Nonlinear state estimation and model predictive control of nitrogen purification columns

Nitrogen purification columns are critical unit operations of air separation plants that produce purified gases for the chemical, steel, food processing, medical, and semiconductor industries. The need to operate these very high purity columns over a wide range of production rates in response to time-varying electrical costs motivates the development of nonlinear control strategies. We utilize a nonlinear wave model previously developed by our group to design a nonlinear model predictive controller for a simulated nitrogen purification column. A first principles model constructed in Aspen Dynamics (Aspen Technology) is used as a surrogate plant in the simulation studies. Estimates of the unmeasured wave position and key wave model parameters are generated with an extended Kalman filter using a combination of composition and temperature measurements strategically placed along the column. Several estimator formulations are investigated to evaluate the effects of different measurement combinations. The estimator and predictive controller are combined through a state disturbance model that provides feedback and eliminates offset due to modeling errors. The controller manipulates the vapor nitrogen production rate to achieve the target nitrogen purity. The proposed control strategy is compared to a classical control system consisting of a ratio controller and a proportional-integral-derivative controller to analyze the potential benefits of nonlinear model-based control for this process.