Flow regimes identification of particles conyveying in pneumatic pipeline using electric charge tomography and fuzzy logic technique

Abstract

A detailed and accurate measurement technique for metering solids bulk pneumatic transportation often creates challenging problems to engineer and scientist. Problems occurred particularly due to spatial and temporal fluctuations of both the solid velocity and concentration during pneumatic transportation. During this development, it leads to the use of tomographic measurement techniques. A well-liked trend in the development of tomographic measurement techniques for research and production is the use of electrical techniques. One of the electrical tomographic techniques is electrical charge tomography or also known as electrodynamic tomography which offers inexpensive, non-invasive, simple and robust method for measuring particulate solids flow in pneumatic pipeline. In this research electrical charge tomography measurement is made by placing an array of 16 electrodynamic sensors evenly around circumference of pipe to detect the existence of inherent charge on the moving particles which passes through the pipe. The converted voltage signals received from the 16 electrodynamic transducers are captured and stored by data acquisition card which acts as interface between the computer and the transducers. The two most commonly methods for image reconstruction namely linear back projection algorithm and filtered back projection algorithm are employed to produce tomographic image. The signals captured are in range of mass flow rate between 110g/s until 500g/s. Matlab is exploited to compute the image reconstruction and visualise the tomogram for concentration distribution across a given cross section of pneumatic pipeline. Baffles of diverse shapes are inserted to create various flow regimes whereby fuzzy logic technique is used to identify these flow regimes. The major conclusions drawn from this research were the successful use of the fuzzy logic technique for flow regime identification and producing an improved image of filtered back concentration profiles for each flow regime.