Dynamic optimization of chemical and biochemical processes using restricted second order information

Abstract The extension of a recently developed method for the dynamic optimization of chemical and biochemical processes is presented. This method is based on the control vector parameterization approach and makes use of the calculation of first- and second-order sensitivities to obtain exact gradient and projected Hessian information. In order to achieve high discretization levels of the control variables with a moderate computational cost, a mesh refining technique is also presented here. The robustness and efficiency of this strategy is illustrated with the solution of several challenging case studies.

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