Cryogenic air separation plants that produce argon in addition to oxygen and nitrogen use two heat-integrated distillation columns and a side rectifier. The dynamics of the side rectifier are very slow because of three factors:  the large number of separation stages, the small amount of argon in the air feed, and the high product purity. Currently, when the plant shuts down, the liquid inventory in the argon column drains into the upper (low-pressure) column. A normal start-up takes about 10 h to achieve argon product purity. The start-up time can be reduced significantly by using storage vessels to collect the liquid that drains from the argon column upon shutdown and reintroducing the liquid during the subsequent start-up. Compared to a 10 h start-up time with no collection points, the time required to achieve argon product purity is reduced to 3.35 h by using 2 collection points and is reduced to 2.23 h by using 6 collection points.

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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.

High purity distillation columns are critical unit operations in cryogenic air separation plants that supply purified gases to a number of industries. We have developed a nonlinear model predictive control (NMPC) strategy based on the assumption of full-state feedback for a prototypical cryogenic distillation column to allow effective operation over a wide range of plant production rates. The controller design was based on a reduced-order compartmental model derived from detailed mass and energy balances by exploiting time-scale separations. Temporal discretization of the compartmental model produced a very large set of nonlinear differential and algebraic equations with advantageous sparsity properties, enabling online solution of the NMPC problem. The synergistic combination of several real-time implementation techniques were found to be essential for further reducing computation time and allowing reliable solution within the 2-min controller sampling interval. Closed-loop simulation studies demonstrated the performance advantages of NMPC compared to linear model predictive control technology currently used in the air separation industry.

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