Multiobjective optimization of multipurpose batch plants using superequipment class concept

We present a novel approach for solving different design problems related to single products in multipurpose batch plants: the selection of one production line out of several available, additional investment into an existing line or plant, and grass-root design of a new plant. Multiple objectives are considered in these design problems. Pareto-optimal solutions are generated by means of a Tabu Search algorithm. In the novel approach the concept of superequipment has been defined as an abstract model, which is capable of performing any physico-chemical batch operation. Each superequipment is transformed into a real equipment unit, for example a reactor, during or after the optimization in order to evaluate performance parameters of a design. This novel concept uses an implicit definition of a superstructure and essentially optimizes on the transfers between different equipment units. On the basis of two case studies we demonstrate that the application of the superequipment concept offers a number of advantages for the investigated design problems. The comparison with optimization results obtained with a conventional Tabu Search algorithm revealed that the superequipment method identifies the Pareto-optimal solutions in significantly reduced computation time.