Quantitative methods for optimal regulatory layer selection

Controlled variables selection based on economic objectives using self optimizing concepts are developed. In this paper, we extend the self optimizing control ideas to find optimal controlled variables in the regulatory layer. The regulatory layer is designed to facilitate stable operation, to regulate and to keep the operation in the linear operating range and its performance is here quantified using the state drift criterion. Quantitative methods are proposed and evaluated on a distillation column case study with 41 stages that minimize state drift in composition states to obtain optimal regulatory layer with 1, 2 and more closed loops.