BibTeX
@ARTICLE{
         Amrita2013Ope,
       title = "Optimizing process economics online using model predictive control",
       author = "Rishi Amrita and James B. Rawlingsa and Lorenz T. Biegler",
       publisher = "Elsevier",
       year = "2013",
       journal = "Computers and Chemical Engineering",
       volume = "58",
       pages = "334– 343",
       abstract = "Optimizing process economics in model predictive control traditionally has been
         done using a twostep approach in which the economic objectives are first converted to steady-state
         operating points, and then the dynamic regulation is designed to track these setpoints. Recent
         research has shown that process economics can be optimized directly in the dynamic control problem,
         which can take advantage of potential higher profit transients to give superior economic
         performance. However, in practice, solution of such nonlinear MPC dynamic control problems can be
         challenging due to the nonlinearity of the model and/or nonconvexity of the economic cost function.
         In this work we propose the use of direct methods to formulate the nonlinear control problem as a
         large-scale NLP, and then solve it using an interior point nonlinear solver in conjunction with
         automatic differentiation. Two case studies demonstrate the computational performance of this
         approach along with the economic performance of economic MPC formulation.",
       ad_area = "Process Engineering",
       ad_tools = "ADOL-C",
       ad_theotech = "Sparsity"
}