The chemical process synthesis problem might also be formulated as an optimization over all feasible process structures. In principle, if we employed all different possible approaches to implement some desired chemistry—including all possible different reactor configurations, all possible different separation and purification schemes, techniques, and equipment, and all interconnections among these potential units in one gigantic tentative process flow sheet—and if we then subjected such a superstructure to economic optimization constrained by environmental, safety, and other criteria (during which process inferior equipment and interconnections were eliminated), the best manufacturing process in terms of both structure and the design of each surviving piece of equipment would emerge. This, of course, is a massive and extremely difficult optimization problem involving a mixture of continuous, integer, and logical variables and relationships. However, new mathematical techniques such as generalized disjunctive programming and global optimization, combined with the tremendous increases in available computing power made possible through large clusters of fast independent processors, give hope that this superstructure optimization approach to chemical process synthesis may be practical in the near future.

The chemical industry can largely be viewed as being composed of two major segments. One is the “value preservation” industry that is largely based on the large-scale production of commodity chemicals. The other is the “value growth” industry that is based on the small-scale production of specialty chemicals, biotechnology products, and pharmaceuticals. For the chemical industry to remain strong, it is essential that these two segments be competitive and economically strong. The “value preservation” industry must be able to reduce costs, operate efficiently, and continuously improve product quality, thereby making process simulation and process optimization its key technologies. The “value growth” industry must be agile and quick to market new products, making supply chain management one of its key technologies. In both cases, major challenges over the next two decades will be to gain a better understanding of the structure and information flows underlying the chemical supply chain of Figure 6-1, and to develop novel mathematical models and methods for its simulation and optimization.

In the past, most of the modeling and optimization activity has taken place in isolated parts of the chemical supply chain. In chemical engineering it has been mostly at the level of process units and plants, but more recently it has been moving in two opposing directions—the molecular and the enterprise levels. In computational chemistry, the modeling research has been directed at the molecular level and is increasingly moving toward the atomic and quantum scale, as discussed in the previous sections. Developments in planning and scheduling are being increasingly directed to address the optimization of the supply chain at the