271893 Application of Linear Multiple Model Predictive Control (MMPC) Framework towards Dynamic Maximization of Oxygen Yield in an Elevated-Pressure Air Separation Unit
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 liquid nitrogen (LN2) stream connecting high-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 LN2 stream1. At nominal full-load operation, the flowrate of LN2 stream is maintained near this optimum value, whereas at part-load conditions this flowrate is typically modified in proportion with the load-change (oxygen demand) through a ratio/feed-forward controller. Due to nonlinearity in the entire ASU process, the ratio-modified LN2 flowrate does not guarantee an optimal oxygen yield at part-load conditions. This is further exacerbated when process disturbances in form of “cold-box” heat-leaks enter the system. To address this problem of dynamically maximizing the oxygen yield while the ASU undergoes a load-change and/or a process disturbance, a multiple model predictive control (MMPC) algorithm is proposed. This approach has been used in previous studies2 to handle large ramp-rates of oxygen demand posed by the gasifier in an IGCC plant. In this study, the proposed algorithm uses linear step-response “blackbox” models surrounding the operating points corresponding to maximum oxygen yield points at different loads. It has been 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 compared to a first-principle based nonlinear MPC.
1. P. Mahapatra and B. Wayne Bequette. “Process design and control studies of Elevated-Pressure Air Separation Unit for IGCC power plants,” In 2010 American Control Council Annual Meeting, Baltimore, DC, June (2010)
2. P. Mahapatra, B. Wayne Bequette, and S.E. Zitney. “Multiple Model Predictive Control of Air Separation Unit as part of IGCC Power Plant during Rapid Load Changes,” Presented at the AIChE 2011 Annual Meeting, Minneapolis, MN, October 16-21 (2011)