Abstract - Barton - 9703623 This research will investigate a rigorous and practical approach to the optimization of nonlinear dynamic systems coupled with integer decisions variables, termed mixed integer dynamic optimization problems. An iteration strategy will be developed and tested that has the potential to avoid total enumeration of the discrete decision space, and on termination yields information on the distance to the global solution. The work is motivated by the need to achieve better solutions to two important problems in the chemical industries: the development of an optimal batch process for a new product, and the synthesis of cost effective operating procedures (for example, start-up and shut-down) subject to safety and operational constraints. The approach will involve decomposition to a primal and a master problem. The primal problem is one in which the binary decision variables are fixed to yield a classical variational problem. This primal is highly nonconvex and will be difficult to solve to guaranteed global optimality. To take care of that problem, the PI plans to use a master problem based on the solution to a screening model: a simplified model of the problem at hand, derived via an unrelated modeling effort, that provides a rigorous lower bound on the solution to the overall problem. A decomposition iteration based on these two subproblems will be used to explore the discrete decision space, and on termination, yield information on the distance to the global solution.
