Abstract
To obtain the ideal electrostatic sensor, it is necessary to optimize the electrode size. A new technique for the optimization of various sizes and shapes of electrodes is presented in this paper. The particle swarm optimization (PSO) technique, which is both heuristic and computational in nature, is proposed to overcome this problem. It was necessary to have uniform spatial sensitivity to lessen the impact of the flow system. Hence, electrodes with distinct shapes, including circular ring, quarter ring and rectangular electrodes, were applied, and their characteristics were optimized to attain a spatial sensitivity that was more uniform. The uniformity of the spatial sensitivity of electrodes is influenced by several factors, such as their length, width and thickness. As such, spatial sensitivity was regarded as the fitness function in the PSO method, and the other factors were investigated as PSO parameters. From observations, the spatial sensitivity of the circular ring electrode is more uniform than that of other electrodes. In addition, the optimal length of circular ring electrode is 5.771 mm, whereas the optimal thickness of this electrode is 4.746 mm. Based on experimental tests, the total induced current, correlation velocity and spatial sensitivity distribution of electrostatic sensors were captured. A close agreement between experimental and optimization results verify that the proposed method is feasible for optimizing the electrode size of electrostatic sensors.
Similar content being viewed by others
References
Rahmat M, Sabit H, Abdul Rahim R (2010) Application of neural network and electrodynamic sensor as flow pattern identifier. Sens Rev 30(2):137–141
Rahmat M, Kamaruddin N (2012) Application of fuzzy logic and electrodynamic sensors as flow pattern identifier. Sens Rev 32(2):123–133
Khairalla M, Rahmat M, Abdul Wahab N, Thuku I, Tajdari T, Amuda Yusuf A (2014) Particles flow identification in pipeline using adaptive network-based fuzzy inference system and electrodynamic sensors. Sens Rev 34(2):201–208
Yan Y (1996) Mass flow measurement of bulk solids in pneumatic pipelines. Meas Sci Technol 7(12):1687
Zhang J, Coulthard J (2005) Theoretical and experimental studies of the spatial sensitivity of an electrostatic pulverised fuel meter. J Electrost 63(12):1133–1149
Peng L, Zhang Y, Yan Y (2008) Characterization of electrostatic sensors for flow measurement of particulate solids in square-shaped pneumatic conveying pipelines. Sensors Actuators A Phys 141(1):59–67
Xu C, Tang G, Zhou B, Wang S (2009) The spatial filtering method for solid particle velocity measurement based on an electrostatic sensor. Meas Sci Technol 20(4):045404
Krabicka J, Yan Y Finite element modelling of intrusive electrostatic sensors for the measurement of pulverised fuel flows. In: Instrumentation and Measurement Technology Conference Proceedings, 2007. IMTC 2007. IEEE, 2007. IEEE, pp 1-4
Xu C, Zhou B, Wang S (2010) Dense-phase pneumatically conveyed coal particle velocity measurement using electrostatic probes. J Electrost 68(1):64–72
Qian X, Cheng X, Zhang L, Cao M (2011) The sensitivity analysis and optimization design of the electrostatic inductive measuring device for weak charge measurement of coal mine dust. In: Computer Science for Environmental Engineering and EcoInformatics. Springer, pp 83-90
Tizhe Thuku I, Fua’ad Rahmat M, Abdul Wahab N, Tajdari T, Amuda Yusuf A (2013) 2-D finite-element modeling of electrostatic sensor for tomography system. Sens Rev 33(2):104–113
Kennedy J, Eberhart RC A discrete binary version of the particle swarm algorithm. In: Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 I.E. International Conference on, 1997. IEEE, pp 4104-4108
Ozcan E, Mohan CK Particle swarm optimization: surfing the waves. In: Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on, 1999. IEEE
Suganthan PN Particle swarm optimiser with neighbourhood operator. In: Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on, 1999. IEEE
Al-kazemi BSNm, Mohan CK (2000) Multi-phase discrete particle swarm optimization.
Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. Evolutionary Computation, IEEE Transactions on 6(1):58–73
Mendes R, Kennedy J, Neves J (2004) The fully informed particle swarm: simpler, maybe better. Evolutionary Computation, IEEE Transactions on 8(3):204–210
Bai Q (2010) Analysis of particle swarm optimization algorithm. Computer and information science 3(1):p180
Ishaque K, Salam Z, Amjad M, Mekhilef S (2012) An improved particle swarm optimization (PSO)-based MPPT for PV with reduced steady-state oscillation. Power Electronics, IEEE Transactions on 27(8):3627–3638
Shao J, Krabicka J, Yan Y (2010) Velocity measurement of pneumatically conveyed particles using intrusive electrostatic sensors. Instrumentation and Measurement, IEEE Transactions on 59(5):1477–1484
Rahmat MF, Yaw WL (2012) Electrostatic sensor for real–time mass flow rate measurement of particle conveying in p n eumatic pipeline. Jurnal Teknologi 41(1):91–104
Heydarianasl M, Rahmat MFa Modelling and simulation of different electrode size for electrostatic sensors. In: Control Conference (ASCC), 2015 10th Asian, 2015. IEEE, pp 1-7
Chen Z, Tang X, Hu Z, Yang Y (2014) Investigations into sensing characteristics of circular thin-plate electrostatic sensors for gas path monitoring. Chin J Aeronaut 27(4):812–820
Xu C, Li J, Gao H, Wang S (2012) Investigations into sensing characteristics of electrostatic sensor arrays through computational modelling and practical experimentation. J Electrost 70(1):60–71
Kennedy J, Mendes R (2002) Population structure and particle swarm performance.
Krabicka J, Yan Y Optimised design of intrusive electrostatic sensors for the velocity measurement of pneumatically conveyed particles. In: Instrumentation and Measurement Technology Conference, 2009. I2MTC’09. IEEE, 2009. IEEE, pp 341-345
Tajdari T, Rahmat MF, Wahab NA (2014) New technique to measure particle size using electrostatic sensor. J Electrost 72(2):120–128
Heydarianasl M, Rahmat MFa (2014) The effects of distance on velocity measurement for different shapes of electrostatic sensor electrodes. Jurnal Teknologi 69 (8)
Yan Y, Byrne B, Woodhead S, Coulthard J (1995) Velocity measurement of pneumatically conveyed solids using electrodynamic sensors. Meas Sci Technol 6(5):515
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Heydarianasl, M., Rahmat, M.F. Optimization of electrostatic sensor electrodes using particle swarm optimization technique. Int J Adv Manuf Technol 89, 905–919 (2017). https://doi.org/10.1007/s00170-016-9076-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-016-9076-4