Abstract-This paper presents a design of tuning mechanism of Proportional Integral

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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.

Purpose -Circular pipelines are mostly used for pneumatic conveyance in industrial processes. For optimum and efficient production in industries that use a pipeline for conveyance, tomographic image of the transport particles is paramount. Sensing mechanism plays a vital role in process tomography. The purpose of this paper is to present a two-dimensional (2-D) model for sensing the characteristics of electrostatic sensors for electrical charge tomography system. The proposed model uses the finite-element method. Design/methodology/approach -The domain is discretized into discrete shapes, called finite elements, by using a MATLAB. Each of these elements is taken as image pixels, on which the electric charges carried by conveyed particles are transformed into equations. The charges' interaction and the sensors installed around the circumference, at the sensing zone of the conveying pipeline are related by the proposed model equations. A matrix compression technique was also introduced to solve the problem of unevenly sensing characteristics of the sensors due to elements' number's concentration. The model equations were used to simulate the modeled electrostatic charge distribution carried by the particles moving in the pipeline. Findings -The simulated results show that the proposed sensors are highly sensitive to electrostatic charge at any position in the sensing zone, thereby making it a good candidate for tomographic image reconstruction. Originality/value -Tomographic imaging using finite element method is found to be more accurate and reliable compared to linear and filtered back projection method.