DYNAMIC MAXIMIZATION OF OXYGEN YIELD IN AN ELEVATED-PRESSURE AIR SEPARATION UNIT USING LINEAR MULTIPLE MODEL PREDICTIVE CONTROL (MMPC) FRAMEWORK

摘要

In a typical air separation unit (ASU) utilizing either a simple gaseous oxygen (GOX) cycle or apumped liquid oxygen (PLOX) cycle, the flowrate of liquid nitrogen (LN2) stream connectinghigh-pressure and low-pressure ASU columns plays an important role in the total oxygen yield.It has been observed that this yield reaches a maximum at a certain optimal flowrate of LN2stream. At nominal full-load operation, the flowrate of LN2 stream is maintained near thisoptimum value, whereas at part-load conditions this flowrate is typically modified in proportionwith the load-change (oxygen demand) through a ratio/feed-forward controller. Due tononlinearity in the entire ASU process, the ratio-modified LN2 flowrate does not guarantee anoptimal oxygen yield at part-load conditions. This is further exacerbated when processdisturbances in form of “cold-box” heat-leaks enter the system. To address this problem ofdynamically maximizing the oxygen yield while the ASU undergoes a load-change and/or aprocess disturbance, a multiple model predictive control (MMPC) algorithm is proposed. Thisalgorithm uses linear step-response “blackbox” models surrounding the operating pointscorresponding to maximum oxygen yield points at different loads. It has been shown that at anyoperating point of the ASU, the MMPC algorithm, through model-weight calculation based onplant measurements, naturally and continuously selects the dominant model(s) corresponding tothe current plant state, while making control-move decisions that approach the maximum oxygenyield point. This dynamically facilitates less energy consumption in form of compressed feed-aircompared to a simple ratio control during load-swings. In addition, since a linear optimizationproblem is solved at each time step, the approach involves much less computational costcompared to a first-principle based nonlinear MPC.