Economic optimization of spray dryer operation using Nonlinear Model Predictive Control with state estimation

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Abstract

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 auto covariance 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.

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APA

Petersen, L. N., Jørgensen, J. B., & Rawlings, J. B. (2015). Economic optimization of spray dryer operation using Nonlinear Model Predictive Control with state estimation. In IFAC-PapersOnLine (Vol. 28, pp. 507–513). https://doi.org/10.1016/j.ifacol.2015.09.018

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