Heuristic-based mathematical programming framework for control structure selection

This paper presents an optimization-based method for selecting manipulated variables for regulatory control schemes. The objective of this mathematical programming technique is the minimization of the overall interaction and sensitivity of the closed-loop system to disturbances. In addition, a general methodology for incorporating qualitative knowledge as linear constraints to the problem is demonstrated. The main advantage of the method is that the plantwide nature of the problem is preserved, because decisions related to different levels of the base regulatory control scheme are made simultaneously. The usefulness of the method is demonstrated using a double-effect evaporator case study, the hydrodealkylation of toluene case study, and the Tennessee Eastman challenge problem.