Modeling and Simulation Study for Black Powder Detection in Gas-Oil Pipelines Using Magnetic Particle Imaging and its Hardware Realization

Abstract

Abstract This paper suggests a new Magnetic Particle Imaging (MPI) technique for two dimensional (2D) tomographic imaging to detect the presence of flowing solid particles (i.e. black powder which consist of paramagnetic nanoparticles (MNPs)) passing through gas-oil pipelines. The hardware consists of a ring of a modified Halbach array of 24 permanent magnets, in addition to two surrounding Helmholtz coil pairs, and a receiving coil, which are evenly distributed across a cross-section of the probe. With the application of static and dynamically moving drive fields, the MPI utilizes the full benefit of superparamagnetic iron oxide nanoparticles (SPIONs) by providing a linear response when they are exposed to a relatively low magnetic field and no response when they are saturated. The scanning of region of interest with the field-free line (FFL) instead of the field-free point (FFP) is motivated by the fact that a high intensity DC current is required for FFP, which is not tolerated in oil-gas pipelines where the maximal current should not exceed few Amps. With the presence of oscillatory magnetic field, SPIONs react with a non-linear magnetization response which is further measured by the receiving coil. A 2D Image reconstruction is then performed using frequency-based image reconstruction process. The hardware design, specifically the sizing of permanent magnets with regard to their relative position and dimensions, was refined following an optimization technique based on Particle Swarm Optimization (PSO) technique. The assessment of the system using finite element method indicates that the system can reconstruct the 2D profile of the SPOIN with a mean absolute error of prediction of less than 4 % and 12% using FFL and FFP methods respectively.