Economic Optimization of Spray Dryer Operation using Nonlinear Model Predictive Control with State Estimation

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedings – Annual report year: 2015Researchpeer-review

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In this paper, we develop an economically optimizing Nonlinear Model Predictive Controller (E-NMPC) for a complete spray drying plant with multiple stages. In the E-NMPC the initial state is estimated by an extended Kalman Filter (EKF) with noise covariances estimated by an autocovariance least squares method (ALS). We present a model for the spray drying plant and use this model for simulation as well as for prediction in the E-NMPC. The open-loop optimal control problem in the E-NMPC is solved using the single-shooting method combined with a quasi-Newton Sequential Quadratic programming (SQP) algorithm and the adjoint method for computation of gradients. We evaluate the economic performance when unmeasured disturbances are present. By simulation, we demonstrate that the E-NMPC improves the profit of spray drying by 17% compared to conventional PI control.
Original languageEnglish
Title of host publicationPreprints of the 9th International Symposium on Advanced Control of Chemical Processes
PublisherInternational Federation of Automatic Control
Publication date2015
Publication statusPublished - 2015
Event9th International Symposium on Advanced Control of Chemical Processes - Whistler, Canada
Duration: 7 Jun 201510 Jun 2015


Conference9th International Symposium on Advanced Control of Chemical Processes
Internet address

    Research areas

  • Nonlinear Model Predictive Control, Optimization, Grey-box model, Spray Drying

ID: 116512598