Experiments in process synthesis via mixed-integer programming

Abstract Mixed-integer programming is the natural formulation of many process design synthesis problems. Up to now mixed-integer programming algorithms have been applied successfully for solving simplified synthesis problems involving Boolean variables, with the assumption of separability and linearity of constraints and/or objective function. This work aims at going still further in the solution of mixed problems with non-Boolean variables, nonlinear and nonseparable constraints and criteria. A general-purpose algorithm is presented and illustrated by two examples of processes. The synthesis of a heat exchanger network shows the capability of the algorithm to solve this problem via a simultaneous optimization of the structure and temperatures, without assuming a minimum temperature difference. The number of stages and the operating conditions of a multistage reactor for esterification of fatty acid are then optimized, under a purity constraint of the ester, computed by simulation. This second example shows that it is possible to handle nonlinear and implicit constraints.

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