Date of Award
Doctor of Philosophy (PhD)
Armando B. Corripio
An algorithm is presented for supervisory optimization of industrial processes that combines the minimization of operating costs with process operating constraints. The supervisory algorithm manipulates the set points of a lower-level control system and 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 dissertation develops the algorithm and then presents results from its application to a simulated distillation train and reactor feed network. In both applications, the algorithm reduces costs while satisfying changing constraints. In the distillation example, two modes of the supervisory control are identified as "Independent" where the cost is minimized using a dynamic programming approach and "Total" where the whole train is treated as one unit to reduce costs. In some cases where manipulated variables are saturating for a column in the train, the total mode is able to follow constraint changes whereas the independent mode cannot. For most other test studies, the two modes produce about the same savings and either can be used. Even when steady state is not reached between optimization moves, there appears to be no appreciable difference between the two modes. For the reactor application, the concept of an extended controller is investigated where saturating manipulated variables are removed from the vector set and the optimization algorithm called again. The results indicate that this extended controller can be cost beneficial and should be used as part of the regular control.
Daniel, William E. Jr, "An Algorithm for Supervisory Multivariable Constrained Optimization of Industrial Processes." (1993). LSU Historical Dissertations and Theses. 5624.