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April 2013

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Mon, 22 Apr 2013 10:40:30 -0400
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Subject: WebCAST Seminar by Floudas on May 7, 2013
WebCAST seminar on “Multi-scale Approaches for Optimizing Novel Hybrid
Feedstock Energy Processes”
 
by Christodoulos A. Floudas 
Department of Chemical and Biological Engineering
Princeton University
 
Date: May 7, 2013, 2-4 pm (EDT)
 
Dial-in from the comfort of your office to hear the presentation.

Deadline to Register: May 3, 2013 (details at www.castdiv.org/WebCAST.htm)

This is a joint CAST / CACHE initiative. The registration rate is: 
• free for the first 100 CAST members and CACHE departments (including
industrial affiliates) 
• for all others, $25/connection. Joining the CAST Division of AIChE only
costs $10/year.]  

Abstract:

Heavy dependence on petroleum and high greenhouse gas (GHG) emissions from
the production, distribution, and consumption of hydrocarbon fuels pose
serious challenges for the United States (US) transportation sector.
Depletion of domestic petroleum sources combined with a volatile global oil
market prompt the need to discover alternative fuel-producing technologies
that utilize domestically abundant sources. The primary aim in the discovery
of hybrid energy processes is to combine coal, biomass, and natural gas to
meet the United States transportation fuel demand.

The first part of this presentation will outline the needs and introduce
novel hybrid feedstock coal, biomass, and natural gas to liquids (CBGTL)
process alternatives. The second part will address important decisions at
the process design and process synthesis level. A thermochemical based
process superstructure, its mixed-integer nonlinear optimization (MINLP)
model, and systematic approaches for its global optimization will be
discussed. The third part will introduce a novel framework for the optimal
energy supply chain of CBGTL processes. The optimal network topology
provides information on (i) the optimal plant locations throughout the
country, (ii) the locations of feedstock sources, (iii) the
interconnectivity between the feedstock source locations, CBGTL plants
locations, and the demand locations, (iv) the modes of transportation used
in each connection, and (v) the flow rate amounts of each feedstock and
product type. Life cycle analysis on the nationwide energy supply chain
shows that at least 50% reduction of GHG emissions is attainable.

Biographical Sketch:

Dr. Floudas is the Stephen C. Macaleer ’63 Professor in Engineering and
Applied Science, Professor of Chemical and Biological Engineering at
Princeton University, Faculty in the Center for Quantitative Biology at
Princeton University’s Lewis-Sigler Institute, Associated Faculty in the
Program of Computational and Applied Mathematics at Princeton University,
Department of Operations Research and Financial Engineering at Princeton
University, and the Andlinger Center for Energy and the Environment. He
earned his B.S.E. in 1982 at Aristotle University of Thessaloniki, Greece,
completed his Ph.D. in December 1985 at Carnegie Mellon University.
Professor Floudas is the author of two graduate textbooks, Nonlinear
Mixed-Integer Optimization (Oxford University Press, 1995), and
Deterministic Global Optimization (Kluwer Academic Publishers, 2000). He has
co-edited ten monographs/books, has over 270 refereed publications, and is
the chief co-editor of the Encyclopedia of Optimization (Kluwer Academic
Publishers, 2001; 2nd edition, Springer, 2008). He is the recipient of
numerous awards and honors for teaching and research that include the NSF
Presidential Young Investigator Award, 1988; the Engineering Council
Teaching Award, Princeton University, 1995; the Bodossaki Foundation Award
in Applied Sciences, 1997; the Best Paper Award in Computers and Chemical
Engineering, 1998; the Aspen Tech Excellence in Teaching Award, 1999; the
2001 AIChE Professional Progress Award for Outstanding Progress in Chemical
Engineering; the 2006 AIChE Computing in Chemical Engineering Award; the
2007 Graduate Mentoring Award, Princeton University; and Member of National
Academy of Engineering, 2011.

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