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
Pages507-513
Publication statusPublished - 2015
Event9th International Symposium on Advanced Control of Chemical Processes - Whistler, Canada
Duration: 7 Jun 201510 Jun 2015
http://adchem2015.org/

Conference

Conference9th International Symposium on Advanced Control of Chemical Processes
CountryCanada
CityWhistler
Period07/06/201510/06/2015
Internet address

    Research areas

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

ID: 116512598