Dynamic Maximization of Oxygen Yield in an Elevated-Pressure Air Separation Unit using Multiple Model Predictive Control

In a typical air separation unit (ASU) utilizing either a simple gaseous oxygen (GOX) cycle or a pumped liquid oxygen (PLOX) cycle, the flowrate of the liquid nitrogen stream connecting the high- and low-pressure columns has a major impact on the total oxygen yield. It is shown that this yield reaches a maximum at a certain optimal flowrate of LN2 stream, creating a challenging feedback controller design problem. To dynamically maximize the oxygen yield while the ASU undergoes a load-change and/or a process disturbance, a multiple model predictive control (MMPC) algorithm is proposed. It is shown that at any operating point of the ASU, the MMPC algorithm, through model-weight calculation based on plant measurements, naturally and continuously selects the dominant model(s) corresponding to the current plant state, while making control-move decisions that approach the maximum oxygen yield point. This dynamically facilitates less energy consumption in form of compressed feed-air compared to a simple ratio control during load-swings. In addition, since a linear optimization problem is solved at each time step, the approach involves much less computational cost than a model predictive controller (MPC) based on a first-principles model.