Vacuum arc remelting is a secondary melting process used to produce a variety of segregation sensitive and reactive metal alloys. The present day VAR practice for superalloys involves, typically, melting electrodes of 17'' into ingots of 20'' in diameter. Even larger diameter forging stock is desirable. However, beyond 20'' ingots of superalloys are increasingly prone to segregation defects if solidification is not adequately controlled. In the past years a new generation of model-based controllers was developed to prevent segregation in VAR by controlling melt rate, or the total amount of power flowing into the liquid pool. These controllers were seen as significant improvements in the industry of remelting processes, but these controllers were still focusing on the melting sub-process and ignoring ingot solidification. Accurate control of the liquid pool profile is expected to result in segregation-free ingots, but unfortunately a controller capable of stabilizing the solidification front in VAR is currently not available. The goal of the proposed research is to develop a cyber-enabled controller for VAR pool depth control that will enhance the capabilities of current technologies. More specifically, the objectives of this research are threefold. Firstly, a control-friendly model is proposed based on a high-fidelity ingot solidification model and is coupled to a thermal model of electrode melting. Secondly, sequential Monte Carlo estimators are proposed to replace the traditional Kalman filter, used in the previous VAR controllers. And finally, a model predictive controller (MPC) is designed based on the proposed reduced-order model. The time-critical characteristics of these methods are studied, and the feasibility of their real-time implementation is reported. / text