Physical properties and X-ray tomography images of five different granular materials: glass bead, glass grit, short grain white rice, sorghum and clay granules (Seramis) are investigated to select the most promising materials for numerical simulations and time-lapse X-ray tomography imaging. The examined materials represent granular materials of non-biological origin (glass bead, glass grit and Seramis) and granular plant materials (rice and sorghum). The choice of materials was dictated by their mechanical and packing properties, on one hand, and 3D imaging qualities of granular materials and quantitative particle shape analysis, on the other hand. It was found that materials of biological origin, i.e. rice and sorghum, showed promising characteristics and may be considered as the most appropriate materials for comparison between time-lapse X-ray tomography experiments and numerical simulation.

A new approach to solve the inverse problem in electrical capacitance tomography is presented. The proposed method is based on an artificial neural network to estimate three different parameters of a circular object present inside a pipeline, i.e. radius and 2D position coordinates. This information allows the estimation of the distribution of material inside a pipe and determination of the characteristic parameters of a range of flows, which are characterised by a circular objects emerging within a cross section such as funnel flow in a silo gravitational discharging process. The main advantages of the proposed approach are explicitly: the desired characteristic flow parameters are estimated directly from the measured capacitances and rapidity, which in turn is crucial for online flow monitoring. In a classic approach in order to obtain these parameters in the first step the image is reconstructed and then the parameters are estimated with the use of image processing methods. The obtained results showed significant reduction of computations time in comparison to the iterative LBP or Levenberg-Marquard algorithms.