Real-time process optimization leverages accurate process models across a processing system of sufficient complexity to require mathematical methods to optimally select available degrees of freedom and maximize a performance objective. Thus by default a process model represents a non-trivial problem that justifies an optimization investment. Traditionally solutions have required experts to solve problems including maintaining model quality to be assured that the right answer will be trusted and result; and to overcome measurement accuracy challenges. These two problems require intellligence in using available measurements and solving tuning and reconciliation problems appropriately (which frequently have conflicting decisions). Additional challenges with real-time optimization in practice relate to data integration and solution time. These are requirments so that decisions can be made at a sufficient frequency with current data to get the right answer at the right time.
A proposed solution that solves a restricted problem set of mixed-integer, utility plant dispatch, i.e. energy optimization seems to overcome traditional challenges amd make real-time optimization a simple, practical, broadly useful technology. How this problem can be solved with light development effort and very light support effort will be presented as a paradigm toward making real-time optimization simple, fast and nearly automatic. Such a system is optimizing a facility energy center including steam production with multiple fuels, turbines and chillers of various designs and energy sources to provide a real-time solution that has been maintained by a day-foreman successfully for more than three years. The a facility manager has been quoted to say "it gives us the right answers".