Evolution of Chemical Engineering Modeling

Chemical Engineering Department
DHC-UPB, Bucarest 25 November 2016
The role of CAPE towards innovation in
Engineering: Evolution or Revolution
Luis Puigjaner
Universitat Politècnica de Catalunya
1

Index
 Evolution vs. Revolution: Introduction
 Part I: Samples of the past:
 Modeling Telecommunication Systems (1965-66)
 The Stochastic computer (1966-72)
 Modeling Macromolecules (1967-1974)
 Modeling Fluidized-bed reactors (1976-
 Modeling batch processes (1982-
• Part II: Samples of the present:
 Modeling the Supply Chain (2001-
 The Future
2

Index
 The Future
3

Introduction
4
Charles Babbage (1791-1871)
Analytical Engine 1812

Index
 The Future
5

Modeling Telecommunication Systems
(1965-66)
Physical Telecomunication System
6

Modeling Telecommunication
Systems (1965-66)
Subcarrier
Oscillator
1
Subcarrier
Modulator
1
Subcarrier
Modulator
n
Subcarrier
oscillator
n
+
RF
Carrier
Modulator
RF
Carrier
Oscillator
S1(t)
Sn(t)
m1(t)
mn(t)
K1
Kn
K1m1(t)
Knmn(t)
Kimi(t)
n

1
Multiplexing System SSC-FM block diagram
7
(Puigjaner, L. 1966)

Systems (1965-66)
Mathematical Modeling of the Multiplexing Telecomunication
System
    
    
S
F (ω) F (ω) JF (ω)ˆA S S
Im A (t ) H Re A (t )s s
Re A (t ) H Im A (t )s s
ˆs(t ) dt F (f ) df F (f ) df s(t ) dtˆs s
 

 
   
  
   
   
2 222
The Analytical Signal
Identical autocorrelation functions
and power spectra
The Modulated Signal in terms of Analytical signal
 S,M S o oA (t ) A (t )exp jω t s(t ) js(t ) exp jω t  
8
(Puigjaner, L., 1969)

Systems (1965-66)
z t jσi i i
n
A(t ) A (t )i
i
n
A(t ) c exp jkω tk o
k
 






1
0
Zero-Locus
The Zero-Pattern of Analytic Signal
The Zero Locus technique
Multiplicative Property
9
(Puigjaner* L., 1969)

Systems (1965-66)
-3ωo -2ωo -ωo ωo 2ωo 3ωo ω
Fm (ω)
Density spectrum at the output of the Mixer
10(Puigjaner, L. 1970)

(1965-66)
ωot
2ωot ωo
2ωo
Re
Im
Phasor diagram for multiplexed signals
11
(Puigjaner*, L., 1970)

(1965-66)
Geometrial representation
for linear signal-space
Effects of distorsion in
signal-space representation
∑
∑
∑
1
2
v
2
v 1
2
∑
1
∑
v
1
v
V’
2
2v
12
(Puigjaner*, L., 1970)

Index
 Evolution vs. Revolution
 Samples of the past: Part I
• Samples of the present: Part I
 The Future
13

The Stochastic computer (1966-72)
The stochastic (random-pulse) computer uses logical elements (gates) to
process the analog magnitude (probability of pulse occurrence in the train of
random pulses) representing process variables.
 Variable X(t)
 Stochastic representation where is a random pulse tram
 A Dirac pulse synchronous form of clock pulses is used to process random-
pulse train variables
s s
X P( X)
s
X
1
0
   
  
   
s
s
s
if X A
X
if X B
*
δ (t)
x s *
δ (t) X δ (t)
14
(Ferraté, Puigjaner*, Agulló, 1969b)

Stochastic
representation
of variables
t
t
t
t
t
t
δ (t)*
| X (t)|s
| X (t)|
s
δ (t)*
δ (t)
X (t)*
15
(Ferraté, Puigjaner*, Agulló, 1969b)

Linear stochastic
conversion
STOCHASTIC
CONVERTER
CLOCK
δ(t)
t
A
B
X
s
t
t
1
0
X N
t
1
0
X
N N < X* *
1 1 1
0 0 0
1 1 1
PN PN
PN
v v v
R
P( z)*
16
(Ferraté, G, Puigjaner*, L, Agulló, J, 1972)

Funcional Stochastic
conversion
STOCHASTIC
CONVERTER
CLOCK
δ(t)
t
A
B
X
s
t
t
1
0
X
t
1
0
X
F < X
*
*
1 1 1
0 0 0
1 1 1
PF PF
PF
v v v
X
P( z)*
F
F *
17
(Ferraté, G., Puigjaner*, L., Agulló, J. 1972)

Generation of Functional noises
1
2
…
p
1
2
…
p
1
2
…
p
1
2
…
p
clock
1 2 k
OOO1 2 k
Adder module
Feed - back
18
(Ferraté, G., Puigjaner*, L., Agulló, J., 1972)

Modeling Macromolecules (1967-1974)
 Samples of the past:
• Samples of the present:
 The Future
19

20
Maurice Wilkins (1916 - 2004)
Nobel Price 1962 - DNA

The diffraction pattern
B DNA fiber diffraction pattern
Modeling Macromolecules (1967-
1974)
21
(Arnott, S., 1965)

Analysis of X-ray diffraction patterns of fibrous structures
Fiber diffraction pattern
Pplymer primary structure
Standard bond lengths and angles
Produce MOLECULAR MODELS constrained to have appropiate pitch,
symmetry and restrained to ahve minimumsteric compression, etc. Attemp
decision among symetry choices
If specimen is oriented,
determine HELIX PITCH and
possible MOLECULAR
SYMETRIES
If specimen is also crystaline determine
DENSITY, UNIT CELL and possible
SPACE GROUPS
Determine possible modes of
CHAIN PACKING
Optimise models to fit X-RAY INTENSITIES while
maintaining CONSTRAINTS and STERIC
RESTRAINTS.Attempt decision among symmetry
and packing choicesUse ELECTRON DENSITY DIFFERENCE
SYNTHESIS to determine possible ION and/or MATER sites
Refine augmented CRYSTAL MODEL until complete
22
(Campbell-Smith, PJ., Arnott, S., 1978)

The variable parameters of a nucleotide residue
1974)
Six backbone conformational angles α,β,γ,δ,ε,ζ ( gauche – , trans, gauche +)
 Glycosidic angle X (anti, syn)
04’
C4’
C3’
C2’
C1’
v
v
v
v
v
0
1
2
3
4
C4’
C3’
C2’
P
01
02
05’
C5’
C4’
C3’
C2’
C1’
04’
03’
N
χ
ζ
ε
δ
γ
βα
23
(Puigjaner* L, 1971)

1974)
Total diffracted intensity along equator
Contribution at each layer line
Intensity factors
2
1
02
21
2
n n
eq ij
i j
J ( πRr)
I J ( πRs )
πRrn R
 
  
 

2
1 1
1
2 k
i
k
J ( πr R') f
I
Rπr R'
 
  
 

2
1
4 01
1
0
2
k
Q(v)exp( πikv)dv
r
f ( )
r
Q(v)dv
 
 
  
 
  


24
Subirana, J.A., Puigjaner, L., 1972

1974)
Model Optimization: linked-atom least squares
Objective function to be minimized
ΔF difference in structure amplitudes
Gh exact relationship among parameters
λn lagrange multipliers initially
unknown
ωm relative weight of observations
dj Non-bonded interatomic distances
E Buckingham energy function
2
m m h h
m h
Φ (ω ΔF ) λ G  
2
2
0
0
m m j h h
m j h
j j a j j j a j
j j a j
Ω ω ΔF s λ G
k ( d d ) d d
,d d
E Bd A exp( μd)
   
   
  
   
  
25
(Campbell-Smith, PJ., Arnott, S., 1978)

1974)
DNA variety and variability
Family A B
Furanose
Conformations
C3’ - endo C2’ - endo
Conformational
Genera
t g- g- t g+ g+ a t g- t t t g+ a t t g- t g+ t a t t t t t t a
No. of Congeneric
Species
16 1 4 1
Helical
Characteristics h(nm)
t(º)
0.26-0.33
30.0-32.7
0.31
36.0
0.30-0.34
36.0-45.0
0.33
48.0
26
(Puigjaner*, L., Subirana, JA., 1974)

1974)
A DNA
27

1974)
Two nucleotide
view of A-DNA
A-DNA Terciary Structure
28

1974)
A-DNA: Building the helical structure
Along the axis
view of A-DNA
29

1974)
A-DNA: Building the helical structure
Down the axis
view of A-DNA
30

1974)
A-DNA Packing
31
(Puigjaner*, L., Subirana, JA., 1974)

B-DNA
32

1974)
B-DNA: Building the helical structure
B-DNA up and down chains
33

1974)
Along & Down the axis
view of B-DNA
B-DNA: Building the helical structure
34

1974)
B-DNA Packing
35

1974)
Z-DNA Transition
36
(Armott, S.,…Puigjaner*, L.., 1983)

1974)
Z-DNA: The Superhelical Heteronomous DNA
37

1974)
The Z-transition embedded in Forms A, B
(Arnott,S., Puigjaner*, L.,1981)B A 38

1974)
The Z-transition embedded in Polymorphs
39

1974)
The DNA rich variety and variability: Hybrid sytuctures
40

1974)
41

1974)
42

1974)
43

1974)
44

1974)
45

1974)
46

1974)
47

1974)
Classification of helical secondary structures of plynucleotides
Family A
C3’ – endo ( LOW γ)
B
C2’ – endo (HIGH γ)
Genera t g- g-t g+ t g-t t t t t g- t g+ t t t t t
Species 19 1 4 1
hǺ 2.6 – 3.4 3.1 3.1 – 3.4 3.25
tº Large Δh & Small Δt Small Δh & Large Δt
EXAMPLES:
1. Duplex
DNA – DNA
RNA – RNA
DNA - DNA
dI.rC
B – DNA
C – DNA
D – DNA
E- DNA
2. Triplex U.A.U., I.A.I.
dT.dA.dT
3. Quadriplex I.I.I.I
48

Index
• Samples of the present: Part II
 The Future
49

The stabilized fluidised bed
Modeling Fluidized-bed reactors (1976-
50(Levenspiel, O./Puigjaner, L., Ed., 1986)

Uniform distribution of gas in the calming section
Modeling Fluidized-bed reactors (1976-
51
(Arnaldos, J.,…Puigjaner, L., 1985)

Magnetically stabilized Fluidized bed behavior and transition velocity
At “transition velocity” Ub, fluidized bed becomes stabilized ( no bubbles are observed)
Modeling Fluidized-bed reactors
(1976-
2800
2000
1200
400
0 10 20 30 40 50 60
ΔP
(N/m2)
u(cm/s)
d=250-400 µm
M=0.5 kg
H=0 A turn/m
H=6200 A turn/m
ub(o) ub(o)
52
(Lucas, A., Casal, J., Puigjaner L.,1984)

Bed expansion
(1976-
Bed length is
expanded until
Ub then stabilises
Ub transition
velocity
Um minimum
fluidised
8.2
7.8
7.4
7.0
0 10 20 30 40 50
u(cm/s)
Bedlength,cm
d=250-400 µm
m=0.5kg
H=3250 A tum/m
um
ub
53
(Lucas, A., Arnaldos, S., Casal, J., Puigjaner, L., 1985)

(1976-
Transition velocity calculation
4
2
2 3 2
2 3
1 2 2 1
2 3
2 1 1 2
1 77 10
1
1
1
H· . x
b m
c'B
b m
ul( ε) μ
ΔP K
d ε φ
ΔP cos ψ ε ( ε )
ΔP cos ψ ε ( ε )
u u e
u u e








Gas flow in the channels
Spatial arrangement
ε bed porosity
Ψ angle of mean velocity in
channels and vertical axis
Transition velocity, ub
H magnetic field
Ub general expresion
54
(Lucas, A., Arnaldos, S., Casal, J., Puigjaner*, L., 1985)

Bed Modeling
(1976-
fixed bed
H
um
ub
u
Spatial rearrangement
Buddling bed
Three zone diagram
55
(Arnaldos, J., Puigjaner*, L., Casal, J., 1986)

Mixed Particles Systems Applications
(1976-
 Sintered mixed oxide and silica
 Co-gasification of waste coals and
wood residuals. Iron oxide (SFO) is
used as sorbent for Sulfur
abatement
 Polymer drying (SAN, ABS)
28
24
20
16
12
0 1000 2000 3000 4000
100% silica
83%
65%
0%sintered nicked oxide 177-250µm
silica 350-420 µm
56
(Perales, F., Velo, E., Puigjaner*, L., 2003)

Index
 The Future
57

Modeling batch processes (1982-
Starting from the roof
Multipurpose batch plants planning and scheduling
(Lázaro and Puigjaner*, 1985)
58

Detailed scheduling model
Detailed subtasks considered in every task starting at time TI
a)
Zm=0
TI1m
TI λµ
b)
Zm=1
TI λµ
TI1m
s=1
Set-up
s=2
load
s=3
Operation
s=4
unload
s=5
clean-up
TW
T I1m T F1m
Unit operation :
a) semicontinuous operation;
b) batch operation
59
( Graells, Espuña & Puigjaner*, 1993)

Planning Parallel Multiproduct Plant: Large
Industrial Application (textiles manufacturing)
60
Espuña & Puigjaner*, 1989

Index
 The Future
61

Transfer times: A critical modeling issue ( Ferrer et al, 2008)
Figure 1. Unfeasible situation
for the illustrative example.
Figure 2. Feasible situation
for the illustrative example.
62
(Ferrer, S., Graells, M., Puigjaner*, L., 1985)

Sustainability Global Index ( AIChE – CEP, 2007)
Modeling the Supply Chain (2001-
Value-Chain
Management
Social
Responsibility
Safety
Performance
Innovation
Strategic
Commitment
Environmental
Performance
Product
Stewardship
U.S. Chemical
Manufacturing Sector
Chemical companies from
Global Fortune 500
63

Raw material
extraction
Manufacturing
plant
Manufacturing
plant
Manufacturing
plant
Retailer
Warehouse
Retailer
Customer
Customer
Customer
Raw material
extraction
Manufacturing
plant
Manufacturing
plant
Manufacturing
plant
Retailer
Warehouse
Retailer
Customer
Customer
Customer
supply chain network
ISA
SP 88
othersfinances
Single site
Supply chain
timehorizon&spatialscope
control production
ISA SP 95
Strategy
SC configuration
Operation
SC planning and coordination:
production, logistics, distribution
Tactic
SC planning
Supervisory control
Monitoring, fault
diagnosis
Local control
Unit coordination &
local control
Global
supervision
Operational
accounting
Strategic
financial
planning
Production Scheduling
Detailed plant production
planning
Execution
Systems and equipment
execution
Production Planning
Approximated plant
production planning
Strategy
Plant design,
retrofit
equipment
P-1
production line
manufacturing plant
Supply Chain
64(Puigjaner, L., 2009)

Tactical and operational level deci-
sions updated monthly to quarterly:
 which products are manufactured
 which factory produce each product
 which supplier is selected
 . . .
Raw material
extraction
Manufacturing
plant
Manufacturing
plant
Manufacturing
plant
Retailer
Warehouse
Retailer
Customer
Customer
Customer
Raw material
extraction
Manufacturing
plant
Manufacturing
plant
Manufacturing
plant
Retailer
Warehouse
Retailer
Customer
Customer
Customer
Other agents
Warehouses
Suppliers
(external)
Distribution
centres
MULTI-AGENT
SYSTEM
EMULATION
AGENTS
MESSAGES
Customers
(external)
Factories
Retailers
Material flows
Information
flows
Cash flows
Inventory
Purchasing
Production
Sales
Transportation
. . .
Economic
indicators
. . .
MODULES
Financial
indicators
Environmental
impact indicators
Optimisation
module
Forecasting
module
Negotiation
module
KPI
evaluators
65(Mele, F.D., Guillén, G., Espuña, A., Puigjaner*, L., 2007)

 Financial performance
 Environmental performance
 Social performance and
negociation
 Dynamic response at all levels
 ELAISA project
66

Index
 The Future
67

The Future
 Emerging fields in CAPE
 Modeling, Simulation & Optimization:
encompassing wide integration of different time &
space scales
 Sustainable process synthesis, by integrating
social, economic, environmental and processing
areas, under risk and uncertainty
68

The Future
 Emerging fields in CAPE
 Process operations and management, looking for
an agile management of process plants and the
global supply chains, comprising enterprises in
different geopolitical situations.
 Integrated atmosphere, vertically across the
automation hierarchy of a single process plant and
horizontally along the SC chain.
69

The Future
 Emerging fields in CAPE (continued)
 Information technology (IT). A new generation of
cost-effective and tailor-made supporting software
solutions is suggested which reflect the culture
and the specific work processes of an enterprise.
70

The Future
 Emerging fields in CAPE (continued)
 Numerical algorithms and computing paradigms
by suitable modular methods, which even take
advantage of distributed and parallel computing
architectures. Such a modular strategy would also
support the use of multiple numerical methods
tailored to the requirements of a partial model.
71

The Future
 Knowledge as base of future developments I
 Systems thinking, which refers to the development
and exploitation of the global system based on
tangible and intangible knowledge resources.
72

The Future
 Semantic technologies seem to offer an attractive
platform for knowledge capturing, information
management and work process guidance in the
design processes including their associated control
and operating support systems.
73

The Future
 Meta-models will arise in the analysis, construction
and development of the frames, rules, constraints,
models and theories applicable and useful for the
modeling in a predefined class of problems.
74

The Future
 Knowledge as base of future developments II
 The evolution of management system includes the
integration of implementation methodologies and
tools which must address the required capabilities
for both, including automation, performance, and
flexibility at the process side, and collaboration,
taxonomy of query and retrieval at the side of
knowledge management.
75
Data  Information  Knowledge 
Diagnosis, Decision Making & Action

The Future
 Knowledge as base of future developments II
 Systems thinking and systems problem solving, must
be enhanced, but still have to be prioritized, rather
than being the mere application of computational
problem solving methods. There are many emerging
areas where systems thinking and systems
engineering methods and tools are most likely a key
to success.
76

Sensors, Actuators & Logical Devices
Business Planning & supply chain Management
Business Process
Transactions (4)
Production
Management
Transactions (3)
Real time Control
Events (0,1,2)
Manufacturing Operations Management
The Future
 Knowledge as base of future developments III
 Since 2007 Dr. Edrisi Muñoz began the research project
called Batch Process Ontology Framework based on ISA
standards, which has evolved along different fields of
CAPE, regarding optimization methodologies, batch
process, mathematical programming, modeling, and
operations research … etc.
77

The Future
 Knowledge as base of future developments IV
 The aim of this work is the development of powerful
decision support tools for the process industry. As a
decision support tool, it must be capable of becoming a
robust model, which interacts among the different
decision hierarchical levels (strategic, tactic and
operational) providing a unified framework of data and
information levels integration
78
Decisions
Decisions
Decisions
Models
Models
Models
Models

The Future
79
(Muñoz, E., Capón-García, E., Laínez, J.M., Espuña, A. Puigjaner, L., 2015)

The Future
80
•Batch ID
•Batch process status
•Current time
•Estimated end time
•Equipment/Unit status
•Material resources
•Lot number
•Batch ID
•Equipment/Unit ID
•Start time
•End time
•Phases
•Parameter max/min
value
00
•Product
•Amount of product to
be produce
•Order production type
•Order priority
•Material resource
•Product
•Lot size
•Product ID
•Estimated processing
time
•Time line
•Equipment availability
•Equipment time for be
available
•Process sequence
•Amount of material
resource to be used
output
input
output
input
output
input
Master
Recipe
Control
Recipe
Data
ANSI /ISA S88
XML message
ANSI /ISA S88
XML message
Production Order
Scheduling Level
Control Level
Data Base
Optimization
Control
Ontology Data
Base (Query
Parameter)
ANSI /ISA S88
XML message
0 1 2 3 4 5 6
0
1000
2000
3000
4000
5000
6000
7000
Time [h]
Massprofiles[Kg]
A B C1 C2 C3
(Puigjaner, L, Muñoz, E., Capón-García, E., 2016)

Conclusions
Breakthrough discoveries do not readily happen in
engineering, even more so in the case of CAPE. This is
mainly because innovation in this field is continuously
undergoing a process of dynamic development until it
proves to be sufficiently mature to be adopted by our
industrial and social fabric, which is precisely the
underlying essence of engineering: the branch of
sciences that has contributed the utmost to make
human work more human.
81

Thanks for your attention !
Special thanks to my 53 brilliant graduate
students (now PhDs) who made possible
partly this presentation. In particular:
E. Capón-García; J. Casal; A. Espuña; S.
Ferrer; M. Graells; J.M. Laínez; M. Lázaro; A.
Lucas; E. Muñoz; F. Perales
82

83

CEPIMA GROUP
84

Evolution of Chemical Engineering Modeling

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