Solution of many problems in plant operations requires determination of optimal control profiles subject to state constraints for systems modeled by ordinary differential equations (ODEs) or differential-algebraic equations (DAEs). For example, optimal temperature and/or feed rate profiles are important for the operation of many batch reactions. Similar observations apply to reflux policies for batch distillation, and feedstock changeover in oil refineries. Currently there are two different classes of methods for determining optimal control profiles for DAEs. Control parameterization techniques rely on the discretization of the control variables to reduce the optimal control problem to an NLP. These methods require repeated integration of the DAEs and some variational equations, which effectively discretizes the state variables within the numerical integrator. Path constraints are typically handled by the master NLP solver, and can force the NLP solver to call for a large nu..