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020 ▼a 9780438058286
035 ▼a (MiAaPQ)AAI10810249
035 ▼a (MiAaPQ)lehigh:11893
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0820 ▼a 660
1001 ▼a Urich, Matthew D. ▼0 (orcid)0000-0001-5146-2895
24510 ▼a Dynamic Modeling, Predictive Control and Optimization of a Rapid Pressure Swing Adsorption System.
260 ▼a [S.l.] : ▼b Lehigh University., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 144 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
500 ▼a Adviser: Mayuresh V. Kothare.
5021 ▼a Thesis (Ph.D.)--Lehigh University, 2018.
520 ▼a Rapid Pressure Swing Adsorption (RPSA) is a gas separation technology with an important commercial application for Medical Oxygen Concentrators (MOCs). MOCs use RPSA technology to produce high purity oxygen (O2) from ambient air, and provide med
520 ▼a In this work, a study of dynamic modeling, predictive control and optimization of this single-bed RPSA device is presented. A detailed, nonlinear plant model of the RPSA device is used to study the dynamics of the system as well as design a Mode
520 ▼a The MPC is implemented onto a lab-scale MOC prototype using Raspberry Pi hardware, and evaluated using several MOC-relevant disturbance scenarios. The MPC is also expanded using piece-wise linear modeling to improve the performance of an RPSA de
520 ▼a Design and optimization of RPSA systems remains an active area of research, and many PSA models have been used to optimize RPSA cycles in simulation. In this work, a model-free steady state optimization approach using the embedded hardware is pr
590 ▼a School code: 0105.
650 4 ▼a Chemical engineering.
650 4 ▼a Engineering.
690 ▼a 0542
690 ▼a 0537
71020 ▼a Lehigh University. ▼b Chemical Engineering.
7730 ▼t Dissertation Abstracts International ▼g 79-10B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0105
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14997930 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자