Browsing by Author "Koermer, Scott Carl"
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- The Application of Mineral Processing Techniques to the Scrap Recycling IndustryKoermer, Scott Carl (Virginia Tech, 2015-11-09)The scrap metal recycling industry is a growing industry that plays an important role in the sustainability of a large global metal supply. Unfortunately, recycling lacks many standards, and test procedures in place for mineral processing. These standards and practices, if used in recycling, could aid recyclers in determining and achieving optimal separations for their plant.. New regulations for scrap imports into China make it difficult to obtain the metal recoveries that have been achieved in the past. In order to help scrap yards adhere to the new regulations the Eriez RCS eddy current separator system was tested in full scale. The principles this system uses, called circuit analysis, have been used by the mining industry for years, and can be used with any separation system. The Eriez RCS system surpassed the requirements of the Chinese regulations, while simultaneously increasing the recovery of metals. In order to further analyze eddy current separator circuits, tree analysis was attempted for single eddy current separators, as well as more complex circuits mimicked using locked cycle tests. The circuits used in the locked cycle test were a rougher-cleaner, a rougher-scavenger, and a rougher-cleaner-scavenger. It was found that it is possible to use tree analysis to compare different eddy current separator circuits using the same settings, however standards for this practice need to be established for it to be useful. Using the data analysis methods developed for this particular tree analysis, the rougher-cleaner-scavenger test had the best performance overall. This is the same result as the full scale testing done on the Eriez RCS system, but more testing should be conducted to confirm the data analysis techniques of calculating theoretical efficiency, recovery efficiency, and rejection efficiency.
- Bayesian Methods for Mineral Processing OperationsKoermer, Scott Carl (Virginia Tech, 2022-06-07)Increases in demand have driven the development of complex processing technology for separating mineral resources from exceedingly low grade multi- component resources. Low mineral concentrations and variable feedstocks can make separating signal from noise difficult, while high process complexity and the multi-component nature of a feedstock can make testwork, optimization, and process simulation difficult or infeasible. A prime example of such a scenario is the recovery and separation of rare earth elements (REEs) and other critical minerals from acid mine drainage (AMD) using a solvent extraction (SX) process. In this process the REE concentration found in an AMD source can vary site to site, and season to season. SX processes take a non-trivial amount of time to reach steady state. The separation of numerous individual elements from gangue metals is a high-dimensional problem, and SX simulators can have a prohibitive computation time. Bayesian statistical methods intrinsically quantify uncertainty of model parameters and predictions given a set of data and a prior distribution and model parameter prior distributions. The uncertainty quantification possible with Bayesian methods lend well to statistical simulation, model selection, and sensitivity analysis. Moreover, Bayesian models utilizing Gaussian Process priors can be used for active learning tasks which allow for prediction, optimization, and simulator calibration while reducing data requirements. However, literature on Bayesian methods applied to separations engineering is sparse. The goal of this dissertation is to investigate, illustrate, and test the use of a handful of Bayesian methods applied to process engineering problems. First further details for the background and motivation are provided in the introduction. The literature review provides further information regarding critical minerals, solvent extraction, Bayeisan inference, data reconciliation for separations, and Gaussian process modeling. The body of work contains four chapters containing a mixture of novel applications for Bayesian methods and a novel statistical method derived for the use with the motivating problem. Chapter topics include Bayesian data reconciliation for processes, Bayesian inference for a model intended to aid engineers in deciding if a process has reached steady state, Bayesian optimization of a process with unknown dynamics, and a novel active learning criteria for reducing the computation time required for the Bayesian calibration of simulations to real data. In closing, the utility of a handfull of Bayesian methods are displayed. However, the work presented is not intended to be complete and suggestions for further improvements to the application of Bayesian methods to separations are provided.