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Systems-Based Approaches at the Frontiers of Chemical Engineering

and Computational Biology:Advances and Challenges

Christodoulos A. Floudas Princeton University

Department of Chemical EngineeringProgram of Applied and Computational Mathematics

Department of Operations Research and Financial EngineeringCenter for Quantitative Biology

Outline

• Theme: Scientific/Personal Journey• Research Philosophy at CASL (Computer

Aided Systems Laboratory, Princeton University)• Research Areas: Advances & Challenges• Acknowledgements

Greece

Ioannina, Greece

Ioannina,Greece(Courtesy of G. Floudas)

Thessaloniki, Greece

Aristotle University of Thessaloniki

Department of Chemical Engineering

Aristotle University of ThessalonikiUndergraduate Studies(1977-1982)

Pittsburgh, PA

Carnegie Mellon University

Graduate Studies

How did it all start?

• Ph.D. Thesis: Sept. 82 – Dec. 85• Advisor: Ignacio E. Grossmann

• Synthesis of Flexible Heat Exchanger Networks (82-85)

• Uncertainty Analysis (82-85)

Princeton, NJ

Princeton University

Computer Aided Systems Laboratory

ResearchAreas

Product & Process Systems Engineering

Computational Biology & Genomics

Interface

• Chemical Engineering • Applied Mathematics• Operations Research• Computer Science• Computational Chemistry• Computational Biology

Unified Theoryand Research

Philosophy

• address fundamental problems and applications via mathematicalmodeling of microscopic, mesoscopic and macroscopic level

• rigorous optimization theory and algorithms• large scale computations in high performance clusters

Themes

Mathematical Modeling, Optimization Theory & Algorithms

Discovery at the Macroscopic Level

Discovery at the Microscopic Level

Research Areas (1986 – present)

Process Operations: Scheduling, Planning and Uncertainty

Process and Product Design & Synthesis

Interaction of Design, Synthesis & Control

Computational Biology & Genomics

Optimization Theory & Methods:- Mixed-Integer Nonlinear Optimization- Deterministic Global Optimization

Process & Product Design and Synthesis(1986-)


Process & Product Design and Synthesis

• Distillation Sequences (86-91)• Heat Exchanger Networks (86-91)• Reactor Networks and

Reactor-Separator-Recycle (87-02)• Phase Equilibrium (94-02)• Azeotropic Separations (96-02)

• Shape Selective Separation/Catalysis (05-)

Rational Design of Shape Selective Separation and Catalysis

C.E. Gounaris, C.A. Floudas, J. WeiDepartment of Chemical Engineering, Princeton University

Fundamental Questions• Given a candidate set of zeolite portals

and a pair of molecules (e.g., Molecule A and Molecule B),

(a) can we identify the best zeoliteportal which can separate molecule A from molecule B more effectively?

(b) can we generate a rank-ordered list of zeolite portals for such separation?

(c) can we identify such zeolite portals for any pair of molecules and the complete set of known zeolites?

Molecular Footprints• Start with a simple molecular model:

• Atoms are spheres of some effective radius• Bond lengths and angles considered fixed

• Given this 3-d conformation, rotate suitably the molecule and project it onto the 2-d xy-plane

• This projection is a set of circles• Circle centers are projections of atom nuclei• Circle radii are effective radii of atom spheres

• Different 3-d orientations result into different projections

• We define the molecular footprint to be such a projection that would likely be explored when penetration through a portal occurs

Molecular Footprints - Examples

• Aromatics :

• Benzene is a planar molecule and results into a linear projection

• The other aromatics have “almost” linear footprints

(a) benzene (b) toluene

(c) o-xylene (d) m-xylene (e) p-xylene

Strain-based Screening

• When a guest molecule approaches a host portal :

No passage -

Constrained passage -

Free passage -

There is no orientation for which all projected atom nuclei fall inside the portal area

There is some orientation for which all projected atom nuclei fall inside the portal area, but some circles have to be squeezed for a complete fit

There is some orientation for which all circles fall completely inside the portal area

Strain-based Screening - Model

• Let us define :

Amount of distortion on an atom :

Total Strain for a guest moleculeto penetrate through a host portal :

• Every projection is associated with some total strain and there is an optimal projection that exhibits the minimum strain, denoted as S*

• Define Strain Index :

• SI is a measure of total distortion needed for penetrationSI = 0 0 < SI < ∞ SI --> ∞

s

o

rr

δ =

12 6 12 6

1 1 1 1G H

i ji i j j

S S Sδ δ δ δ

⎛ ⎞⎛ ⎞= + = − + −⎜ ⎟⎜ ⎟ ⎜ ⎟⎝ ⎠ ⎝ ⎠

∑ ∑

*log(1 )SI S= +

(squeezed radius)

(original radius)

Strain-based Screening - Results

• 38 molecules / 217 zeolite windows

StrainIndex

Strain-based Screening - Results

• 38 molecules / 217 zeolite windows

Strain Index of aromaticson some 12-oxygen ring windows

Detail:

Interaction of Design & Control(1987-2000)


Interaction of Design & Control

• Structural Properties: Generic Rank• Dynamic Operability of MIMO Systems:

Time Delays & Transmission Zeroes• Multi-objective Framework for the Interaction of

Design and Control• Optimal Control of Reactors• Dynamic Models in the Interaction of Design and

Control

Deterministic Global Optimization(1987-)


Deterministic Global OptimizationHow/When did we start?

Motivation: Multiple local minima in- distillation sequencing- heat exchanger networks- reactor network synthesis- pooling problems

Initial Studies: 1987-89

Historical Global Optimization Perspective

1980-1984 1985-1989 1990-1994 1995-1999 2000-2006

# Pu

blic

atio

ns

3397

1046

2397

5496

1960-1979

25

ChE Global Optimization Perspective:Early Contributions

1980 1985 1990 1995 2000

ChE

Pub

licat

ions

Stephanopoulos & Westerberg (1975)

Westerberg & Shah (1978)Wang & Luus (1978)

Floudas, Agarwal, Ciric (1989)

1975 2005

Floudas & Visweswaran (1990)

Deterministic Global Optimization• GOS: Global Optimum Search (88-89)

• GOP: Biconvex, Bilinear, Polynomial (90-96)

• aBB: Twice Continuously Differentiable Constrained Nonlinear Problems (94-98)

• SMIN-aBB, GMIN-aBB: Mixed-Integer Nonlinear (98-00)

• DAE: Differential-Algebraic Systems (98-02)

• Bilevel Nonlinear Problems (01-05)

• Convex Envelopes for Trilinear Monomials (03-04)

• Convex Underestimators for Trigonometric Functions (04-05)

• G-aBB: Generalized aBB (04-)

• P-aBB: Piecewise aBB (05-)

• Tight Convex Underestimators for General C2 Functions (06-)

Deterministic Global Optimization: Chemical Engineering Applications

• Phase & Chemical Reaction Equilibrium

• Homogeneous, Heterogeneous & Reactive Azeotropes

• Pooling Problems

• Parameter Estimation & Data Reconciliation

• Trim Loss Minimization

• Generalized Pooling Problems

• Nesting of Arbitrary Shapes

Generalized Pooling ProblemMeyer, Floudas (2006)

Sources Plants Destinations

Q1: What is the optimal topology? Binary TermsQ2: Which plants exist? Binary Variables

Objective: Minimize Overall Cost- Plant construction and operating costs

- Pipeline construction and operating cost

Binary Variables- ya

s,e: Existence of stream connecting source s to exit stream e.

- ybt,e: Existence of stream connecting plant t to exit stream e.

- yct,t’: Existence of directed stream connecting plant t to plant t’.

- yds,t: Existence of stream connecting source s to plant t.

- yet: Existence of plant t.

Formulation of Generalized Pooling Problem

Problem Characteristics- Mixed integer bilinear programming problem with bilinearities involving

pairs of continuous variables, (b,f) and (c,f) and (d,f).- Nonconvex mass balance constraints on the species include bilinear

terms.- Industrial case study: |C| = 3, |E| = 1, |S| = 7, |T| = 10.- Number of nonconvex equality constraints: |C| x (|T| + |E|). (33)

- Number of bilinear terms: |C| x |T| x (|E| + |S| + 2|T| - 2). (780)- Complex network structure with numerous feasible yet nonoptimal

possibilities.- Number of binary variables: |T| x (|E| + |S| + |T|) + |S| x |E|. (187)- Fixing the y variables, the problem is a nonconvex bilinear NLP.

Feasible Solutions

S2

S1

S3

S4

S5

S6

S7

E1

T2

T3

T7

T9

Objective function value: 1.132e6

S2

S1

S3

S4

S5

S6

S7

E1

T3

T7

T9

T10

Objective function value: 1.198e6Objective function value: 1.086e6

S2

S1

S3

S4

S5

S6

S7

E1

Objective function value: 1.620e6

1T1

3T3

T7

T9

S2

S1

S3

S4

S5

S6

S7

E1

T3

T7

T9

T10

Industrial Case StudyComponents: 3 Best known solution: 1.086 x 106

Sources: 7 Lower bound on solution: 1.070 x 106

Exit streams: 1 Absolute Gap: 0.016 x 106

Potential plants: 10 Relative Gap: 1.5 %

1.0705948611706139766Bin RLT N = 7

1.0518580010338127766Bin RLT N = 6

1.03176178970115766Bin RLT N = 5

1.02236727602103766Bin RLT N = 4

1.005816623491766Bin RLT N = 3

0.977519486679766Bin RLT N = 2

Subnetwork {t3, t7, t9, t10}

0.7433621193211873850RLT

0.550583544187987Bilinear Terms

1.0862.5424187207Nonconvex

Obj (106)CPU (s)Constr.{0,1} varℜ var.Formulation

Challenges/OpportunitiesCurrent Status: Great Success for Theory & Algorithm for Small to Medium-size Applications

• Improved Convex Underestimation Methods• Now Theoretical Results on Convex

Envelopes• Medium to Large-scale C2-NLPs

– Pooling Problems• Medium to Large-scale MINLPs

– Product & Process Design/Synthesis/Operations– Signal Transduction/Metabolic Pathways– Generalized Pooling Problems

• New Theory and Algorithms for DAE Models• New Theory and Algorithms for Grey/Black

box models• Multi-level Nonlinear Optimization

Computational Biology & Genomics(1990-)


Computational Biology & GenomicsHow/When did we start?

Motivation: Multiple local minima in- Lennard-Jones Cluster packing- Structure prediction of small molecules- Structure prediction of oligo-peptides- Structure prediction in protein folding


Computational Biology and Genomics• Structure Prediction in Lennard-Jones Clusters & Acyclic Molecules (90-95)

• Structure Prediction in Protein Folding (95-)

• Dynamics in Protein Folding (96-00)

• Force Field Development (01-)

• De Novo Protein Design (01-)

• Protein-Peptide Interactions (95-03)

• Metabolic and Signal Transduction Networks (95-)

• Proteomics: Peptide & Protein Identification (05-)

Amino acid sequence [PDB: 1q4sA ]MHRTSNGSHATGGNLPDVASHYPVAYEQTLDGTVGFVIDEMTPERATASVEVTDTLRQRWGLVHGGAYCALAEMLATEATVAVVHEKGMMAVGQSNHTSFFRPVKEGHVRAEAVRIHAGSTTWFWDVSLRDDAGRLCAVSSMSIAVRPRRD

Beta strand and sheet structureMHRTSNGSHATGGNLPDVASHYPVAYEQTLDGTVGFVIDEMTPERATASVEVTDTLRQRWGLVHGGAYCALAEMLATEATVAVVHEKGMMAVGQSNHTSFFRPVKEGHVRAEAVRIHAGSTTWFWDVSLRDDAGRLCAVSSMSIAVRPRRD

3D Protein Structure

Structure Prediction in Protein Folding

Helical structureMHRTSNGSHATGGNLPDVASHYPVAYEQTLDGTVGFVIDEMTPERATASVEVTDTLRQRWGLVHGGAYCALAEMLATEATVAVVHEKGMMAVGQSNHTSFFRPVKEGHVRAEAVRIHAGSTTWFWDVSLRDDAGRLCAVSSMSIAVRPRRD

Protein Folding: Advances• Homology Modeling / Comparative Modeling

– The probe and template sequences are evolutionary related– Honig et al.; Sali et al.; Fischer et al.; Rost et al;

• Fold Recognition / Threading– For the query sequence, determine closest matching

structure from a library of known folds by scoring function– Skolnick et al.; Jones et al.; Bryant et al.; Xu et al.; Elber et

al.;– Baker et al.; Rychlewski & Ginalski; Honig et al.

• First Principles with Database Information– Secondary and/or tertiary information from

databases/statistical methods– Levitt et al.; Baker et al.; Skolnick, Kolinski et al.; Friesner

et al.• First Principles without Database Information

– Physiochemical models with most general application– Scheraga et al.; Rose et al.; Floudas et al.

Derivation of Restraints-Dihedral angle restrictions-Cα−Cα distance constraints

Helix Prediction-Detailed atomistic modeling-Simulations of local interactions(Free Energy Calculations)

Tertiary Structure Prediction-Structural data from previous stages-Prediction via novel solution approach

(Global Optimization and TorsionalAngle Dynamics)

Flexible Stems Loop Prediction-Dihedral angle sampling-Discard conformers by clustering(Novel Clustering Methodology)

β-sheet Prediction-Novel hydrophobic modeling-Predict list of optimal topologies(Combinatorial Optimization)

Force Field for High and MediumResolution Decoys-Novel linear programming approach-Distinguishes high resolution structures

(Large-scale linear programming)

Interhelical Contacts-Maximize common residue pairs-Rank-order list of topologies(MILP Optimization Model)

α / β proteinsα proteins

Improved Distance Restraints-Iterative LP-based boundtightening approach

Enhanced ASTRO-FOLD

Structure Prediction- S824: Blind Test S824: 102 Residues (Professor Michael Hecht, Princeton University)No knowledge of secondary/tertiary structure

Backbone variable restraints• α-helices: 5-21, 30-49, 56-75, 80-100Distance restraints• No β sheet contacts • 63 lower and upper Cα-Cα for α-helices Klepeis, Floudas, Wei, Hecht, Proteins (2005)

Tertiary Fold• Best Energy: -846.0 kcal/mol RMSD: 5.1 Å (Prediction:2003)

S836 Lowest E vs. S836 (NMR-1): Blind Test

1-4 1-2 2-3 3-4

S836: 102 Residues (Professor Michael Hecht, Princeton University)No knowledge of secondary/tertiary structure

Lowest Energy: -1740 Kcal/molRMSD: 2.84 A

Lowest RMSD: 2.39 A (Prediction:2006)

S836 Lowest E vs. S836 (NMR 20 models)

1-4 1-2 2-3 3-4

Prediction: October 2006

Challenges and Opportunities• New/Improved Methods for Prediction of Helices• New/Improved Methods for Prediction of β-strands/β-sheet topologies

• Loop Prediction (3-D)• Fixed stems (crystallography)• Flexible stems (first principles method)

• Prediction of Disulfide Bridges• Force-field development for Fold Recognition• New/Improved Methods for Threading/Fold Recognition• Uncertainty in Force-fields• Packing of Helices in Globular Proteins

• Prediction of Tertiary Interhelical Contacts in α and α/β proteins• Helical Membrane Proteins (e.g. GPCRs)

• Improved prediction of Helical Sequences• Loop predictions • Packing of Helices in Lipid Bilayers• 3-D structure prediction

De Novo Protein DesignDefine target template

Human β-Defensin-2hbd-2 (PDB: 1fqq)

Full sequence designMayo et al.; Hellinga et al.; DeGrado et al;

Saven et al.; Hecht et al.

Design folded protein

ChallengesIn silico sequence selection

Fold specificity

Backbone coordinates for N,Ca,C,Oand possibly Ca-Cb vectors from PDB

Which amino acid sequences willstabilize this target structure ?

Combinatorial complexity-Backbone length : n-Amino acids per position : mmn possible sequences

De Novo Protein DesignDefine target template

Human β-Defensin-2hbd-2 (PDB: 1fqq)

Full sequence designMayo et al.; Hellinga et al.; DeGrado et al;

Saven et al.; Hecht et al.

Design folded protein

ChallengesIn silico sequence selection

Fold validation/specificity

Backbone coordinates for N,Ca,C,Oand possibly Ca-Cb vectors from PDB

Which amino acid sequences willstabilize this target structure ?

Combinatorial complexity-Backbone length : n-Amino acids per position : mmn possible sequences

De Novo Protein DesignStructure to Function

Enhance Structural Stability

Enhance Functionality

Combinatorial complexity• Backbone length : n• Amino acids per position : m

Multiplicity of sequences• How to determine most stable ?• How to determine most functional ?

mn possible sequences

De Novo Protein Design Framework: AdvancesKlepeis, Floudas,Lambris & Morikis,JACS(2003); Klepeis et al., IECR(2004)Loose, Klepeis, Floudas, PROTEINS (2004); Fung, Rao, Floudas et al., JOCO (2005)Fung, Taylor, Floudas et al., OMS (2006)

Sequence selection• Identify target template for desired fold;specify coordinates of backbone

• Identify possible residue mutations• Introduce distance dependentpairwise potential based on Ca

• Generate rank-ordered energeticlist from mixed-integer linear (MILP)

Fold Validation via Astro-Fold• Model selected sequences using flexible, detailed energetics• Employ global optimization for free system• Employ global optimization for system constrained to template

• Calculate relative probability for structures similar to desired fold

CompstatinPotent inhibitor of third component of complement

Structural features• Cyclic, 13 residues• Disulfide Bridge Cys2-Cys12• Central beta-turn

Gln5-Asp6-Trp7-Gly8• Hydrophobic core• Acetylated form displayshigher inhibitory activity

Functional features• Binds to and inactivatesthird component of complement

• Structure of bound complex notyet available

with Dr. John Lambris(Univ. of Pennsylvania)and Dr. Dimitri Morikis(Univ. of California, Riverside)

Ac-compstatin

In Silico De Novo Design

Analog Ac-V4Y/H9AAnalog Ac-W4Y/H9A

Klepeis, Floudas, Morikis, Tsokos, Argyropoulos, Spruce, Lambris (2003) J. American Chemical Society.Klepeis, Floudas, Morikis, Lambris (2004) Ind. & Eng. Chem. Res.Fung, Rao, Floudas (2005); Fung, Taylor, Floudas (2006)

x7 x16x45

Challenges and Opportunities• Improved Methods for In Silico Sequence Selection with flexible templates from 2013

to 2050 to 20100

• Improved Force-field development for De Novo Protein Design

• Simultaneous Sequence and Structure Selection

• Design of Peptidic Inhibitors for Complement 3• Design of novel human β-defensin• Discovery of novel GPCRs• De Novo Design of Medium-size Proteins• Map Sequences to Known Folds

Proteomics: Peptide and Protein Identificationvia Tandem Mass Spectroscopy

LKYVI STCMYAR DILNG

GGAWKLK ILFAD

MS-MS spectra

A B C

Peptide Mixture Peptide Identifications

Protein sample Protein identifications

Protein level

Enzymaticdigestion

Peptide level

Mixture separationMS-MS sequencing

MS-MS spectra level

ValidationDatabase search

Experimental C

ompu

tatio

nal

Validation

Peptidegrouping

?

Peptide & Protein Identification via Tandem MS

• Database-based methods• Correlate the experimental spectra with spectra of peptides/proteins which exist in the databases

• De Novo Methods• Predict peptides without sequence databases• Exhaustive listing; sub-sequencing; graphical• Graph theory and shortest path algorithms• Graph theory and dynamic programming• Bayesian scoring of random peptides

Key ideaUtilization of binary variablesbinary variables to model logical decisions: 1 = yes; 0 = no

Paths between peaks (wij)Selection of peaks (pi)

Novel ConceptNovel Concept: use of mixed-integer linear optimization (MILPMILP) to

solve the peptide sequencing problem

De Novo Framework: De Novo Framework: PILOTPILOT

Peptide identification via Integer Linear Optimization and Tandem mass spectrometry

Challenges and Opportunities• Develop a De Novo computational approach based on a novel Mixed-Integer Linear Optimization (MILP) framework for the peptide identification using only information of the ion peaks in the spectrum

• Develop a hybrid method in combination with database methods

• Develop a novel approach which will account for experiment uncertainty

• Develop computational methods for protein identification

• Develop approaches for predicting protein-protein interactions in a complex mixture of proteins using tandem MS/MS and protein cross-linking technology

Process Operations: Scheduling & Planning(1996-)


Process Operations: Scheduling & PlanningHow/When did we start?

Suggestion of Prof. R.W.H. Sargent, Imperial College, Fall 1992.

Motivation: Are Continuous-Time Formulations Effective for Short-Term Scheduling?


Process and Product Operations: Scheduling, Planning & Uncertainty

• Short-Term Scheduling: Unit-Specific Event-BasedContinuous-Time Approach (98-)

• Design, Synthesis & Scheduling (01-)• Medium-Term Scheduling (02-)• Reactive Scheduling (05-)• Scheduling with Resources (04-)• Scheduling under Uncertainty (04-)• Planning & Scheduling (05-)

Process Operations: Scheduling• Given:

– Production in terms of task sequences– Pieces of equipment and their ranges of capacities– Intermediate storage capacity– Production requirement– Time horizon under consideration

• Determine:– Optimal sequence of tasks taking place in each unit– Amount of material processed at each time in each unit– Processing time of each task in each unit

• so as to optimize a performance criterion,– Maximization of production, minimization of makespan, etc.

Process Operations: Scheduling - Advances

• From Discrete-Time to Continuous-TimeScheduling Approaches

– Significant reduction of binary variables(combinatorial complexity)

– Better solutions & improved integrality gap– Address industrial case studies effectively

• Short-term scheduling (days)• Medium-term scheduling (weeks)

– Rolling horizon approaches– Decomposition methods

• Periodic scheduling

Floudas & Lin, (2004a): C&ChE; Floudas & Lin (2005): Annals of OR

Global Event Based Models Unit-Specific Event Based Models

Short-Term, Medium-Term and Reactive Scheduling of an Industrial

Polymer Compounding Plant

Plant Data Description• Over 80 different products considered in time horizon

(250 overall)• Over 85 orders in nominal schedule and over 65 orders

added in reactive schedule• Basic operations: reaction, filtering, storage, filling• Units: reactors, filters, prill tower, swing and product

tanks, filling stations – (85 units)• Scheduling horizon: ~ 2 weeks• Storage limitations on reactors and tanks• Campaign mode production for prill tower and associated

units• Additional considerations:

– Clean-up times for each unit switching between tasks– Demands with intermediate due dates– Different types of final products

Process Alternatives: Polymer Compounding Plant

State-Task Network (STN) Representation

F Type 1 I1 Type 6 P

F Type 1 I1 Type 4a P Type 6 P

F Type 1 I1 Type 4b P Type 6 P

F Type 1 I1 PType 4b Type 6 PType 3 I2

F Type 2 I1 Type 4a P Type 6 P

F Type 1 I1 PType 5 Type 6 PType 4a I2

F Type 2 I1 Type 4a I2 Type 4a P Type 6 P

Mathematical Framework• Decompose the large and complex problem for a

long time period into smaller short-term scheduling sub-problems in successive time horizons.

• Decomposition determines each time horizon as well as the products to include based on:– Number of products with demands– Complexity of corresponding process recipes– Resulting computational complexity

• Connection between consecutive time horizons:– Available starting time of units– Available intermediate materials

Industrial Polymer Compounding Plant: Case Study 2

• Campaign Mode Production determined first• Nominal and Reactive Scheduling performed• Time horizons considered: 18 days• Constraint to limit lateness of orders to be <= 24 hours

Dr. J. Kallrath, BASF Dr. A. Schreieck, BASF Stacy Janak

Case 2: Nominal: Process Units (18 days)

Case 2: Nominal: Storage Units (18 days)

Case 2: Results Summary (18 days)

41.14

20.33

% Increase

35.41

20.49

% Increase

242128.48

171556.64

244006.16

202774.07

Profit Value

1504.47

1848.11

Extra Time (hr.)

2264.578Reactive Demand

3066.419Reactive Prod.

2799.119Nominal Prod.

Production (tons)

2323.191Nominal Demand

• In both cases, production is increased significantly compared to the required demand. The value of the profit also increased compared to the value of the required demand.

• In the reactive schedule, the overall demand has decreased compared to the nominal schedule, but the production and profit have increased.

• The extra time is the total time available for additional production in all the reactors where blocks of time must be 11 hours or greater.

Challenges/Opportunities• Modeling to reduce/close the integrality gap

• New/Improved Methods for Medium-term scheduling

• Multi-site production scheduling

• Reactive Scheduling

• Scheduling under Uncertainty

• Design/Synthesis and Scheduling under Uncertainty

• Planning and Scheduling

• Planning under Uncertainty

• Validation/Application to Manufacturing Operations

Acknowledgements

Aristotle University of ThessalonikiUndergraduate Studies

Prof. V. Papageorgiou

Organic Chemistry

Prof. C. Georgakis

Process Control

Prof. S. Nychas

Fluid Mechanics

Prof. M. Assael

DiplomaThesis

Prof. I. Vasalos

Petroleum Technologies

Prof. Bekiaroglou Prof. A. Karabelas

Physical Chemistry Process Design

Graduate Studies

How did it all start?

• Ph.D. Thesis: Sept. 82 – Dec. 85• Advisor: Ignacio E. Grossmann

• Synthesis of Flexible Heat Exchanger Networks (82-85)

• Uncertainty Analysis (82-85)

Carnegie Mellon UniversityGraduate Studies

Prof. I.E. Grossmann

Prof. L.T. Biegler Prof. K.O. Kortanek Prof. A.W. Westerberg

Prof. M. Jhon Prof. D. Prieve Prof. G. Powers Prof. P. Sides Prof. J.L. Anderson

Princeton University

Chemical EngineeringSenior Theses

B.Mickus 5/2003

Christopher R. Loose 5/02Michael Pieja 5/01

Cole DeForest May 2006Paul Reiter 5/05 Rachel Hoff 5/05Marty Taylor 5/05 Huan Zheng 5/05

Jared Jensen 5/03 Sasha Rao 5/04

Ralph Kleiner 5/05

David R. Volk 5/02

T. Hene 5/97 M.Ow 5/98Duncan Rein 5/97 M. Matz 5/98

A. Rojnuckarin 5/94 J. Bossert 5/96 Navin Nayak 5/96

C. Gaffney 5/99

Jeff Wilke 5/89Russell Allgor 5/88 C. Papouras 5/89

Chemical EngineeringSenior Theses with Scholarly Publications

B.Mickus 8/2000-5/2003

Christopher R. Loose 5/02Michael Pieja 5/01

Cole DeForest May 2006Paul Reiter 5/05 Rachel Hoff 5/05Marty Taylor 5/05 Huan Zheng 9/02-5/05

Jared Jensen 5/03Sasha Rao 5/04

Ralph Kleiner 9/02-5/05

David R. Volk 5/02

T. Hene 5/97 M.Ow 5/98Duncan Rein 5/97

M. Matz 5/98

A. Rojnuckarin 5/94 J. Bossert 5/96 Navin Nayak 5/96

C. Gaffney 5/99

Jeff Wilke 5/89Russell Allgor 5/88 C. Papouras 5/89

Princeton University – CASLGraduate Students

G.E. Paules A. AggarwalA.R. Ciric P. PsarrisA.C. Kokossis M.L. Luyben V. Visweswaran

C.M. McDonald C.D. Maranas V. Hatzimanikatis C.S. Adjiman C.A. Schweiger S.T. Harding W.R. Esposito J.L. Klepeis

H.D. Schafroth X. Lin Z.H. Gumus C.A. Meyer S.L. Janak S.R. McAllister

H.K. Fung C.E. Gounaris R. Rajgaria P.A. DiMaggio M.P. Tan P. Verderame

Princeton University - CASLPost-doctoral Associates

A. Georgiou (88-90) V. Vassilliadis (93-94) I.P. Androulakis (93-96) M.G. Ierapetritou (96-98)

K.M. Westerberg (97-02) S. Caratzoulas (01-02) J.L. Klepeis (02-03)

I.G. Akrotirianakis (01-04) M. Monnigmann (04-05) M.A. Shaik (05-present)

Research Collaborators

Prof. J.R. BroachPrinceton University

Prof. M. HechtPrinceton University

Prof. D. MorikisUC at Riverside

Prof. J.D. LambrisUniv. Pennsylvania

Prof. A. NeumaierUniv. Vienna

Prof. E.N. PistikopoulosImperial College

Prof. P.M. PardalosUniv. Florida

Prof. H. RabitzPrinceton University

Prof. J. WeiPrinceton University

Prof. O. SteinRTWH Aachen

Prof. H.A. WeinsteinCornell Medical School

Prof. R. SilicianoJohns Hopkins Univ.

Prof. H.Th. JongenRTWH Aachen

Princeton CHE Colleagues

Prof. D.A. Saville

Prof. P.G. Debenedetti Prof. A.Z. PanagiotopoulosProf. I.G. Kevrekidis

Prof. S. SundaresanProf. W.R. Schowalter Prof. J. Wei

Prof. R. Jackson

Prof. T.K. VanderlickProf. R.A. Register Prof. S.Y. Shvartsman

Prof. I.A. Aksay Prof. J.B. Benziger Prof. R.K.Prud’homme

Prof. D.W. WoodProf. W.B. RusselProf. J.D. Carbeck Prof. S.M. Troian

Funding

• National Science Foundation

• National Institutes of Health

• Air Force Office of Scientific Research

• Environmental Protection Agency

• Mobil• Eastman• Amoco• E.I. Du Pont Nemours • Shell Laboratorium• ExxonMobil• General Motors• Delphi• Arkema (Atofina)• AspenTech• BASF

Documents

Systems-Based Approaches at the Frontiers of Chemical ... Floudas simplified 02.pdf · Frontiers of Chemical Engineering and Computational Biology: ... Ignacio E. Grossmann ... •