​​Electrical resistance tomography (ERT) and electrical impedance tomography (EIT) are imaging techniques reconstructing the internal conductivity distribution image of an object based on voltage measurements at the periphery of the object with a given applied current. ERT uses a direct current (DC), while EIT uses an alternating current (AC). However, for low frequencies both ERT and EIT have the same governing equation, which is often referred to as a non-linear and ill-posed inverse problem. Both methods have diverse applications in biology, biomedicine, and industry. ​This master’s degree project aims to create a low-cost imaging system for the ERT, which is the main focus, as well as for the EIT. The project includes three main components: 1) Simulations and reconstructions using EIDORS (Electrical Impedance Tomography and Diffuse Optical Tomography Reconstruction Software), 2) Developing an experimental workbench (a measurement system), and 3) developing a machine learning model for the ERT. ​EIDORS was used to simulate and reconstruct ERT and EIT images. It was also used to generate training data for the machine learning model to be developed. ​The measurement system includes a circular water tank with electrodes, power supplies, and measurement units. Tanks with 8 and 16 electrodes were designed using 3D printers. Initially, aluminium electrodes provided inconsistent measurements due to magnetization and electrolysis, later replaced by graphite electrodes, offering better but not yet accurate enough results. ​After implementing reconstruction algorithms in EIDORS, a machine learning model was developed for ERT. It involved: 1) generating a training set, containing over 5000 simulated data points, 2) preprocessing the generated data set which included PCA dimensionality reduction, 3) and lastly a linear regression model developed. The model struggled with small object detection and occasional inconclusive results, likely due to limited training dataset diversity. Additionally, images of two cases were reconstructed using EIT and comparing it to ERT it can be concluded that EIT performs better than ERT. ​

Radio magnetotellurics (RMT), crosshole ground penetrating radar (GPR), and crosshole electrical resistance tomography (ERT) were applied in a range of hydrogeological applications where geophysical data could improve hydrogeological characterization. A profile of RMT data collected over highly resistive granite was used to map subhorizontal fracture zones below 300m depth, as well as a steeply dipping fracture zone, which was also observed on a coinciding seismic reflection profile. One-dimensional inverse modelling and 3D forward modelling with displacement currents included were necessary to test the reliability of features found in the 2D models, where the forward models did not include displacement currents and only lower frequencies were considered. An inversion code for RMT data was developed and applied to RMT data with azimuthal electrical anisotropy signature collected over a limestone formation. The results indicated that RMT is a faster and more reliable technique for studying electrical anisotropy than are azimuthal resistivity surveys. A new sequential inversion method to estimate hydraulic conductivity fields using crosshole GPR and tracer test data was applied to 2D synthetic examples. Given careful surveying, the results indicated that regularization of hydrogeological inverse problems using geophysical tomograms might improve models of hydraulic conductivity. A method to regularize geophysical inverse problems using geostatistical models was developed and applied to crosshole ERT and GPR data collected in unsaturated sandstone. The resulting models were geologically more reasonable than models where the regularization was based on traditional smoothness constraints. Electromagnetic geophysical techniques provide an inexpensive data source in estimating qualitative hydrogeological models, but hydrogeological data must be incorporated to make quantitative estimation of hydrogeological systems feasible.

A dissertation submitted to the Faculty of Engineering, Cape Peninsula University of Technology, Cape Town, in partial fulfilment of the requirements for the MTech Degree in Mechanical Engineering 2012 / Flow features provide considerable guidance for the rational selection of techniques to predict hydraulic behaviour and for suitable operating conditions for pipelines. Traditionally, water was used to transport coarse particles, and it was necessary to operate at velocities at which the flow was turbulent in order to avoid blockage. Consequently the friction losses were too high for economic operation. In addition, wear on pipes, fittings and pumps presented serious problems. Nowadays, it is well established that it is possible to operate at very high solids concentration in a heavy vehicle (carrier fluid). Similar solids throughputs may be achieved at very much lower velocities by operating in the laminar flow regime. This results not only in lower power requirement, but it also reduces wear and water consumption. In spite of these potential benefits, only a few studies dealing with the transport of coarse particles in heavy media have been reported. Since the distinction between different flow patterns is of paramount importance for modelling purposes, as equations are flow pattern dependent, and given the importance of avoiding excessive wear of pipes at low and high velocities, the present work was carried out in the context of dense or non-Newtonian carrier fluid. This project comprised analysis of existing data acquired at the Flow Process and Rheology Centre of the Cape Peninsula University of Technology. Kaolin in the range of 6% to 15% volumetric concentration was used as a carrier fluid and coarse material in the range of 10% to 30% volumetric concentration was simulated by silica sand ranging in size from 1 mm to 3 mm. For the purpose of this study flow patterns derived from resistance curves for various mixtures, particle concentrations, particle grading and flow conditions were compared with “concentration profiles” and images obtained from electrical resistance tomography (ERT). It appeared from this work that the sand concentration does not change the flow pattern but increases or reduces the pressure gradients depending on the case. The concentration of kaolin carrier can change the flow patterns from layered to homogeneous flow, inducing an increase in total pressure gradients as it increases. Flow patterns obtained from ERT compared reasonably well with those derived from pressure gradients profiles. The transition velocities from layered to heterogeneous flow obtained from both methods were similar, especially for low and moderate carrier concentrations. As the kaolin carrier concentration or as the sand concentration increased it became more difficult to distinguish the transition velocity between heterogeneous and layered flow. More work is still needed to improve the ERT instrument and its image reconstruction software.