CAST Award Lecture

8/10/2019 CAST Award Lecture

1/51

From Macroscopic World to Microscopic World

through Mazes of Process Graphs and fromMicroscopic World to Mesoscopic World through

Drunkards Paths

Computing in Chemical Engineering Award

Lecture

Copyright by L. T. Fan 2004All rights reserved.

Department of Chemical Engineering

Kansas State University

Manhattan, Kansas 66506, U.S.A.Phone: (785) 532-5584

Fax: (785) 532-7372

E-mail: [email protected]

Computing and Systems Technology (CAST) Banquet

November 18, 2003

AIChE Annual MeetingSan Francisco, CA, November 16 21, 2003

Abstract

This is a narrative of my 2003 Computing in Engineering Award lecture. Itessentially comprises two parts: graph-theoretical approach to process-network synthesis

and stochastic analysis and modeling of random phenomena in process systems. These

areas are two focuses of my research and teaching endeavors in recent years.


2/51

Table of Contents

Page

Abstract i

Introduction 1

From Macroscopic World to Microscopic World through Mazes of Process

Graphs 2

Pre-process-graphs: early 1950s ~ late 1980s 2

Meeting of minds: 1989 ~ 90 3

First milestone: early 1990s 3

Second milestone: mid 1990s 6

Third milestone: mid 1990s ~ late 90s 8

Fourth milestone: late 1990s ~ early 2000 8

Fifth milestone: early 2000 9

Current status and future prospect of P-graphs 9

From Microscopic World to Mesoscopic World through Drunkards Paths 10

Awakening at dawn: 1950s 10

Encounter with Markov: mid 1960s ~ early 1970s 11

Encounter with Prigogine: early 1980s 12Encounter with van Kampen: mid 1980s 13

Current status and future prospect of stochastic analysis and modeling 14

Concluding Remarks 15

Acknowledgements 15

Attachment 1. List of Publications on P-graphs by L. T. Fan and Collaborators

Attachment 2. List of Publications on Stochastic Analysis and Modeling by L. T.

Fan and Collaborators

Att h t 3 Abb i t d C d d Vi G h f Th P


3/51

Introduction

The award citation states, For broad and outstanding contributions to the

analysis, synthesis, and control of process and material systems. This citation implies

that my contributions have encompassed many areas of Computing and Systems

Technologies in Chemical Engineering in the nearly 50 years of my professional career.

These areas include: 1. Computer-Aided Process Synthesis, Design, and Optimization; 2.

Process Identification, Dynamics and Control; 3. Stochastic, Statistical, Fractal, and

Chaos Analyses; and 4. Modeling, Simulation and Numerical Solution. The list of my

publications submitted in support of my nomination contains 5 books, 1 book chapter,

and 374 refereed journal articles in these areas. I am at the ripe old (?) or young (?) age

of 74, and am half-retired; however, I continue to work at about the same pace as when I

was a full-time faculty member of 40 years, chairing the department for exactly 30 years.

The title of my lecture reflects the sub-areas of two areas, specifically the first and

third, on which my collaborators and I have been focusing in recent years. The graph-

theoretic approach originally established by my collaborators and me for the optimal

synthesis of process networks has been extended eventually to the identification of

catalytic-reaction or metabolic pathways through mimicking their synthesis in nature.

Obviously, the process networks are macroscopic, and the catalytic-reaction or metabolic

pathways are microscopic, thus, the front part of the title. While our groups effort on

stochastic analysis and modeling was originally prompted by my interest in the motion of

molecular species and the reactions among them and while some meaningful results were

obtained, we came to the realization that it would be far more fruitful to concentrate oureffort mainly on the motion and behavior of gas bubbles, liquid droplets or solid particles

and the interactions among them. Clearly, the molecular species are microscopic, and the

gas bubbles, liquid droplets or solid particles are mesoscopic, thus, the back part of the


4/51


5/51

Meeting of minds: 1989 ~ 90

My plea for the aforementioned robust and highly efficient algorithm for process-

network synthesis was answered when I met Dr. F. Friedler at the North American-

German Workshop on Chemical Engineering Mathematics, held in Gttingen, Germany,

July 18 to 23 of 1989. I was there as a lecturer; and Dr. Friedler together with his wife,

Dr. K. Tarjan, a mathematician, were participants. At that time, Dr. Friedler was a young

researcher affiliated with The Institute of Technical Chemistry of the Hungarian

Academy of Sciences located in Veszprm; he was already a full-fledged mathematician-

computer scientist. According to Dr. Friedler, he closely followed my work on process

optimization and synthesis. Hence, he could feel my frustration in my inability to deal

with large-scale systems. Meanwhile, since he was neither a chemist nor a chemical

engineer, he himself had been struggling mightily to establish a formal framework for the

algorithm to execute optimal process-network synthesis in terms of a set of axioms

couched in the parlance of chemical, or material, transformation. His collaboration with

his colleagues at the Institute was not sufficiently fruitful; his attempt to initiate joint

research, also in this regard, with some of Europes leading process-systems engineering

groups came to naught. After Dr. Friedlers presentation at the Workshop and an ensuing

short discussion between us, we sensed each others need and immediately decided to

collaborate. Both he and his wife spent a year with me as research associates. The rest is

history - at least up to now.

First milestone: early 1990s

The first milestone of our journey toward the formalization and popularization ofP-graphs for process-network synthesis was reached sometime in the early 90s when

several memorable breakthroughs occurred. These breakthroughs included the

presentations of a series of papers at major national and international technical


6/51

Tarjan, Huang, and Fan (see Attachment 1). At that time, Prof. Rippin was one of the

two chief co-editors of Computers in Chemical Engineering. His most favorable and

encouraging comments on our manuscript were indeed gratifying; naturally, receiving

such comments made us euphoric.

At this juncture, a short discussion on graphs in general and P-graphs in particular

is in order. Graphs constitute a natural mathematical language or logical tool for

describing and representing networks. Examples of such networks are gas or oil

pipelines, waterways or irrigation channels, process flowsheets, highways, railroads,

telephone lines, family trees, social relationships, and organizational structures. Some of

these networks are physically visible, and some are not. The conventional graphs are

represented by nodes, o, and arcs, or . Monopartite and bipartite graphs are typical

conventional graphs; the former contains one kind of nodes, and the latter, two kinds of

nodes.

Then, what are P-graphs? They are unique bipartite graphs depicted in Figure 1.

Obviously, the question arises as to the rationale for proposing P-graphs or to their need.

At the very early stage of development, it was demonstrated unequivocally that the

conventional graphs, either monopartite or bipartite, are incapable of uniquely

representing process networks: they are not sufficiently rich syntactically and

semantically. Hence, a special class of bipartite graphs is sorely needed for this purpose.

In this regard, it is worth noting that in recent years we have witnessed a proliferation of

special classes of graphs in various fields, including call graphs, social graphs, and

highway graphs. According to Hayes (Graph Theory in Practice: Part I, American

Scientist, January-February, 2000), The next step is to develop a mathematical modelof the structure, which typically takes the form of an algorithm for generating graphs with

the same statistical properties. Such models of very large graphs will be the subject of

The P-graphs constitute one such special class of graphs.


7/51

Figure 1. P-graphs of some operating units and their concomitant material

streams:

(a) Materials A, B, and C, and operating unit ({A, B}, {C})

(b) Material C, D, and E, and operating unit ({C}, {D, E}).

A B

C

C

ED

(a) (b)


8/51

the mass-conservation law and stoichiomatric principle are two perfect statistical

properties shared by all the process networks or their graph representations; such

perfect statistics naturally leads to rigorous axioms. It is not difficult to imagine that an

algorithm or algorithms on the basis of such rigorous axioms would be robust.

Specifically, we have established five axioms, which in turn, have given rise to the three

algorithms, algorithm MSG for maximal-structure generation; algorithm SSG for solution

structure generation; and algorithm ABB for accelerated branch-and-bound for

generating the optimal and near optimal solutions. The maximal structure is the

rigorously defined super-structure without redundancy.

The profound robustness and efficacy of the three algorithms, MSG, SSG, and

ABB, have their roots in the rigorously, or exactly, stated set of five axioms.

Nevertheless, their extraordinarily computational efficiency is mainly, but not

exclusively, attributable to the drastic reduction in the search space resulting from the

construction of the maximal structure in polynomial steps with algorithm MSG. This is

illustrated in Figure 2, based on the optimal synthesis of an industrial-scale process

containing 35 operating units, which are functional units, performing material

transformation. This example appears in the first several publications of ours. Note that

(2n

1) networks are possibly generated from n operating units, the majority of which is,

almost always, combinatorially infeasible.

Second milestone: mid 1990s

We reached the second milestone of our journey around mid 90s when our P-

graph-based approach to process-network synthesis received favorable comments byProfessor Sargent in a report of the Center for Process Systems Engineering of Imperial

College. The report was kindly sent to me by Prof. Sargent. Our approach was also

endorsed by Prof. Sargent in the Rippin memorial issue of Computers Chem. Eng. (Vol.


9/51

Possible networks

3436 107

Combinatoriallyfeasible networks

3465

Optimalnetwork

1

Near-optimalnetworks

5

Inputn = 35

MSG

SSG

ABB

99.99999%reduction

in the search space

Figure 2. Reduction in the search space of (2n 1).


10/51

sensed that his favorable inclination to our approach was largely influenced by Prof.

Rippin whose name is prominently mentioned in connection with the first milestone.

Third milestone: mid 1990s ~ late 90s

The third milestone of our journey was reached sometime between mid and late

90s. Around that time, we came to the realization that P-graphs can be potentially

adapted for or extended to the synthesis of mesoscopic processes and the identification of

molecular networks by mimicking their syntheses in nature. The former led to the

publication of a series of papers on azeotropic distillation, the first of which is the journal

article entitled Identifying Operating Units for the Design and Synthesis of Azeotropic-

Distillation Systems, by Feng, Fan, Friedler, and Seib, appearing in Industrial

Engineering Chemistry Research in year 2000 (see Attachment 1). The latter led to the

preparation of a series of papers on the identification of catalytic-reaction and metabolic

pathways, of which the combinatorial complexity of their syntheses is (3n 1) instead of

(2n 1). The first of the papers in this series entitled, Combinatorial Framework for the

Systematic Generation of Reaction Pathways, was presented at the AIChE Annual

Meeting held in Dallas, Texas in 1999, following which was the publication of three

journal articles (see Attachment 1).

Fourth milestone: late 1990s ~ early 2000

The fourth milestone of our journey was reached essentially around year 2000.

The most noteworthy event during this period was the endorsement of our P-graph-based

approach by Dr. George Keller in his Institute Lecture at the 1999 AIChE AnnualMeeting. Subsequently, the major portion of his Lecture was published in CEP (Volume

96, No. 1, 2000). Quoting directly from the published article, the P-graph may be the

fastest computationally, as well as the method most likely to find a truly optimal


11/51

review any of these books and articles prior to their publication. As a matter of fact, we

took it as another sign of the acceptance of P-graphs.

Fifth milestone: early 2000

The fifth milestone of our journey was reached in early 2000 when Prof. Klaus

Timmerhaus identified our P-graph-based approach as the method of choice for

algorithmic flowsheeting. This has resulted in the inclusion of a 30-page section entitled,

ALGORITHMIC FLOWSHEET GENERATION, in the 5-th edition of Plant Design

and Economics for Chemical Engineers, by Peters, Timmerhaus, and West. This

premier textbook on plant design is published by McGraw-Hill.

Around this time, we published a landmark paper in Volume 24 of Computers in

Chemical Engineering. This paper, co-authored by Brendel, Friedler and Fan, provides

additional proof of the rigorousness and superiority of our P-graph-based algorithmic

method for process synthesis over other algorithmic methods for process synthesis that

initiate the procedure with the construction of the super-structure as is the case with our

method (see Attachment 1).

Current status and future prospect of P-graphs

Now the point is reached for me to reveal what is transpiring currently and what

will transpire in the near future regarding our work on P-graphs. These include: optimal

syntheses of various downstream processing systems for biochemical production of

chemicals from grains and other natural resources; complex azeotropic-distillation system

synthesis; design of alternative synthetic routes; profit-potential estimation; separation-network synthesis incorporating separators effected by different separation methods;

heat-integrated separation-network synthesis; identification of catalytic-reaction

mechanisms; and metabolic-pathway identification and metabolic-flux analysis, which


12/51

Given in Attachment 3 to illustrate our current and future works related to P-

graphs are the abbreviated or condensed view graphs of the 3 papers that are to be

presented at this 2003 Annual Meeting of AIChE. These illustrations are arranged in the

order of presentation.

From Microscopic World to Mesoscopic World through Drunkards Paths

Drunkards paths are often used in popular expositions of random walks that

probably belong to the simplest class of stochastic processes. Collectively, stochastic

processes constitute a rigorous branch of mathematics or mathematical statistics. It is

concerned with random phenomena occurring over time or space according to a certain

mathematical property defined by a distribution of the random variable. What

distinguishes any stochastic model from the corresponding deterministic or continuum

model is its capability to represent rigorously not only the gross, or mean, behavior of the

phenomenon or process of interest but also its inherent fluctuations. This capability of

revealing inherent or characteristic fluctuations is absent in the deterministic or

continuum model.

This section is structured similar to the preceding section. The headings of

subsections of the preceding section are mainly in terms of milestones. In contrast, those

in this section are mainly in terms of major personalities whose contributions inspired us

or guided our work during my journey.

Awakening at dawn: 1950sSince my undergraduate days, I have been a student of the analysis, design,

fabrication and operation of continuous flow chemical reactors in either the tubular or

stirred-vessel configuration. It was not difficult for me, like everyone else, to experience


13/51

end of the 50s. A large part of the contributions of my collaborators and me is

contained in the well-received monograph published by Marcel-Dekker in 1966, Models

for Flow Systems and Chemical Reactors, written with my closest friend and classmate,

the late C. Y. Wen.

The molecules flowing through a reactor are microscopic and discrete entities;

they move or behave independently and randomly. It is not surprising, therefore, that

qualitative discourses of residence-time distributions in the publications, including my

own, are full of statistical, or stochastic, jargon; yet, the quantitative treatments of

residence-time distributions are entirely deterministic involving much hand-waving

arguments. For me, this was and has been for a long time intellectually untenable. I

knew deep down that the residence-time distribution could be rigorously treated based on

statistics or stochastic processes. This was very clear to me even in the late 50s: I

became aware that the frequently-used alternative name to the residence-time distribution

is the age distribution belonging to the parlance of actuaries, practitioners of biostatics or

stochastic processes.

During the second half of the 50s, my participation in the various process

operations related to solid particles, liquid droplets or gas bubbles noticeably quickened.

To me, those mesoscopic discrete entities almost always randomly dance in process

vessels. The majority, if not all, of the mathematical models of processes involving these

entities that were available then, however, were deterministic in nature. I attempted to

remedy this situation by emulating the methods of statistical mechanics without much

success. Eventually, I reached the conclusion that the methods of stochastic processes

would be most appropriate because of the time-dependency of these processes.

Encounter with Markov: mid 1960s ~ early 1970s

During this period, some of my graduate students, research associates and I self-


14/51

leads to the well-known Fokker-Planck equations. All these Markov processes invoke

the renowned Markov assumption. This assumption contends that the probability of

occurrence of the event of interest at present depends only on its occurrence in the

preceding time period. In other words,

Pr[X(tn) xn| X(t1) = x1, X(t2) = x2,, X(tn-1)= xn-1]

= Pr[X(tn) xn| X(tn-1) = xn-1]

We were fairly successful in stochastically modeling a number of processes or

phenomena involving discrete microscopic or mesoscopic entities by invoking the

Markov assumption. On the other hand, we were somewhat disappointed that all the

methods or techniques of stochastic processes we learned from the aforementioned

classical textbooks were applicable only to linear processes. Yet, many of the significant

problems requiring stochastic treatment but remaining unsolved were nonlinear in nature.

Encounter with Prigogine: early 1980s

I have been attracted to the papers by Prigogine, who recently passed away, since

the mid 60s because of my interest in Non-equilibrium Thermodynamics. In fact, my

student and I contributed a short article on the subject to IEC Research although it was

concerned only with the linear version of Non-equilibrium Thermodynamics by Onsager.

I found almost all of the monographs and papers written by Prigogine to be extremely

difficult to tackle. It was indeed discouraging; I attributed it to my mental incapability.

It was a relieve when a short story about Prigogine in a reputable publication

caught my eye one day while I was wasting my time browsing randomly through

books and journals in the university library. The gist of one passage said that bookcompanies requested Prigogine to write monographs only with his students or research

associates who suffered from his notoriously convoluted writings and thus became

proficient in interpreting or delineating them. One such monograph is, Self-organization


15/51

in the non-linear master equations that are solvable. Later, the contribution of Prigogine

earned him a Nobel Prize in Chemistry. Nevertheless, I could never master the

solution procedure adopted by him. Despite this dilemma, I was encouraged: Anything

good enough for Prigogine is good enough for me.

Encounter with van Kampen: mid 1980s

My dilemma vanished when we discovered monographs and journal articles by

one of the leading theoretical physicists, van Kampen. The most important among his

contributions is a monograph, Stochastic Processes in Physics and Chemistry,

published in 1982. I consider this monograph, without question, as the bible of non-

linear master equations, probability-balance equations or gain-loss equations derived

from the birth-death processes. The complexity arising from the non-linearity is

circumvented by a rational approximate method, i.e., system-size expansion.

Mastering the master-equation approach immeasurably enhanced our groups

productivity. It has led to the publication of series of papers, each ranging from two to

more than a dozen, mostly dealing with mesoscopic entities or systems on various

subjects such as chemically reacting systems, solids mixing, grinding, fluidization,

crystallization, filtration, biochemical processes, interphase mass transport, andresidence-time distribution. These papers are listed in Attachment 2. Naturally, we

called on the system-size expansion whenever necessary to deal with non-linearity.

Among the publications, I would like to single out the following two articles pertaining to

the last-mentioned subject, residence-time distribution.

The Surface-Renewal Theory of Interphase Transport: A Stochastic Treatment,

Chem. Eng. Sci., 48, 3971-3982 (1993), by Fan, Shen, and Chou.


16/51

The first of the two is concerned with the rigorous, non-handwaving treatment of the

celebrated interphase transport theory of Danckwerts. The residence-time distribution at

the interface comes into play in this theory.

The readers might recall that I alluded to my frustration with the deterministic or

continuum treatment of residence-time distribution, which ultimately motivated me to

start my journey through Drunkards Paths charted by the rigorous mathematics of

Stochastic Processes. I am firmly committed to continue my work in stochastic analysis

and modeling. To follow a drunkards path would be much more enjoyable than to walk

through a rigid path.

Current status and future prospect of stochastic analysis and modeling

To illustrate what our group is currently doing, one of our recent papers (see

Attachment 2) and a paper to be presented at this 2003 Annual Meeting of AIChE are

listed below.

Stochastic Modeling of Thermal Death Kinetics of a Cell Population: Revisited.

Stochastic Modeling and Simulation of the Formation of Carbon MolecularSieves by Carbon Deposition.

The abbreviated or condensed viewgraphs of these papers are given in Attachment 4.

The papers are based on linear master equations; they are being actively extended to

various non-linear cases. Moreover, we are preparing monographs on stochastic analysis

and modeling of particulate systems, chemically reacting systems, and biochemical

systems.

The future of our work will entail the extension to such subjects as nanoparticle


17/51

Concluding Remarks

At the outset, I unabashedly declared my age, namely, 74. Hence, the most

appropriate concluding remark would be, Old professors never die; they just

(asymptotically) fade away. Obviously, I borrowed heavily from General Douglas

MacArthur.

Acknowledgements

I would like to express my profound appreciation to all my current and former

students, assistants, associates, collaborators, and teachers; all my current and former

colleagues and staff in the department; all organizations and agencies in and out of the

University that supported my research; all attendants who were bewildered by my

entangled and random talks; and last, but not least, all my family members,

especially my wife, Eva, who has accompanied me for 45 years in the journey through

the Mazes of Process Graphs and Drunkards Paths.


18/51

Attachment 1

List of Publications on P-graphsby

L. T. Fan and Collaborators

NOTE: Two categories of reference listings are provided:

(1) Refereed journal articles

(2) Non-refereed articles

In (1) and (2), the individual articles are listed from the most recent (2003) to the

oldest (1992 and 1991, respectively).


19/51

Refereed journal articles

32 Holenda, B., A. Dallos, A. B. Nagy, F. Friedler, and L. T. Fan, A Combinatorial

Approach for Generating Environmentally Benign Solvents and Separation Agents,

Chemical Engineering Transactions, 3, S871-S876 (2003).

31 Heckl, I., Z. Kovacs, F. Friedler, and L. T. Fan, Super-structure Generation forSeparation Network Synthesis Involving Different Separation Methods, Chemical

Engineering Transactions, 3, S1209-S1214 (2003).

30 Novaki, S., B. Bertok, F. Friedler, L. T. Fan, and G. Feng, Rigorous Algorithm forSynthesizing Azeotropic-Distillation Systems, Chemical Engineering Transactions, 3,S1123-S1127 (2003).

29 Sarkozi, N., B. Bertok, F. Friedler, and L. T. Fan, Software Tool for Formulating andSolving Various Process-Synthesis Problems, Chemical Engineering Transactions, 3,

S1203-S1208 (2003).

28 Feng, G., L. T. Fan, P. A. Seib, B. Bertok, L. Kalotai, and F. Friedler, A Graph-

Theoretic Method for the Algorithmic Synthesis of Azeotropic-Distillation Systems,Ind. Eng. Chem. Res., 42, 3602-3611 (2003).

27 Halasz, L., A. B. Nagy, T. Ivicz, F. Friedler, L. T. Fan, Optimal Retrofit Design and

Operation of the Steam-Supply System of a Chemical Complex, Applied Thermal

Engineering 22, 939 -947 (2002).

26 Fan, L. T., B. Bertok, and F. Friedler, A Graph-Theoretic Method to IdentifyCandidate Mechanisms for Deriving the Rate Law of a Catalytic Reaction, Computers

and Chemistry, 26, 265-292 (2002).25 Fan, L. T., B. Bertok, F. Friedler, and S. Shafie, Mechanisms of Ammonia-Synthesis

Reaction Revisited with the Aid of a Novel Graph-Theoretic Method for DeterminingCandidate Mechanisms in Deriving the Rate Law of a Catalytic Reaction, Hungarian

Journal of Industrial Chemistry, 29, 71-80 (2001).

24 Seo, H., D.-Y. Lee, S. Park, L. T. Fan, S. Shafie, B. Bertok, and F. Friedler, Graph-

Theoretical Identification of Pathways for Biochemical Reactions, Biotechnology

Letters, 23, 1551-1557 (2001).

23 Bertok, B., F. Friedler, G. Feng, and L.T. Fan, Systematic Generation of the Optimal

and Alternative Flowsheets for Azeotropic Distillation Systems, Computer-Aided

Chemical Engineering, 9, S351-S356 (2001).

22 Nagy, A. B., R. Adonyi, L. Halasz, F. Friedler, and L. T. Fan, Integrated Synthesis of


20/51

20 Kovacs, Z., Z. Ercsey, F. Friedler, and L. T. Fan, Separation-Network Synthesis:

Global Optimum through Rigorous Super-Structure, Computers Chem. Engng, 24,

1881-1900 (2000).19 Brendel, M. H., F. Friedler, and L. T. Fan, Combinatorial Foundation for Logical

Formulation in Process Network Synthesis, Computers Chem. Engng, 24, 1859-1864

(2000).

18 Feng, G., L. T. Fan, and F. Friedler, Synthesizing Alternative Sequences via a P-Graph-Based Approach in Azeotropic Distillation Systems, Waste Management, 20,

639-643 (2000).

17 Kovacs, Z., Z. Ercsey, F. Friedler, and L. T. Fan, Exact Super-Structure for theSynthesis of Separation-Networks with Multiple Feed-Streams and Sharp Separators,Computers Chem. Engng, 23, S1007-1010 (1999).

16 Kalotai L., Dallos A., Friedler F., L. T. Fan, Kombinatorikus modszer kivant

tulajdonsagu molekulak tervezesehez (in Hungarian), Magyar Kemikusok Lapja, 54,

173-181 (1999).

15 Kovacs, Z., Z. Ercsey, F. Friedler, and L. T. Fan, Redundancy in a Separation-

Network, Hungarian Journal of Industrial Chemistry, 26, 213-219 (1998).

14 Friedler, F., L. T. Fan, L. Kalotai, and A. Dallos, A Combinatorial Approach for

Generating Candidate Molecules with Desired Properties Based on Group

Contribution, Computers Chem. Engng, 22, 809-817 (1998).

13 Friedler, F., L. T. Fan, and B. Imreh, Process Network Synthesis: Problem Definition,Networks, 28, 119-124 (1998).

12 Imreh, B., F. Friedler, and L. T. Fan, An Algorithm for Improving the BoundingProcedure in Solving Process Network Synthesis by a Branch-and-Bound Method,

Nonconvex Optimization and Its Applications, Developments in Global Optimization(Eds: I. M. Bomze, T. Csendes, R. Horst, and P. M. Pardalos), pp. 315-348, KluwerAcademic Publishers, Dordrecht, 1997.

11 Friedler, F., J. B. Varga, E. Feher, and L. T. Fan, Combinatorially Accelerated

Branch-and-Bound Method for Solving the MIP Model of Process Network

Synthesis, Nonconvex Optimization and Its Applications, State of the Art in GlobalOptimization, Computational Methods and Applications (Eds: C. A. Floudas and P.

M. Pardalos), pp. 609-626, Kluwer Academic Publishers, Dordrecht, 1996.

10 Friedler, F., J. B. Varga, and L. T. Fan, Decision-Mapping: A Tool for Consistent and

Complete Decisions in Process Synthesis, Chem. Engng Sci., 50, 1755-1768 (1995).

9 V J B F F i dl d L T F P ll li ti f th A l t d B h d


21/51

7 Kovacs, Z., F. Friedler, and L. T. Fan, Parametric Study of Separation Network

Synthesis: Extreme Properties of Optimal Structures, Computers Chem. Engng, 19,

S107-112 (1995).6 Friedler, F., J. B. Varga, and L. T. Fan, Decision-Mapping for Design and Synthesis

of Chemical Processes: Application to Reactor-Network Synthesis, AIChE

Symposium Series (Eds: L. T. Biegler and M. F. Doherty), 91, 246-250 (1995).

5 Friedler, F., J. B. Varga, and L. T. Fan, Algorithmic Approach to the Integration ofTotal Flowsheet Synthesis and Waste Minimization, AIChE Symposium Series,

Volume on Pollution Prevention via Process and Product Modifications (Eds: M. M.

El-Halwagi and D. P. Petrides), 90, 86-97 (1995).4 Friedler, F., K. Tarjan, Y. W. Huang, and L. T. Fan, Graph-Theoretic Approach to

Process Synthesis: Polynomial Algorithm for Maximal Structure Generation,

Computers Chem. Engng, 17, 929-942 (1993).

3 Kovacs, Z., F. Friedler, and L. T. Fan, Recycling in a Separation Process Structure,

AIChE J., 39, 1087-1089 (1993).

2 Friedler, F., K. Tarjan, Y. W. Huang, and L. T. Fan, Graph-Theoretic Approach to

Process Synthesis: Axioms and Theorems, Chem. Engng Sci., 47, 1973-1988 (1992).

1 Friedler, F., K. Tarjan, Y. W. Huang, and L. T. Fan, Combinatorial Algorithms for

Process Synthesis, Computers Chem. Engng, 16, S313-320 (1992).


22/51

Non-refereed articles

88 Fan, L. T., S. Shafie, S. Khaitan, A. More, B. Bertok, and F. Friedler, AlgorithmicIdentification of Mechanisms of the Ethylene Hydrogenation Reaction, accepted for

presentation at the AIChE Annual Meeting, San Francisco, CA, U.S.A., November

16-21, 2003.

87 Lee, D.-Y., L. T. Fan, S. Park, S. Y. Lee, S. Shafie, B. Bertok, and F. Friedler,Synergistic Identification of Multiple Flux Distributions and Multiple Metabolic

Pathways, accepted for presentation at the AIChE Annual Meeting, San Francisco,

CA, U.S.A., November 16-21, 2003.

86 Liu, J., L. T. Fan, P. Seib, F. Friedler, and B. Bertok, Feasible and OptimalFlowsheets for Downstream Processing in Biochemical Production of Butanol,

Ethanol, and Acetone: Inclusion of Pervaporation, accepted for presentation at the

AIChE Annual Meeting, San Francisco, CA, U.S.A., November 16-21, 2003.

85 Heckl, I., Z. Kovacs, F. Friedler, and L. T. Fan, Super-Structure Generation forSeparation-Network Synthesis Involving Different Separation Methods, presented at

ICheaP-6, Pisa, Italy, June 8-11, 2003.

84 Novaki, S., B. Bertok, F. Friedler, L. T. Fan, and G. Feng, Rigorous Algorithm for

Synthesizing Azeotropic-Distillation Systems, presented at ICheaP-6, Pisa, Italy, June8-11, 2003.

83 Sarkozi, N., B. Bertok, F. Friedler, and L. T. Fan, Software Tool for Formulating and

Solving Various Process-Synthesis Problems, presented at ICheaP-6, Pisa, Italy, June

8-11, 2003.

82 Holenda, B., A. B. Nagy, A. Dallos, F. Friedler, and L. T. Fan, A CombinatorialApproach for Generating Environmentally Benign Solvents and Separation Agents,

presented at ICheaP-6, Pisa, Italy, June 8-11, 2003.

81 Seo, H., S. Park, L. T. Fan, S. Shafie, B. Bertok, and F. Friedler, Algorithmic

Identification of Stoichiometrically Exact, Plausible Mechanisms of the CatalyticCombustion of Hydrogen on Platinum, presented at the AIChE Annual Meeting,

Indianapolis, IN, U.S.A., November 3-8, 2002.

80 Seo, H., S. Park, L. T. Fan, S. Shafie, B. Bertok, and F. Friedler, Graph-TheoreticIdentification of Stoichiometrically Exact Mechanisms of the Catalytic Reduction ofNitrogen Dioxide (NO2) by Carbon Monoxide (CO) on Platinum Catalysts, presented

at the AIChE Annual Meeting, Indianapolis, IN, U.S.A., November 3-8, 2002.

79 Shafie, S., L. T. Fan, B. Bertok, F. Friedler, H. Seo, and S. Park, Rapid Algorithmic

i i f i hi i ll h i f h l i i


23/51

78 Novaki, S., B. Bertok, F. Friedler, and L. T. Fan, Algorithmic Synthesis of an

Azeotropic-Distillation System: Production of Pure Ethanol Revisited, presented atthe PRES'02 (5th Conference on Process Integration, Modelling and Optimisation for

Energy Saving and Pollution Reduction), Praha, Czech Republic, August 25-29,

2002.

77 Ercsey, Z., Z. Kovacs, F. Friedler, and L. T. Fan, Super-Structure in Action, presentedat the PRES'02 (5th Conference on Process Integration, Modelling and Optimisation

for Energy Saving and Pollution Reduction), Praha, Czech Republic, August 25-29,

2002.

76 Fan, L. T., J. Liu, F. Friedler, and B. Bertok, Identification of Alternative ReactionPaths via Graph-theoretic Synthesis of Reaction Networks, presented at the AIChE

Annual Meeting, Indianapolis, IN, U.S.A., November 3-8, 2002.

75 Fan, L. T., J. Liu, F. Friedler, and B. Bertok, Algorithmic Profit-potential Estimation

for Developing Green Processes, presented at the AIChE Annual Meeting,Indianapolis, IN, U.S.A., November 3-8, 2002.

74 Bertok, B., F. Friedler, G. Feng, and L.T. Fan, Systematic Generation of the Optimal

and Alternative Flowsheets for Azeotropic Distillation Systems, presented atESCAPE-11 (Eleventh European Symposium on Computer Aided ProcessEngineering), Kolding, Denmark, May 27-30, 2001.

73 Seo, H., D.-Y. Lee, S. Park, L. T. Fan, S. Shafie, B. Bertok, and F. Friedler, Graph-

Theoretical Identification of Pathways for Biochemical Reactions, presented at

PRES01(the 4th Conference on Process Integration, Modelling and Optimisation forEnergy Saving and Pollution Reduction), Florence, Italy, May 20-23, 2001.

72 Halasz, L., A. B. Nagy, T. Ivicz, F. Friedler, and L. T. Fan, Optimal Operation of theSteam Supply Network of a Complex Chemical Processing System, appeared in the

proceedings of the PRES'01 (4th Conference on Process Integration, Modelling andOptimisation for Energy Saving and Pollution Reduction), Florence, Italy, May 20 -

May 23, 2001, pp.331-336

71 Seo, H., S. Park, L. T. Fan, S. Shafie, B. Bertok, and F. Friedler, An Attempt for theIdentification of Stoichiometrically Exact Mechanisms of the Catalytic Reduction of

Nitrogen Dioxide (NO2) by Carbon Monoxide (CO) on Platinum Catalysts, presented

at the KIChE 2001 Spring Meeting, Seoul, South Korea, April 27-28, 2001.

70 Seo, H., S. Park, L. T. Fan, S. Shafie, B. Bertok, and F. Friedler, Mechanism ofCatalytic Combustion of Hydrogen, presented at PSE Asia 2000 (International

Symposium on Design, Operation and Control of Next Generation Chemical Plants),

Kyoto Japan December 6-8 2000


24/51

68 Friedler, F., B. Bertok, L. T. Fan, and S. Shafie, Detailed Mechanism of Ammonia-

Synthesis Reaction, presented at the AIChE Annual Meeting, Los Angeles, CA,U.S.A., November 12-17, 2000.

67 Bertok, B., F. Friedler, G. Feng, and L. T. Fan, Combinatorial Framework for the

Algorithmic Synthesis of Azeotropic-Distillation Systems, presented at the AIChE

Annual Meeting, Los Angeles, CA, U.S.A., November 12-17, 2000.

66 Seo, H., S. Park, L. T. Fan, S. Shafie, B. Bertok, and F. Friedler, An Attempt for theIdentification of Stoichiometrically Exact Mechanisms of the Reaction between H2

and O2 on Platinum Catalysts, presented at the KIChE fall meeting, Pohang, South

Korea, October 20-21, 2000.65 Bertok, B., G. Feng, L. T. Fan, and F. Friedler, Combinatorial Framework for the

Algorithmic Synthesis of Azeotropic-Distillation Systems, presented at the CHISA

2000 (14th International Congress of Chemical and Process Engineering), Praha,

Czech Republic, August 27-31, 2000.

64 Bertok, B., R. Adonyi, G. Feng, L. T. Fan, and F. Friedler, Systematic Synthesis of anAzeotropic-Distillation System for Production of Pure Ethanol from its Aqueous

Solution with Toluene as the Entrainer, presented at the CHISA 2000 (14thInternational Congress of Chemical and Process Engineering), Praha, CzechRepublic, August 27-31, 2000.

63 Nagy, A. B., G. Biros, F. Friedler, and L. T. Fan, Integrated Synthesis of Combined

Process and Heat Exchanger Networks, presented at the CHISA 2000 (14th

International Congress of Chemical and Process Engineering), Praha, CzechRepublic, August 27-31, 2000.

62 Nagy, A. B., F. Friedler, and L. T. Fan, Integrated Synthesis of Combined Processand Heat Exchanger Networks, presented at the 20th Workshop on Chemical

Engineering Mathematics, Veszprem, Hungary, July 26-29, 2000.

61 Feng, G., L. T. Fan, B. Bertok, L. Kalotai, and F. Friedler, A Graph-Theoretic

Approach to the Algorithmic Synthesis of Azeotropic-Distillation Systems, presented

at the AIChE Spring National Meeting, Atlanta, GA, U.S.A., March 5-9, 2000.

60 Fan, L. T., B. Bertok, and F. Friedler, Combinatorial Framework for the Systematic

Generation of Reaction Pathways, presented at the AIChE Annual Meeting, Dallas,TX, U.S.A., October 31 - November 5, 1999.

59 Kovacs, Z., Z. Ercsey, F. Friedler, and L. T. Fan, Exact Super-Structure for the

Synthesis of Separation-Networks with Multiple Feed-Streams and Sharp Separators,presented at the ESCAPE-9 (Ninth European Symposium on Computer Aided

i i ) d 31 2 1999


25/51

57 Feng, G., L. T. Fan, and F. Friedler, Synthesizing Alternative Sequences via a P-

Graph Based Approach in Azeotropic Destillation Systems, presented at the PRES'99(Second Conference on Process Integration, Modelling and Optimisation for Energy

Saving and Pollution Reduction), Budapest, Hungary, May 31 - June 2, 1999.

56 Nagy, A., L. Halasz, F. Friedler, and L. T. Fan, Integrated Synthesis of Process and

Heat Exchanger Networks: Algorithmic Approach, presented at the PRES'99 (2ndConference on Process Integration, Modelling and Optimisation for Energy Saving

and Pollution Reduction, Budapest, Hungary, May 31 - June 2, 1999.

55 Ercsey, Z., Z. Kovacs, F. Friedler, and L. T. Fan, Separation-Network Synthesis:Global Optimum through Rigorous Super-Structure, presented at the AIChE AnnualMeeting, Miami Beach, FL, U.S.A., November 15-20, 1998.

54 Nagy, A., F. Friedler, and L. T. Fan, Combinatorial Acceleration of Separable

Concave Programming for Process Synthesis, presented at the AIChE Annual

Meeting, Miami Beach, FL, U.S.A., November 15-20, 1998.

53 Nagy, A., F. Friedler, and L. T. Fan, Algorithmic Generation of Multiple Solutions

for Process Synthesis, presented at the CHISA '98 (13th International Congress ofChemical and Process Engineering), Praha, Czech Republic, August 23-28, 1998.

52 Kalotai, L., A. Dallos, F. Friedler, and L. T Fan, Design of Environmentally Benign

Chemical Species, presented at the CHISA '98 (13th International Congress ofChemical and Process Engineering), Praha, Czech Republic, August 23-28, 1998.

51 Holczinger, T., F. Friedler, and L. T. Fan, Process Synthesis for Retrofit Design,

presented at the CHISA '98 (13th International Congress of Chemical and Process

Engineering), Praha, Czech Republic, August 23-28, 1998.

50 Bertok, B., F. Friedler, and L. T. Fan, Random Generation of Test Problems forProcess Synthesis, presented at the CHISA '98 (13th International Congress ofChemical and Process Engineering), Praha, Czech Republic, August 23-28, 1998.

49 Fan, L. T. and F. Friedler, Reaction Pathway Analysis by a Network Synthesis

Technique, presented in the Session on Reaction Path Analysis, AIChE Annual

Meeting, Los Angeles, CA, U.S.A., November 16-21, 1997.

48 Friedler, F., J. B. Varga, P. Hobor, and L. T. Fan, Combinatorial Approach to ProcessSynthesis: Sectionally Continuous Cost Functions of Operating Units, presented in

the Session on Process Synthesis, AIChE Annual Meeting, Los Angeles, CA, U.S.A.,

November 16-21, 1997.

47 F G F F i dl d L T F G ti Alt ti S i P G h


26/51

46 Imreh, B., F. Friedler, and L. T. Fan, Effective Branch-and-Bound Strategy for

Process Network Synthesis, presented at the INFORMS (Institute for OperationsResearch and the Management Sciences) Atlanta Fall 1996 Conference, Atlanta, GA,

U.S.A., November 3-6, 1996.

45 Feher, E., F. Friedler, and L. T. Fan, Accelerated Branch-and-Bound Method for

Solving Process Network Synthesis Problems with Sectionally Continuous CostFunctions, presented at the XIII. International Conference on Mathematical

Programming, Matrahza, Hungary, March 24-27, 1996.

44 Friedler, F., B. Imreh, and L. T. Fan, Satisfiability for the Structural Model of Process

Network Synthesis, presented at the DIMACS (Center for Mathematics andTheoretical Computer Sciences) Workshop on Satisfiability Problem: Theory and

Applications, Rutgers University, New Brunswick, NJ, U.S.A., March 11-13, 1996.

43 Kovacs, Z., F. Friedler, and L. T. Fan, Algorithmic Generation of the Mathematical

Programming Model for Process Network Synthesis, presented at the IIIrd Workshopon Global Optimization, Szeged, Hungary, December 10-14, 1995, p. 60.

42 Imreh, B., F. Friedler, and L. T. Fan, Polynomial Algorithm for Improving the

Bounding Procedure in Solving Process Network Synthesis by a Branch and BoundMethod, presented at the IIIrd Workshop on Global Optimization, Szeged, Hungary,December 10-14, 1995, p. 27.

41 Kovacs, Z., F. Friedler, and L. T. Fan, Algorithmic Generation of the Mathematical

Programming Model for Process Synthesis, presented in the Session on

Computational Approaches in Systems Engineering, AIChE Annual Meeting, MiamiBeach, FL, U.S.A., November 12-17, 1995.

40 Friedler, F. and L. T. Fan, Combinatorial Foundation of Process Synthesis, PlenaryLecture at the 7th International Summer School of Chemical Engineering, Varna,

Bulgaria, September 19-25, 1995.

39 Varga, J. B., F. Friedler, and L. T. Fan, Parallelization of the Accelerated Branch and

Bound Algorithm of Process Synthesis: Application in Total Flowsheet Synthesis,

presented at the ESCAPE-5 (Fifth European Symposium on Computer Aided ProcessEngineering), Bled, Slovenia, June 11-14, 1995.

38 Hangos, K. M., J. B. Varga, F. Friedler, and L. T. Fan, Integrated Synthesis of aProcess and its Fault-Tolerant Control System, presented at the ESCAPE-5 (Fifth

European Symposium on Computer Aided Process Engineering), Bled, Slovenia,June 11-14, 1995.

37 Kovacs, Z., F. Friedler, and L. T. Fan, Parametric Study of Separation Network

S h i i f O i l S d h SCA


27/51

Conference on Hazardous Waste Research, Kansas State University, Manhattan, KS,

U.S.A., May 23-24, 1995.

35 Friedler, F., J. B. Varga, E. Feher, and L. T. Fan, Combinatorially AcceleratedBranch-and-Bound Method for Solving the MIP Model of Process Network

Synthesis, presented at the International Conference on State of the Art in Global

Optimization: Computational Methods and Applications, Princeton University,Princeton, NJ, U.S.A., April 28-30, 1995.

34 Varga, J. B., F. Friedler, and L. T. Fan, Risk Reduction through Waste Minimizing

Process Synthesis, presented at the 21st Annual RREL (Risk Reduction Engineering

Laboratory) Research Symposium, Cincinnati, OH, U.S.A., April 4-6, 1995; alsopublished in the Proceedings of the Symposium, 359-363.

33 Kovacs, Z., F. Friedler, and L. T. Fan, A New Formulation for Solving Separation

Network Synthesis Problems of Multiple Feed-Streams and Multicomponent Product

Streams, presented in the Session on Design and Analysis, AIChE National Meeting,Houston, TX, U.S.A., March 19-23, 1995.

32 Friedler, F., J. B. Varga, and L. T. Fan, Integration of Waste Treatment into Process

Synthesis, presented in the Session on Design and Analysis, AIChE NationalMeeting, Houston, TX, U.S.A., March 19-23, 1995.

31 Varga, J. B., F. Friedler, and L. T. Fan, Parallel Combinatorially Accelerated Branchand Bound Algorithm for Process Synthesis, presented in the Session on High

Performance Computing in Computer Process Design, AIChE Annual Meeting, San

Francisco, CA, U.S.A., November 13-18, 1994.

30 Friedler, F. and L. T. Fan, Algorithmic Generation of a Minimally Complex Mixed-

Integer Programming Model for Process Synthesis, presented in the Session onAdvances in Optimization, AIChE Annual Meeting, San Francisco, CA, U.S.A.,

November 13-18, 1994.

29 Friedler, F., J. B. Varga, and L. T. Fan, Algorithmic Approach to the Integration of

Total Flowsheet Synthesis and Waste Minimization, presented in the Session on

Pollution Prevention via Process and Product Modifications; Tools, Methodologies,Case Studies, AIChE Summer National Meeting, Denver, CO, U.S.A., August 14-17,

1994.

28 Friedler, F., J. B. Varga, and L. T. Fan, Decision-Mapping for Design and Synthesis

of Chemical Processes: Application to Reactor-Network Synthesis, presented at theFOCAPD '94 (Fourth International Conference on Foundations of Computer-Aided

Process Design), Snowmass Village, CO, U.S.A., July 10-14, 1994.

2 i dl d G h h i A h


28/51

26 Varga, J. B., F. Friedler, and L. T. Fan, Systematic Approach for Process Synthesis

Incorporating In-Plant Waste Treatment, presented at the Conference on HazardousWaste Remediation, Bozeman, MT, U.S.A., June 8-10, 1994.

25 Friedler, F. and L. T. Fan, Reduction of Subproblem Size for the Accelerated Branch

and Bound Algorithm of Process Network Synthesis, presented at the XII.

International Conference on Mathematical Programming, Matrafured, Hungary,January 22-27, 1994.

24 Kovacs, Z., F. Friedler, and L. T. Fan, Fundamental Properties of Optimal Separation

Networks, presented in the Session on Design and Analysis, AIChE Annual Meeting,

St. Louis, MO, U.S.A., November 7-12, 1993.

23 Varga, J., F. Friedler, and L. T. Fan, Combinatorial Technique for MultiperiodProcess Network Synthesis, presented in the Session on Network Synthesis at the 21st

Hungarian Conference on Operations Research, Szeged, Hungary, October 2-4, 1993.

22 Kovacs, Z., F. Friedler, and L. T. Fan, Algorithmic Generation of the NLP Model for

Separation Network Synthesis, presented in the Session on Network Synthesis at the21st Hungarian Conference on Operations Research, Szeged, Hungary, October 2-4,

1993.21 Friedler, F., L. T. Fan, and B. Imreh, Maximal Structure Generation in Process

Network Synthesis, presented in the Session on Network Synthesis at the 21stHungarian Conference on Operations Research, Szeged, Hungary, October 2-4, 1993.

20 Friedler, F. and L. T. Fan, Reduction of Subproblem Size for the Combinatorially

Accelerated Branch and Bound Algorithm of Process Network Synthesis, presented

in the Session on Network Synthesis at the 21st Hungarian Conference on Operations

Research, Szeged, Hungary, October 2-4, 1993.19 Friedler, F. and L. T. Fan, Combinatorial Technique for the Design and Synthesis of

Large Scale Manufacturing Systems, presented in the Session on the Large ScaleSystems, Methodology and Applications at the IFAC '93 (12th World Congress,

International Federation of Automatic Control), Sydney, Australia, July 19-23, 1993;

also published in the Proceedings of the Congress, 10, 397-400.

18 Friedler, F., J. Varga, and L. T. Fan, A Combinatorial Approach to the Multiperiod

Synthesis of the Structure of Process Industries, presented at the ESCAPE-3(European Symposium on Computer Aided Process Engineering), Graz, Austria, July

5-7, 1993; also published in the Proceedings of the Symposium, 2-6 (1993).

17 Kovacs, Z., F. Friedler, and L. T. Fan, Algorithmic Generation of the MathematicalModel for Separation Network Synthesis, presented at the ESCAPE-3 (European

S i C Aid d i i ) G A i l 1993


29/51

15 Friedler, F., J. Varga, and L. T. Fan, A Combinatorial Technique for Determining the

Optimal Development Strategy for the Chemical and Petroleum Industries, presentedin the Session on Industrial Applications of CAD at the AIChE National Meeting,

Houston, TX, U.S.A., March 28-April 1, 1993.

14 Friedler, F. and L. T. Fan, Reduction of Subproblem Size for the Accelerated Branch

and Bound Method of Process Synthesis, presented in the Session on New Advancesin Process Synthesis and Analysis at the AIChE National Meeting, Houston, TX,

U.S.A., March 28-April 1, 1993.

13 Friedler, F. and L. T. Fan, Combinatorial Acceleration of the Branch and Bound

Search for Process Network Synthesis, presented in the Session on ComputationalMethods for Linear and Mixed-Integer Programming at the Symposium on Applied

Mathematical Programming and Modeling, Budapest, Hungary, January 6-8, 1993;

also published in the Proceedings of the Symposium, 192-200.

12 Friedler, F., K. Tarjan, Y. W. Huang, and L. T. Fan, Combinatorial Technique forProcess Design and Synthesis, presented in the Session on Design & Analysis at the

AIChE Annual Meeting, Miami, FL, U.S.A., November 1-6, 1992.

11 Friedler, F., K. Tarjan, Y. W. Huang, and L. T. Fan, Combinatorial Foundation ofProcess Synthesis, presented in the Session on Mathematical Applications inChemical Engineering at the 42nd CSChE (Canadian Society for Chemical

Engineering) Annual Conference, Toronto, Canada, October 18-21, 1992; also

published in the Proceedings of the Conference, 207-208.

10 Friedler, F. and L. T. Fan, Process Synthesis Incorporating In-Plant Waste Treatment:Algorithmic Approach, presented in the Session on Systematic Design Approaches to

Hazardous-Waste Management at the ACS Special Symposium on Hazardous-Waste

Management, Atlanta, GA, U.S.A., September 21-23, 1992; also published in theProceedings of the Symposium, I, 325-328.

9 Friedler, F., Z. Kovacs, and L. T. Fan, Unique Separation Networks for Improved

Waste Elimination, presented in the Session on Computer Aided Approaches toHazardous-Waste Management at the ACS Special Symposium on Hazardous-Waste

Management, Atlanta, GA, U.S.A., September 21-23, 1992; also published in the

Proceedings of the Symposium, II, 457-460.

8 Friedler, F., K. Tarjan, Y. W. Huang, and L. T. Fan, Combinatorial Structure ofProcess Network Synthesis, presented at the Sixth SIAM Conference on Discrete

Mathematics, Vancouver, Canada, June 8-11, 1992.

7 Friedler, F., K. Tarjan, Y. W. Huang, and L. T. Fan, Computer-Aided Waste

Minimizing Design of a Chemical Process presented at the Seventh Annual


30/51

5 Friedler, F., K. Tarjan, Y. W. Huang, and L. T. Fan, Accelerated Branch and Bound

Method for Process Network Synthesis, Preprints of the Department of Mathematics,University of Veszprem, Hungary, 3/5, 1-7 (1992).

4 Friedler, F., K. Tarjan, Y. W. Huang, and L. T. Fan, A Systematic Approach to Waste

Minimizing Synthesis of a Chemical Process: Production of Perchloromethyl

Mercaptan, presented in the Session on Process Technology for Waste Reduction atthe AIChE National Meeting, Pittsburgh, PA, U.S.A., August 17-21, 1991; also

published in the Proceedings of the Conference, 93-98.

3 Friedler, F., K. Tarjan, Y. W. Huang, and L. T. Fan, An Accelerated Branch and

Bound Method for Process Synthesis, presented at the Fourth World Congress ofChemical Engineering, Karlsruhe, Germany, June 16-21, 1991; also published in the

Proceedings of the Congress, paper 12.2-9.

2 Friedler, F., K. Tarjan, Y. W. Huang, and L. T. Fan, Process Synthesis by Exploiting

the Combinatorial Properties of Feasible Process Structures, presented at the FourthWorld Congress of Chemical Engineering, Karlsruhe, Germany, June 16-21, 1991;

also published in the Proceedings of the Congress, paper 12.2-5.

1 Friedler, F., K. Tarjan, Y. W. Huang, and L. T. Fan, Waste Minimizing Synthesis of aProcess for Production of Perchloromethyl Mercaptan: Systematic Approach,presented at the Conference on Hazardous Waste Research, Kansas State University,

Manhattan, KS, U.S.A., May 29-30, 1991; also published in the Proceedings of the

Conference, 248-261.


31/51

Attachment 2

List of Publications on Stochastic Analysis and Modeling

by

L. T. Fan and Collaborators

NOTE: Only refereed journal articles are provided.

The individual articles are listed from the most recent (2003) to the oldest (1972).


32/51

Refereed journal articles

67 Fan, L. T., A. Argoti Caicedo, S. T. Chou, and W. Y. Chen, Stochastic Modeling of

Thermal Death Kinetics of a Cell Population: Revisited, Chemical Engineering

Education, 37, 228-235 (2003).

66 Chen, W. Y., A. Kulkarni, J. L. Milum, and L. T. Fan, Stochastic Modeling of Carbon

Oxidation,AIChE J., 45, 2557-2570 (1999).

65 Chen, X., W. Y. Chen, A. H. Hikal, B. C. Shen, and L.T. Fan, Stochastic Modeling of

Controlled-Drug Release,Biochemical Engineering Journal, 2, 161-177 (1998).

64 Fan, L. T., Y. Kang, M. Yashima, and D. Neogi, Stochastic Behavior of Fluidized

Particles in a Liquid-Solid Fluidized Bed, Chem. Eng. Comm., 135, 147-160 (1995).

63 Fan, L. T., B. C. Shen, and S. T. Chou, Stochastic Modeling of TransientResidence-Time Distributions during Start-Up, Chem. Eng. Sci., 50, 211-221 (1995).

62 Chen, W. Y., Z. P. Zhang, B. C. Shen, and L. T. Fan, Stochastic Modeling of TarMolecular Weight Distribution During Coal Pyrolysis, Chem. Eng. Sci., 49, 3687-3698

(1994).

61 Shen, B. C., L. T. Fan, and W. Y. Chen, Stochastic Modeling of Adsorption in a Batch

System,J. of Hazardous Materials, 38, 353-371 (1994).

60 Chen, W. Y., G. Nagarajan, Z. P. Zhang, B. C. Shen, and L. T. Fan, Stochastic Modeling

of Devolatilization-Induced Coal Fragmentation during Fluidized-Bed Combustion,Ind.Eng. Chem. Res., 33, 137-145 (1994).

59 Singh, S. K., B. C. Shen, S. T. Chou, and L. T. Fan, Acid Hydrolysis of

4K2-Carrageenan in a Batch Reactor: Stochastic Simulation of Change of MolecularWeight Distribution with Time,Biotechnol. Prog., 10, 389-397 (1994).

58 Fan, L. T., B. C. Shen, and S. T. Chou, The Surface-Renewal Theory of Interphase

Transport: A Stochastic Treatment, Chem. Eng. Sci., 48, 3971-3982 (1993).

57 Neogi, D., R. Nassar, and L. T. Fan, Fractional Brownian Motion Modeling of PressureFluctuations in Multiphase Flow System,Applied Stochastic Models and Data Analysis,

9, 19-38 (1993).


33/51

54 Fan, L. T., Y. Y. Chiu, J. R. Schlup, and S. T. Chou, The Master Equation for Linear

Adsorption and Desorption of Gases on Solid Surfaces, Chem. Eng. Comm., 108,127-146 (1991).

53 Shen, B. C., L. T. Fan, and S. T. Chou, Dynamic Modeling of Active Transport Across a

Biological Cell: Distribution of Protein Complex Molecules in the Cell Membrane andFluxes of Transported Molecules,J. of Chin. I. Ch. E., 22, 335-344 (1991).

52 Fan, L. T., D. Neogi, M. Yashima, and R. Nassar, Stochastic Analysis of a Three-PhaseFluidized Bed: Fractal Approach,AIChE J., 36, 1529-1535 (1990).

51 Fox, R. O., and L. T. Fan, Stochastic Modeling of Chemical Process Systems, ChemicalEngineering Education, XXIV, 56-60 (1990).

50 Fox, R. O., and L. T. Fan, Stochastic Modeling of Chemical Process Systems, Part 2,

The Master Equation, Chemical Engineering Education, XXIV, 88-92 (1990).

49 Fox, R. O., and L. T. Fan, Stochastic Modeling of Chemical Process Systems, Part 3,

Application, Chemical Engineering Education, XXIV, 164-167 (1990).

48 Nassar, R., S. T. Chou, L. T. Fan, and J. R. Too, A Probabilistic Model for the Dynamics

of a Structured Population of Unicellular Organisms, Comm. Statist. Stochastic Models,

6, 593 614 (1990).

47 Duggirala, S. K., and L. T. Fan, Stochastic Analysis of Attrition A General CellModel, Powder Technology, 57, 1-20 (1989).

46 Neogi, D., L. T. Fan, N. Yutani, R. Nassar, and W. P. Walawender, Effect of Superficial

Velocity on Pressure Fluctuations in a Gas-Solid Fluidized Bed: A Stochastic Analysis,

Applied Stochastic Models and Data Analysis, 4, 13-34 (1988).

45 Fox, R. O., and L. T. Fan, Application of the Master Equation to Coalescence and

Dispersion Phenomena, Chem. Eng. Sci., 43, 655-670 (1988).

44 Chou, S. T., L. T. Fan, and J. P. Hsu, Stochastic Analysis of the Transient Behavior ofan Msmpr Crystallizer; Effects of the Seed Size Distribution and Size Dependent Growth

Rate, Probability,Engineering and Informational Sciences, 1, 383-404 (1987).

43 Duggirala, S. K., and L. T. Fan, Stochastic Modeling of Non-Linear Sieving Kinetics,


34/51

41 Fox, R. O., and L. T. Fan, Stochastic Analysis of Axial Solids Mixing in a Fluidized

Bed, Chem. Eng. Comm., 60, 27-45 (1987).

40 Nassar, R., S. T. Chou, and L. T. Fan, Stochastic Analysis of Particle Degradation in aSemi-Continuous Flow System Containing Solid Particles, Hungarian Journal of

Industrial Chemistry Veszprem, 15, 73-82 (1987).

39 Yutani, N., N. Ototake, and L.T. Fan, Statistical Analysis of Mass Transfer in

Liquids-Solids Fluidized Beds,Ind. Eng. Chem. Res., 26, 343-347 (1987).

38 Fox, R. O., and L. T. Fan, Stochastic Modeling of Chemical Engineering Systems.

Application of the Generalized Master Equation to the Bubble Population in a BubblingFluidized Bed, Chem. Eng. Sci., 42, 1345-1358 (1987).

37 Too, J. R., L. T. Fan, and R. Nassar, A Stochastic Axial Dispersion Model for Tubular

Flow Reactors, Chem. Eng. Sci., 41, 2341-2346 (1986).

36 Too, J. R., R. Nassar, S. T. Chou, and L. T. Fan, Stochastic Analysis of Crystallization

in an Open Flow System,J. of the Chin. I. Ch. E., 17, 304-313 (1986).

35 Yutani, N., N. Ototake, and L. T. Fan, Stochastic Analysis of Fluctuations in the Local

Void Fraction of a Gas-Solids Fluidized Bed, Powder Technology, 48, 31-38 (1986).

34 Nassar, R., S. T. Chou, and L. T. Fan, Modeling and Simulation of Deep-Bed Filtration:

A Stochastic Compartmental Model, Chem. Eng. Sci., 41, 2017-2027 (1986).

33 Fox, R. O., and L. T. Fan, A Stochastic Model of the Bubble Population in a FluidizedBed, Chem. React. Des. Technol., 110, 291-304 (1986).

32 Yutani, N., and L. T. Fan, Stochastic Analysis and Its Application to Fluidized Beds,Kagaku Kogaku, 50, 321-326 (1986). (In Japanese).

31 Nassar, R., J. R. Too, and L. T. Fan, A Probabilistic Model of the Fischer-Tropsch

Synthesis in a Flow Reactor, Chem. Eng. Comm., 43, 287-300 (1986).

30 Too, J. R., R. O. Fox, L. T. Fan, and R. Nassar, Stochastic Modeling of a Fluidized-Bed

Reactor,AIChE J., 31, 992-998 (1985).

29 Fan, L. T., J. R. Too, and R. Nassar, Stochastic Simulation of Residence Time


35/51

26 Hsu, E. H., and L. T. Fan, Experimental Study of Deep Bed Filtration: A StochasticTreatment,AIChE J., 30, 267-273 (1984).

25 Nassar, R., J. R. Too, and L. T. Fan, Stochastic Diffusion Model for Crystal Size

Distribution in an Open Flow System,AIChE J., 30, 1014-1016 (1984).

24 Song, J. C., L. T. Fan, and N. Yutani, Fault Detection of the Fluidized Bed Distributor

by Pressure Fluctuation Signal, Chem. Eng. Comm.25, 105-116 (1984).

23 Hiraoka, S., S. H. Shin, L. T. Fan, and K. C. Kim, Pressure Fluctuations in a Gas-Solid

Fluidized Bed Effect of External Noise and Bubble Residence Time Distribution,Powder Technology, 38, 125-143 (1984).

22 Fan, L. T., S. Hiraoka, and S. H. Shin, Analysis of Pressure Fluctuations in a Gas-Solid

Fluidized Bed,AIChE J., 30, 346-349 (1984).

21 Yutani, N., T. C. Ho, L. T. Fan, W. P. Walawender, and J. C. Song, Statistical Study of

the Grid Zone Behavior in a Shallow Gas-Solid Fluidized Bed Using A Mini-CapacitanceProbe, Chem. Eng. Sci., 38, 575-582 (1983).

20 Fan, L. T., T. C. Ho, and W. P. Walawender, Measurements of the Rise Velocities ofBubbles, Slugs and Pressure Waves in a Gas-Solid Fluidized Bed Using Pressure

Fluctuation Signals,AIChE J., 29, 33-39 (1983).

19 Too, J. R., L. T. Fan, and R. Nassar, Markov Chain Models of Complex Chemical

Reactions in Continuous Flow Reactors, Comp. Chem. Eng., 7, 1-12 (1983).

18 Lin, S. T., and L. T. Fan, A Simple Stochastic Model of Two Phase Flow Pressure Drop

Accompanied by Boiling Inside Circular Tubes with Inline Static Mixers, Int. J.Multiphase Flow, 8, 279-284 (1982).

17 Yutani, N., N. Ototake, J. R. Too, and L.T. Fan, Estimation of the Particle Diffusivity in

a Liquid-Solids Fluidized Bed Based on a Stochastic Model, Chem. Eng. Sci., 37,

1079-1085 (1982).

16 Lin, S. T., and L. T. Fan, Pressure Drop of Two-Phase Flow through Circular Tubes withIn-Line Static Mixers Accompanied by Condensation-Simple Stochastic Modeling of the

Data,Int. J. Heat Mass Transfer, 24, 1851-1853 (1981).


36/51

13 Too, J. R., L. T. Fan, R. M. Rubison, and F. S. Lai, Applications of Nonparametric

Statistics to Multicomponent Solids Mixing, Powder Technol., 26, 131-146 (1980).

12 Fan, L. T., L. S. Fan, and R. F. Nassar, A Stochastic Model of the Unsteady State AgeDistribution in a Flow System, Chem. Eng. Sci., 34, 1172-1174 (1979).

11 Fan, L. T., and S. H. Shin, Stochastic Diffusion Model of Non-Ideal Mixing in a

Horizontal Drum Mixer, Chem. Eng. Sci., 34, 811-820 (1979).

10 Wang, R. H., L. T. Fan, and J. R. Too, Multivariate Statistical Analysis of Solids

Mixing, Powder Technol., 21, 171-182 (1978).

9 Wang, R. H., and L. T. Fan, Stochastic Modeling of Segregation in a Motionless Mixer,

Chem. Eng. Sci., 32, 695-701 (1977).

8 Lai, F. S., R. H. Wang, and L. T. Fan, Reply to Comments on 'An Application ofNonparametric Statistics to the Sampling in Solids Mixing,' Powder Technol., 12, 95

(1975).

7 Fan, L. T., and R. H. Wang, Probability Models in Reaction Path Synthesis,AIChE J.,

21, 1233-1234 (1975).

6 Radhakrishnan, K. P., J. J. Lizcano, L. T. Fan, and L. E. Erickson, Experimental

Simulation of Stochastic Stream Response to Thermal Inputs and Application of Spectral

Analysis Techniques, Water Research, 8, 455-466 (1974).

5 Radhakrishnan, K. P., J. J. Lizcano, L. E. Erickson, and L. T. Fan, Evaluation of Methodsfor Estimating Stream Water Quality Parameters in a Transient Model from Stochastic

Data, Water Resources Bulletin, 10, 899-913 (1974).

4 Lizcano, J. J., K. P. Radhakrishnan, L. T. Fan, and L. E. Erickson, Identification ofParameters in Transient Water Quality Models from Stochastic Data, Water, Air, and

Soil Pollution, 3, 261-278 (1974).

3 Lai, F. S., R. H. Wang, and L. T. Fan, An Application of Nonparametric Statistics to theSampling in Solids Mixing, Powder Technol., 10, 13-21 (1974).

2 Chen, S. J., L. T. Fan, and C. A. Watson, The Mixing of Solid Particles in a Motionless

Mixer A Stochastic Approach,AIChE J., 18,984-989 (1972).


37/51

Attachment 3

Abbreviated or Condensed View Graphs of Three Papers on P-

graph Presented at the Annual Meeting of AIChE, San Francisco,

CA, November 16 21, 2003

Algorithmic Identification of Stoichiometrically Exact


38/51

Algorithmic Identification of Stoichiometrically Exact,Plausible Mechanisms of the Catalytic Ethylene

Hydrogenation Reactionby Fan, Shafie, Khaitan, More, Bertok, and Friedler

A graph- theoretic algorithmicmethod

Process graphs (P-graphs)

Axioms

Feasible reaction pathways

Combinatorially feasiblereaction networks

Algorithms

RPIMSG

PBT

Input

Overall reaction: 1

Elementary reactions: 7

Combinatorial complexity:

(3n 1) = 37 1 = 2,186

Overall reaction :

C2H4 + H2 C2H6

Elementary reactions:proposedmechanism for two active sites

H2 + 2l1 2Hl1H2 + 2l2 2Hl2C2H4 + 2l1 l1C2H4l1l1C2H4l1 + Hl1 l1C2H5 + 2l1l1C2H4l1 + Hl2 l1C2H5 + l1 + l2l1C2H5 + Hl1 C2H6 + 2l1l1C2H5 + Hl2 C2H6 + l1 + l2

A3.1


39/51

Results

Feasible pathways:independent; 8combined acyclic; 17

Conventionallyaccepted mechanismfor one active site:

Pathway 3

Computationalefficiency: less than asecond with a PC(Intel Pentium III,533 MHz, 128 MBRAM)

A3.2


40/51

P-graph representation of

independent pathway 7

H2 + 2l1 2Hl1

H2 + 2l2 2Hl2

2C2H4 + 4l1 2l1C2H4l1

2l1C2H4l1 + 2Hl1 2l1C2H5 + 4l1

2l1C2H5 + 2Hl2 2C2H6 + 2l1 + 2l2

A3.3

Feasible and Optimal Flowsheets for Downstream


41/51

Feasible and Optimal Flowsheets for DownstreamProcessing in Biochemical Production of Butanol,

Ethanol and Acetone: Inclusion of Pervaporationby Liu, Fan, Seib, Friedler, and Bertok

Comprehensive flowsheet with inclusion of pervaporation: P-graph

S55S56

U1

S57

P1

E1

S11

S08

S1

S16

S13

D1

S05

G1

S06 S07

D2

S00

S51 S52

C1

S53

B1 B2

S54

B3B4

A1 D3 A2

S06

D7

D8

S15

D5

D6

S31

D9

D10

S32

D11

D12

D13 D14

S34

S33

D15

D16

S36

S35

D17

D18

S38S37

D19

D20

S39 S40

D21

D22

S46S43

S44 S45

D25

D26

S50S47

S48 S49

D27

D28 D29

S01

S02

S03

S09

S19

S20

A graph- theoretic approach Process graphs (P-graphs)

Axioms Feasible reaction pathways Combinatorially feasible

reaction networks

Algorithms MSG SSG ABB

Input: 25operating units

A3.4


42/51

Comparison of the total costs of the 10-best flowsheets generated

4000

4500

5000

5500

6000

6500

7000

7500

8000

1 2 3 4 5 6 7 8 9 10

Rank

Cost(1000US$/Year)

4000

4500

5000

5500

6000

6500

7000

7500

8000

1 2 3 4 5 6 7 8 9 10

Rank

Cost(1000US$/Year)

4000

4500

5000

5500

6000

6500

7000

7500

8000

1 2 3 4 5 6 7 8 9 10

Rank

Cost(1000US$/Year)

4000

4500

5000

5500

6000

6500

7000

7500

8000

1 2 3 4 5 6 7 8 9 10

Rank

Cost(1000US$/Year)

Combinatorialcomplexity:(2n 1) = 225 1

= 33.554 106

Results Optimal and

near-optimalflowsheets: 4sets of 10 eachfor parametricstudy withrespect to thecost of

pervaporation

Conclusion Profound computational

efficiency: less than 5 sfor each set with PC(Pentium 266 Mhz; 65MB RAM; W95)

Novel paradigm forprocess design anddevelopment

Retrofitting vs newdesign

(a) Conventionaloperating unitsonly

(b) Current bestestimate

(c) 84% reduction (d) 97% reduction

A3.5

A (A)


43/51

The optimal flowsheet with pervaporating and ultrafiltering units included

P-graph representation

S20 S19

S39S09 S40

20-1(D21)

20-2(D22)

S00

S56

26(U1)

S08

S55

S57

27(P1)

Conventional representation

S09

S20

S19

S40

S39

Acetone(A)

A 7

Butanol(B)Ethanol(E)


Ethanol(E)E 2

Butanol(B)B 26

S00

Acetone(A)

Butanol(B)Solids(D)

Ethanol(E)Water(W)

A 11

B 27S 87

E 4W 1773

S56

Ethanol(E)Water(W)Butanol(B)Solids (D)

E 1W 249B 1

S 87

S55

Acetone(A)

Butanol(B)

Ethanol(E)Water(W)

A 11E 3

B 26W 1524

U1

S57

Acetone(A)Ethanol(E)Water(W)

A 4E 1W 899

P1

S08

Acetone(A)


A 7

B 26E 2

D21

D22

A3.6

Synergistic identification of multiple flux


44/51

Synergistic identification of multiple fluxdistributions and multiple metabolic pathways:

Application to the E. coli modelby Lee, Fan, Park, Lee, Shafie, Bertok, and Friedler

Model

Metabolites: 52

Reactions: 48

A3.7

Algorithmic Method


45/51

Algorithmic Method

P-graph Representation of

the E. coli Model: Input

Glycolytic pathway

PPP TCA

Algorithm RPIMSG

Algorithm PBT

A3.8

Results


46/51

Normalized Multiple Flux Distributions for the Maximum

Ethanol Production Four different flux distributions leading to the same external state:

the net reaction balance, GLCxt 2 ETHxt + 2 CO2xt

Pathway redundancy: cell robustness which is a unique feature ofcomplex systems

Computational efficacy: less than 1 second with a PC (Intel PentiumIV, 1.8 GHz, 768 MB RAM); extension to the large-scale models(300 & 700 metabolic reactions)A

3.9


47/51

Attachment 4

Abbreviated or Condensed View Graphs of Two Papers on

Stochastic Analysis and Modeling, One Recently Published and

the Other Presented at the Annual Meeting of AIChE, SanFrancisco, CA, November 16 21, 2003

Stochastic Modeling of Thermal Death Kinetics of a


48/51

Microorganisms: Discrete and

randomly behaving Stochastic modeling: Pure-death

process

Mean and higher moments:

Variance, skewness, and kurtosis.

Comparison with experimental

data: Mean in accord withexperimentally measured data

n n 1 n 1 n n

dp (t) p (t) p (t)

dt

+ +=

A4.1

Stochastic Modeling of Thermal Death Kinetics of aCell Population: Revisited

by Fan, Argoti-Caicedo, Chou, and Chen(Chemical Engineering Education, 37, 228-235)

Electron microscopic image of S. aureus (From:http://www2.uol.com.br/cienciahoje/chdia/n468.htm)

0.200


49/51

Normalized mean, m, and normalized standard deviation, , as functions of the

dimensionless time, , for the low-range of the number concentration of live cells

0.000

0.001

0.002

0.003

0.004

0.005

0.006

0 2 4 6 8 10 12 14 16 18 20 44 46 48 50

Dimensionless time,

0.040

0.060

0.080

0.100

0.120

0.140

0.160

0.180

Experimental data

T = 52 Co

T = 54 Co

T = 56 Co

n = 100

n = 1000

n = 100,0000

Mean

m( ) ( ) /n0

m()

or

m()

)/n

(

0

A4.2

Stochastic Modeling and Simulation of the Formation


50/51

Stochastic Modeling and Simulation of the Formationof Carbon Molecular Sieves by Carbon Deposition

by Fan, Argoti, Walawender, and Chou

CMS Formation: Complex and

random

Stochastic modeling: Pure-birthprocess

Mean and higher moments:Variance, skewness, kurtosis, etc

Comparison with experimental

data: Mean in accord with

experimentally measured data

Side view of the progression of CMS formation:

Carbon source ; Fine carbon particle ; Carbon packet

n n 1 n 1 n n

dp (t) p (t) p (t)

dt =

A4.3


51/51

Dimensionless mean, m, and dimensionless standard deviation, , forthe pore-narrowing as functions of the dimensionless time,

Dimensionless time,

0.000 1.000 2.000 3.000 4.000 5.000 6.000

w(),m(),orm()+

()

0.000

0.200

0.400

0.600

0.800

1.000

0.000

0.200

0.400

0.600

0.800

1.000

w

(

),m(

),orm(

)+

(

)T = 873 K

T = 923 K

T = 948 K

T = 973 K

T = 1023 K

T = 1048 K

T = 1073 KT = 1098 K

T = 1123 K

T = 1173 K

T = 1223 K

Dimensionless mean, m()

m() ()

Dimensionless experimental data, w()

A4.4

Documents

CAST Award Lecture