Williams RA, Beck MS (eds) (1995) Process tomography, principles, techniques and applications. Butterworth-Heinemann, Oxford
Guo Z, Shao F, Lv D (2009) Sensitivity matrix construction for electrical capacitance tomography based on the difference model. Flow Meas Instrum 20:95–102
Yang WQ, Conway WF (1998) Measurement of sensitivity distributions of capacitance tomography sensors. Rev Sci Instrum 69(1):233–236
Acknowledgments
This work was supported by the Internal Grant Agency – Faculty of Engineering, Czech University of Life Sciences Prague, Project No. 31160/1312/3111.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Lev, J., Mayer, P., Wohlmuthová, M. et al. The mathematical model of experimental sensor for material distribution detecting on the conveyor. Computing 95 (Suppl 1), 521–536 (2013). https://doi.org/10.1007/s00607-012-0273-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00607-012-0273-1