Application of fuzzy logic and electrodynamic sensors as flow pattern identifier

Rahmat, M. F.; Kamaruddin, N. S.

doi:10.1108/02602281211209419

Cited by 12 publications

(4 citation statements)

References 15 publications

(25 reference statements)

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“…Ten samples of the highest voltage outputs were taken at every flow rate in order to train and test the identification model. Baffles of different shapes were sequentially installed above the measurement sensors in order to create training patterns for the flow regime identification (Rahmat et al, 2010;Rahmat and Kamaruddin, 2012). The baffles were applied to shape the desired flow regime inside the pipe.…”

Section: Experimental Designmentioning

confidence: 99%

Particles flow identification in pipeline using adaptive network-based fuzzy inference system and electrodynamic sensors

et al. 2014

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Purpose – An identification model for materials flow through a pipeline is presented in this paper. The development of the model involves fuzzy C-means clustering, in which different flow regimes can be identified by every adaptive network-based fuzzy inference system (ANFIS). The paper aims to discuss these issues. Design/methodology/approach – For experimentation, 16 electrodynamic sensors were used to monitor and measure the charge carried by dense particles flow through a pipeline in a vertical gravity flow rig system. Four ANFIS models were also used simultaneously to provide the expected output on thresh-holding and were evaluated for ten different flow regimes, which produced satisfactory results at high flow rate. Findings – The observations made on the four ANFIS models in the flow identification experimentation (in ten different flow regimes) have shown convincing and satisfactory results at high-flow rate of the particles. Originality/value – Electrodynamic sensors have shown strong sensing capability in identification of dense-particle flows within a conveyor; and also proven capability to operate effectively in harsh industrial environments due to their firm and simple structures. Moreover, it has been verified that these sensors can conveniently be applied in flow regime identification of solid particles.

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Section: Experimental Designmentioning

confidence: 99%

Particles flow identification in pipeline using adaptive network-based fuzzy inference system and electrodynamic sensors

et al. 2014

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“…The main problem of tomographic imaging of multiphase flow is the quality of resultant charge distribution within the sensing zone [9]. Several efforts have been made to improve the quality of the image as reported in [10,11] with no specific solution to the problem of particle distribution within the sensing zone. Concentration profile or the tomographic imaging is one of the inverse problems characterized by ill-posed and ill-conditioned phenomenon, where small changes in the data cause arbitrarily large changes in the solution making it unstable [8].…”

Section: Introductionmentioning

confidence: 99%

Determination of Concentration Profile for Flowing Solid Particles in Pipeline Using Electric Charge Tomography System

Thuku

Rahmat

Wahab

et al. 2014

Mathematical Problems in Engineering

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Tomography aims to present an image of a cross-sectional distribution of materials in some regions of interest such as cross-section of a pipeline or process vessel. This paper presents the concentration profiles of solid particles across a conveying pipeline obtained using tomographic imaging. In the paper, 16 electrodynamic sensors were installed around the circumference of a pipeline to capture electric charges carried by the particles moving through the pipeline under gravity. The Cartesian coordinate system used to derive the system equation gave an accurate charge distribution while the meshing technique of the finite element method applied miniaturized the pixel sizes within the sensing zone. The problem of unstable matrix and weak signal response around the center of the pipe cross-section, normally associated with the electric charge tomography system, was addressed using matrix compression through transposition and filtering. The pro rata distribution method mostly applied in the financial accounting analysis was used in the final stage. An algorithm for realization of the concepts was developed using MATLAB. The qualities of the resulting images for four different flow regimes provide good quality images representing the distribution of the particles across the pipeline cross-section.

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“…The development of pneumatic systems has been an attraction in recent years. This system is widely used in industrial and commercial applications due to its lower cost, compact size, high power-to-weight ratio, reliability, low maintenance, and because it is fast and accurate (Dan et al, 2007;Ahn and Yokota, 2005;Rahmat and Kamaruddin, 2012). Currently, pneumatic technology combines with many types of sensing array, which consists of sensing elements that ideally respond to a form of stimulus such as pressure, force, heat, displacement, etc.…”

Section: Introductionmentioning

confidence: 99%

Real-time position control of intelligent pneumatic actuator (IPA) system using optical encoder and pressure sensor

et al. 2013

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Purpose – Intelligent pneumatic actuator (IPA) is a new generation of actuator developed for Research and Development (R&D) purposes in the academic and industrial fields. The purpose of this paper is to show the application of optical encoder and pressure sensor in IPA, to develop a real-time model similar to the existing devices, and to assess the position control performance using a proportional-integrative (PI) controller and a bang-bang controller in real-time. Design/methodology/approach – A micro optical encoder chip is used to detect cylinder rod position by reading constructed laser stripes on a guide rod, whereas a pressure sensor is used to detect the chamber pressure reading. To control the cylinder movements by manipulating pulse-width modulation (PWM) cycles, two unit valves of two ports and two positions were used. A PI controller and a bang-bang controller are used with suitable gain value to drive the valve using PWM to achieve the target actuator position. Findings – The results show the experimental results of the closed-loop position tracking performance of the system using a data acquisition (DAQ) card over MATLAB software. Originality/value – This paper presents a real-time model used to replace the microcontroller-based system from previous IPA design. The paper proposes two control strategies, PI and bang-bang, to control position using encoder and pressure reading.

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Application of fuzzy logic and electrodynamic sensors as flow pattern identifier

Cited by 12 publications

References 15 publications

Particles flow identification in pipeline using adaptive network-based fuzzy inference system and electrodynamic sensors

Particles flow identification in pipeline using adaptive network-based fuzzy inference system and electrodynamic sensors

Determination of Concentration Profile for Flowing Solid Particles in Pipeline Using Electric Charge Tomography System

Real-time position control of intelligent pneumatic actuator (IPA) system using optical encoder and pressure sensor

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