Global optimization of dynamic systems
Many chemical engineering systems are described by differential equations. Their optimization is often complicated by the presence of nonconvexities. A deterministic spatial branch and bound global optimization algorithm is presented for problems with a set of first-order differential equations in the constraints. The global minimum is approached from above and below by generating converging sequences of upper and lower bounds. Local solutions, obtained using the sequential approach for the solution of the dynamic optimization problem, provide upper bounds. Lower bounds are produced from the solution of a convex relaxation of the original problem. Algebraic functions are relaxed using well-known convex underestimation techniques. The convex relaxation of the dynamic information is achieved using a new convex relaxation procedure. Parameter independent as well as parameter dependent bounds on the dynamic system are utilized. The global optimization algorithm is illustrated by applying it to case studies relevant to chemical engineering, where affine functions of the optimization variables are used as a relaxation of the dynamic system. (C) 2003 Elsevier Ltd. All rights reserved.
global optimization differential equations convex relaxation nonconvex dynamic optimization