450130 Multivariable Model Predictive Control of a Novel Rapid Pressure Swing Adsorption Process

Thursday, November 17, 2016: 12:30 PM
Monterey II (Hotel Nikko San Francisco)
Matthew Urich, Lehigh University

Abstract for AIChE Conference, 2016                                                                      March 14, 2016

Multivariable Model Predictive Control of a Novel Rapid Pressure Swing Adsorption Process

Matthew Urich[1], Rama Rao Vemula, Mayuresh V. Kothare

Department of Chemical and Biomolecular Engineering, Lehigh University

Bethlehem, PA, USA 18015

            A novel, single bed Rapid Pressure Swing Adsorption (RPSA) system was developed previously, and details can be found in [1,2]. The principal objective of this research work is the design of control algorithm for the operation of a novel, single-bed, 4-step, Rapid Pressure Swing Adsorption (RPSA) Process. The proprietary RPSA design has an adsorber enclosed inside a product storage tank which supplies ~90% oxygen continuously and back purges the column during regeneration steps. A N2-O2 gas mixture was selected as the adsorbate and a Li-X zeolite was chosen as an adsorbent for this modeling and simulation study. A Skarstrom type, 4-step, RPSA cycle has been implemented for the separation of O2 from the N2-O2 mixture. During the pressurization step, the adsorption column pressurizes to a super atmospheric pressure from the feed-end with compressed N2-O2 mixture. High purity O2 goes to the storage tank during the adsorption step from the product-end while compressed N2-O2 is continuously supplied at the feed-end. During the blow down step, the column pressure is reduced to atmosphere for desorbing the nitrogen from feed-end. During the purge step, a portion of the high purity oxygen is used to back purge the column and clean nitrogen from the column voids. A mathematical model of the RPSA process integrated with the product storage tank was developed from first principles and the model equations were solved using COMSOL MULTIPHYSICS with MATLAB. The model incorporates detailed adsorption kinetics, equilibrium equations, heat effects, mass transfer behavior and pressure effects. To the best of knowledge, this is the first attempt to incorporate all physical chemical effects coupled with storage tank dynamics for a dynamic RPSA simulator. The generation of a control algorithm/scheme is critical for the optimum operation of RPSA process by controlling the product storage tank pressure, and oxygen product purity. Most reported control studies only use adsorption time as a manipulated variable [3,4]. We propose a full multivariable control architecture which varies all four step times in the cycle as manipulated variables. The controller uses individual step durations as the manipulated variables to achieve the control objective. The Model Predictive Controller presented here uses an identified linear observer model and an optimization program to optimally achieve the control objectives. Open and closed-loop disturbance simulations are compared to evaluate the MPC controller performance. The results obtained from the closed-loop simulations and challenges for implementing a control structure for RPSA design will be discussed in this presentation.


[1] Chai SW, Kothare MV, Sircar S. Rapid pressure swing adsorption for reduction of bed size factor of a medical oxygen concentrator. Ind. Eng Chem Res. 2011; 50: 8703-8710.

[2] Rama Rao V, Kothare MV, Sircar S. Novel Design and Performance of a Medical Oxygen Concentrator Using a Rapid Pressure Swing Adsorption Concept. AIChE. 2014; 60: 3330-3335.

[3] Khajuria H, Pistikopoulos EN. Dynamic modeling and explicit/multi-parametric MPC control of pressure swing adsorption systems. Journal of Process Control. 2011; 21: 151-163.

[4] Khajuria H, Pistikopoulos EN. Optimization and control of pressure swing adsorption processes under uncertainty. AIChE. 2012; 59: 120-131.

[1] Presenting author: Matthew Urich

Extended Abstract: File Uploaded
See more of this Session: Process Control Applications I
See more of this Group/Topical: Computing and Systems Technology Division