An algorithm is presented for supervisory optimization of industrial processes that integrates the minimization of operating costs with process operating constraints. It is assumed that the supervisory algorithm manipulates the set points of a lower-level control system and that the set points are updated at long enough intervals of time so that the process reaches steady state between set point updates. This steady state assumption greatly simplifies the algorithm computations and, more importantly, significantly reduces the effort required for process identification. This article develops the algorithm and then presents results from its application to a simulated distillation train. The simulation parallels an application of the algorithm to an actual industrial train on a commercial distributed control system.