Dynamic optimization of single- and multi-stage systems using a hybrid stochastic-deterministic method

Abstract

A hybrid stochastic−deterministic method, based on the control vector parameterization (CVP) approach, is presented as a reliable and efficient alternative for the solution of dynamic optimization (or open loop optimal control) problems.The problems under consideration are related to free final time single-stage systems and more general multi-stage procecesses that are described by different sets of differential and algebraic equations (DAEs), one for each stage. The operating conditions and the duration of each stage must be computed in order to achieve an overall optimal result for the process subject to constraints in the state and control variables. The solution of three challenging dynamic optimization problems is presented, including a large-scale case study, showing the capabilities of this new strategy