Convex formulations for optimal selection of controlled variables and measurements using Mixed Integer Quadratic Programming

The appropriate selection of controlled variables is important for operating a process optimally in the presence of disturbances. Self-optimizing control provides a mathematical framework for selecting the controlled variables as combinations of measurements, c = Hy, with the aim to minimize the steady state loss from optimal operation. In this paper, we present (i) a convex formulation to find the optimal combination matrix H for a given measurement set and (ii) a Mixed-Integer Quadratic Programming (MIQP) methodology to select optimal measurement subsets that result in minimal loss. The methods presented in this paper are exact for quadratic problems with linear measurement relations. The MIQP methods can handle additional structural constraints compared to the branch and bound (BAB) methods reported in literature. The MIQP methods are evaluated on a toy test problem, an evaporator example, a binary distillation column example with 41 stages and a Kaibel column with 71 stages.

► Self-optimizing control concepts are used to find controlled variables that minimize steady state loss in the presence of disturbances.
► Controlled variables are individual/combination of measurements, c = Hy.
► We present (i) a convex formulation to find H and (ii) a Mixed-Integer Quadratic Programming to select optimal measurement subsets.
► The MIQP methods can handle additional structural constraints than the branch and bound (BAB) methods.
► The MIQP methods are evaluated on a toy test problem, an evaporator example, and on distillation columns.