Dr. Vergara-Rojas, Samuel

Electrical impedance tomography (EIT) is a technology that estimates the electrical properties of a body or a cross section. Its main advantages are its non-invasiveness, low cost and operation free of radiation. The estimation of the conductivity field leads to low resolution images compared with other technologies, and high computational cost. However, in many applications the target information lies in a low intrinsic dimensionality of the conductivity field. The estimation of this low-dimensional information is addressed in this work. It proposes optimization-based and data-driven approaches for estimating this low-dimensional information. The accuracy of the results obtained with these approaches depends on modelling and experimental conditions. Optimization approaches are sensitive to model discretization, type of cost function and searching algorithms. Data-driven methods are sensitive to the assumed model structure and the data set used for parameter estimation. The system configuration and experimental conditions, such as number of electrodes and signal-to-noise ratio (SNR), also have an impact on the results. In order to illustrate the effects of all these factors, the position estimation of a circular anomaly is addressed. Optimization methods based on weighted error cost functions and derivate-free optimization algorithms provided the best results. Data-driven approaches based on linear models provided, in this case, good estimates, but the use of nonlinear models enhanced the estimation accuracy. The results obtained by optimization-based algorithms were less sensitive to experimental conditions, such as number of electrodes and SNR, than data-driven approaches. Position estimation mean squared errors for simulation and experimental conditions were more than twice for the optimization-based approaches compared with the data-driven ones. The experimental position estimation mean squared error of the data-driven models using a 16-electrode setup was less than 0.05% of the tomograph radius value. These results demonstrate that the proposed approaches can estimate an object's position accurately based on EIT measurements if enough process information is available for training or modelling. Since they do not require complex calculations it is possible to use them in real-time applications without requiring high-performance computers.

A theory of sedimentation-consolidation evolved in the last decades of the 20th century and is accepted today by researchers worldwide. This theory provides a reliable method of thickener design, simulation and control. However, a process model, simple or sophisticated, empirical or phenomenological, is useful only if it is possible to determine, in an objective way, its experimental parameters. Although it is important for a mineral processing plant to perform periodical laboratory test to determine thickening parameters, and adjust the operation in this way, laboratory tests not always represent the behavior of the material in a thickener. Research workers at the University of Concepción developed new instrumentation, algorithms and software to determine the material properties of the thickener feed, such as settling velocity of the suspension and the compressibility of the sediment produced. Work was made in a major Chilean copper mineral processing plant to test the new instrumentation. In this paper, the auditing of one molybdenum thickener and a tailings thickener are presented using the two new online instrument.