This paper presents a model predictive control (MPC) algorithm utilizing a state-space approach that allows for inferential control of unmeasured states using a prioritized control objective formulation. Knowledge of the unmeasured states is gained through the use of an external state estimation routine, while mixed-integer methods are used to implement the prioritization of the objectives. The capabilities of the algorithm are demonstrated by the application of the controller to a fermentation reactor model in a simulation environment.