A deterministic global optimization algorithm for problems with nonlinear dynamics

A deterministic spatial branch and bound global optimization algorithm is presented for systems with an initial value problem for a set of first-order, typically nonlinear, differential equations in the constraints. Upper bounds on the global minimum are obtained using the sequential approach for the local solution of the dynamic optimization problem. The solution of a convex relaxation of the problem provides lower bounds. Well-known convex underestimation techniques are used for the relaxation of the algebraic functions. The concept of differential inequalities is utilized for the development of parameter independent as well as parameter dependent bounds on the dynamic system. Three convex relaxation procedures are proposed for the parameter dependent solution of the initial value problem. The global optimization algorithm is illustrated by applying it to several case studies relevant to chemical engineering.