Analyzing the interaction between process design and process control

The design of a chemical process determines the properties of variable interaction, disturbance rejection, and set-point tracking which comprise its inherent controllability. A systematic procedure is presented for considering simultaneously both economic and open-loop controllability objectives within the mathematical programming framework of process synthesis. This approach translates a superstructure of design alternatives into a multiobjective mixed-integer nonlinear programming (MINLP) problem. With the flowsheet unknown a priori, the formulation requires expressions for the economic and control objectives in terms of the unknown design variables of the optimization. These control objectives come from open-loop controllability measures of the process and disturbance transfer functions. Use of the {dollar}epsilon{dollar}-constraint method within the framework of the Generalized Benders Decomposition algorithm generates the noninferior solution set, which consists of those designs in which a control objective can be improved only at the expense of the economic objective. Application of a cutting-plane algorithm determines a best-compromise solution among the competing objectives using as trade-off weights the partial derivative information from the noninferior set.; This work illustrates the proposed procedure first in binary distillation synthesis involving multiple objectives using the reflux and vapor boilup to control the distillate and bottoms compositions. It addresses the control configuration selection problem in distillation synthesis by comparing the noninferior sets among several different control schemes. It deals with the possibility of heat-integration in distillation columns by showing with the noninferior set the economic and control trade-offs between a single column and a double-effect configuration. Finally, it examines as a nonlinear programming problem the design of a reactor-separator-recycle system. In all of these applications, the derivation of steady-state gain expressions relating the controlled variables to both manipulated inputs and disturbances allows the incorporation of controllability measures within the optimization problem. The generality of the procedure means it can accommodate any number or type of objectives provided these can be expressed in terms of the design variables. The work establishes a systematic framework to consider the effects of design changes on both economic and control objectives.