Date of Graduation


Document Type


Degree Type



Statler College of Engineering and Mineral Resources


Mining Engineering

Committee Chair

Aaron Noble

Committee Co-Chair

Rashpal Ahluwalia

Committee Member

John Herbst

Committee Member

Felicia Peng

Committee Member

Mark Sindelar


The estimation and analysis of uncertainty propagation in mineral processing separation circuits is an essential and significant, but challenging, aspect of a comprehensive optimal circuit design procedure. Owing to the sophisticated modeling requirements, many of the current circuit optimization tools rely on deterministic models, despite the ubiquity of uncertainty in the techno-economic input parameters (e.g. mineral price, plant feed grade, and process kinetic coefficients). While individual sources of uncertainty are substantial, the circuit designer must also estimate the compounded uncertainty imputed by the actual circuit design and identify which units are most influential in this uncertainty propagation. Additionally, despite the uncertainty in the input factors, the designer must identify precise specifications for the number and size of individual separation units with the objective of optimizing technical and economic performance measures. These factors must be considered very early in the design process due to the rigidity of the final flowsheet and early constraints of product specifications imposed by sales contracts. This dissertation seeks to resolve these issues by providing a suite of novel techniques that augment the state-of-the-art process models currently used by circuit designers. First, the linear circuit analysis approach and the law of propagation of error are combined to effectively analyze and evaluate circuit uncertainty propagation in the early design stages. Subsequently, the capability of this novel methodology is demonstrated to accurately recognize the most influential factors in the uncertainty contribution. Parallel to this part of the study, Taguchi's method is employed to evaluate the level of compounded circuit uncertainty while using fewer function evaluations compared to the common stochastic techniques, such as Monte Carlo simulation. Then, a systematic separation experimental study is performed using an electrostatic separator to validate the fundamental conclusions derived from the proposed methodologies. Finally, a comprehensive circuit optimization technique, based on the sample average approximation approach, is applied to determine the most profitable separation circuit configuration under uncertainty. Given the large flow volumes, high capital costs, and relative rigidity of the final flowsheet, findings of the current study will guarantee that a suitable separation circuit is selected relatively early in the design process.