MARC보기
LDR02103nam u200397 4500
001000000421523
00520190215165402
008181129s2018 |||||||||||||||||c||eng d
020 ▼a 9780438048003
035 ▼a (MiAaPQ)AAI10816009
035 ▼a (MiAaPQ)princeton:12533
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 247004
0820 ▼a 660
1001 ▼a Matthews, Logan Ryan.
24510 ▼a Advancing Robust Optimization for Process Systems Engineering Applications.
260 ▼a [S.l.]: ▼b Princeton University., ▼c 2018.
260 1 ▼a Ann Arbor: ▼b ProQuest Dissertations & Theses, ▼c 2018.
300 ▼a 321 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
500 ▼a Adviser: Ioannis G. Kevrekidis.
5021 ▼a Thesis (Ph.D.)--Princeton University, 2018.
520 ▼a Robust optimization is a popular method for incorporating parameter uncertainty into optimization models. Whether parameters represent the price of a feedstock or product, the operability of an edge in a network, or length of time required for a
520 ▼a This dissertation seeks to expand the theory and application of robust optimization for problems in process systems engineering. Theoretically, this focuses on decreasing the conservatism and increasing the applicability of robust optimization m
520 ▼a Robust optimization is also shown to be effective in two major application areas. First, it is applied to process synthesis and global optimization of liquid transportation fuel refineries from natural gas and biomass, when feedstock prices, pro
590 ▼a School code: 0181.
650 4 ▼a Chemical engineering.
690 ▼a 0542
71020 ▼a Princeton University. ▼b Chemical and Biological Engineering.
7730 ▼t Dissertation Abstracts International ▼g 79-10B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0181
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998214 ▼n KERIS ▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다.
980 ▼a 201812 ▼f 2019
990 ▼a ***1012033