Computer-Aided Methods & Tools for the Pharmaceutical Industry - Rafiqul Gani Department of Chemical Engineering
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C APEC
Computer-Aided Methods &
Tools for the Pharmaceutical
Industry
Rafiqul Gani
Department of Chemical Engineering
Technical University of Denmark
DK-2800 Lyngby, Denmark
www.capec.kt.dtu.dk
rag@kt.dtu.dkStatements from Industry
We develop and provide customized computer-aided solutions in
process/product development, planning and operations to assist our
company (pharmaceutical and specialty chemical products) to
introduce new products to the market faster with lower initial cost of
goods while improving cost effectiveness of their development and
manufacturing groups - CAPEC member company
One of the key challenges facing pharmaceutical companies is to
reduce the time to market and cost of goods of their products whilst
continuing to comply and exceed stringent regulatory conditions -
CAPEC member company
Need to make design decisions in the early stages of the process
(design) lifecycle so that aspects of energy conservation, environment,
waste, etc., can be incorporated - CAPEC member company, …
Our (CAPEC) role is to provide the methods & tools so that
the above can be accomplished!
Symposium on Science Collaborations, AstraZeneca, 2002 (R.Gani) 2The Challenge
The Product Tree
The objective
(challenge) is to
identify the
important fruits
(products), the
optimal path to
reach them, the
feasibility of the
process, …..
Symposium on Science Collaborations, AstraZeneca, 2002 (R.Gani) 3Dimension of the problem: Number of isomers
Cn CnH2n+1OH CnH2n+2 CnH2n CnH2n-2
p-alcohol Alkanes Alkenes Alkynes
1 1 1 0 0
4 2 2 3 2
8 39 18 66 32
12 1238 355 2281 989
16 48865 10359 93650 38422
20 2156010 366319 4224993 1678969
How many of these compounds are known? Can we afford
to ignore this knowledge when developing new products
and/or new processes?
Symposium on Science Collaborations, AstraZeneca, 2002 (R.Gani) 4Need for models C APEC
Information Problem
(knowledge, Design
Process/ data)
Product
Production
Operation Simulation
Planning
Environment
Model Analysis
Business
System
Symposium on Science Collaborations, AstraZeneca, 2002 (R.Gani) 5Research Programs in CAPEC C APEC
Tools Integration E
G
E C Synthesis/Design/Analysis
Safety & Hazards
Pollution & Waste
Process Models B
Product
Process
Property/Phenomena
Models A Numerical Tools
B Product Models Databases F
Control & Operation D
Process Integration E
Symposium on Science Collaborations, AstraZeneca, 2002 (R.Gani) 6Activities
Activitiesin
inCAPE
CAPEProblems
Problems
Parameter fitting
Choosing among Simulation
Thermodynamic for the
Process Alternatives Design Calculation
Model Selection Thermodynamic
Feasibility Study Optimization
Model
Properties of Equilibrium
unknown molecules Analysis Model Selection Guide
Regression Regression Process Properties
Input Result
TMS Conditions Values
Thermodynamic Model
Selection
Propred TML
Thermodynamics Model Library
3.5
Pure component Pure properties
Export / Import
properties
predeiciton CAPEC Thermophysical Properties Database
- Pure component properties and parameters
- Mixture properties and parameters
- Experimetal data
Symposium on Science Collaborations, AstraZeneca, 2002 (R.Gani) 7Correlation & Prediction of Properties
! Group contribution approach for pure component
and mixture properties
! Applications in specialty chemicals, fine chemicals,
pharmaceuticals
! Electrolyte systems modeled
! Maintenance of library modules & model parameter
tables
! CAPEC database of pure component and mixture
properties (including solvents)
! Integrated computer aided methods & tools
I&EC Research (2002), Fluid Phase Equilibria (2001), J Chem Eng Data
(2001), Trans IChemE (2000), I&EC Research (1999)
Symposium on Science Collaborations, AstraZeneca, 2002 (R.Gani) 8Property Estimation C APEC
Step 1: System conditions
Problem analysis Compounds present
Operation type
in the system
Step 2:
Generation/creation of Generation of problem
specific model
Property Model
Library
property model
Step 3: Parameter estimation
Parameter Estimation
Validation of generated Property calculation
Step 4: model (eg., simulator)
Validation of Property
Model
Symposium on Science Collaborations, AstraZeneca, 2002 (R.Gani) 9New Group Contribution Method C APEC
Property (X) Left-hand side of Eq. 1 Right-hand side of Eq. 1
[Function f(X)] (Group-Contribution Terms)
Normal Melting Point exp(Tm/Tm0) Σ iN iTm1i+ Σ jM jTm2j+ Σ kO kTm3k
(Tm)
Normal Boiling Point exp(Tb/Tb0) Σ iN iTb1i+ Σ jM jTb2j+ Σ kO kTb3k
(Tb)
Critical Temperature exp(Tc/Tc0) Σ iN iTc1i+ Σ jM jTc2j+ Σ kO kTc3k
(Tc)
Critical Pressure (Pc) (Pc-Pc1) -0.5 -Pc2 Σ iN iPc1i+ Σ jM jPc2j+ Σ kO kPc3k
Critical Volume (Vc) Vc-Vc0 Σ iN iVc1i+ Σ jM jVc2j+ Σ kO kVc3k
Standard Gibbs Gf-Gf0 Σ iN iGf1i+ Σ jM jGf2j+ Σ kO kGf3k
Energy at 298 K (Gf)
Standard Enthalpy of Hf-Hf0 Σ iN iHf1i+ Σ jM jHf2j+ Σ kO kHf3k
Formation at 298 K
(Hf)
Standard Enthalpy of Hv-Hv0 Σ iN iHv1i+ Σ jM jHv2j
Large increase
Vaporization at 298 K in application range; Improved accuracy;
(Hv)
Automatic group selection
Standard Enthalpy of
from theΣ imolecular
Hfus-Hfus0
structure
N iHfus1i+ Σ jM jHfus2j+ Σ kO kHfus3k
Fusion (Hfus)
Fluid Phase Equilibria (2001)
Symposium on Science Collaborations, AstraZeneca, 2002 (R.Gani) 10Application Example C APEC
Analyze the solubility of Fentanyl
* Generate data (using ProPred)
* Insert generated data into database
* Perform solid-liquid phase diagrams
Symposium on Science Collaborations, AstraZeneca, 2002 (R.Gani) 11Separation Boundaries: Distillation VLE system VLLE system Symposium on Science Collaborations, AstraZeneca, 2002 (R.Gani) 12
Separation Boundaries: Crystallization
Symposium on Science Collaborations, AstraZeneca, 2002 (R.Gani) 13Process & Product Modeling C APEC
• Modeling of micro-organisms
• Modeling of fermentation Models are the basis for all
processes further work. Once the
• Modeling of hybrid distillation process/operation model
operations and other non- has been established, it is
conventional separation analyzed, solved, combined,
processes
manipulated, etc., in other
• Modeling of fixed-bed reactor projects for development of
systems
algorithms for synthesis,
• Special purpose models (eg.,
uptake of pesticides, drugs, …) design, control; for better
• Computer aided process understanding of process &
modeling toolbox product; for inventory, ….
C&CE (1999, 2001, 2002), CAPE-NET report, Modeling Workshop (1999), Escape-
10,11 12, PSE-2000, Trans IChemE (2000)
Symposium on Science Collaborations, AstraZeneca, 2002 (R.Gani) 14What is computer aided modeling?
Problem
definition Human
System
characteristics
Problem
data
Model
construction
Model
solution
Model
Model verification
calibration &
Computer
validation
Symposium on Science Collaborations, AstraZeneca, 2002 (R.Gani) 15Model Construction Steps
M O D ELS
M A T H E M A T IC A L
M O D ELS
PRO CESS M O D ELS
• Derive the model equations
• Analyze model equations
• Translate the model equations to a solvable form;
create library for use with a simulator or for on-line
solution
A computer aided system assists the user in
performing the above tasks
Symposium on Science Collaborations, AstraZeneca, 2002 (R.Gani) 16Model construction, generation & reuse,
Extract equations
Decomposition from library
* Balance
Equations
Mathematical
model *Constraint
Equations
*Constitutive
Building block Equations
Aggregation
Define Boundary Describe System Identify Building
Block
Symposium on Science Collaborations, AstraZeneca, 2002 (R.Gani) 17Application of the modeling tool-box
Calculator or
solver
Modeling Model
Process or Tool-box library in
Model (MoT in simulator
ICAS)
Connect Create own
model object customized
to external simulator
applications
Symposium on Science Collaborations, AstraZeneca, 2002 (R.Gani) 18Construction of an operation & design model
F3 Operation/Design Model
F1
1. Charge Feed (open F1
F4 & close F2)
Reactor
2. Close F1
3. Heat until
F2 temperature = 340 K
Reaction : A → B 4. Control temperature
at 340 K
Maximum conversion 5. Charge solvent by
of 50% A at T = 340 K opening F3
Extract B from reactor 6. Extract B by opening
with a solvent! F4
Solvent ID and effects 7. …….
need to be modeled
Symposium on Science Collaborations, AstraZeneca, 2002 (R.Gani) 19Process/Product Synthesis & Design
Generate new process/product alternatives Determine
Process ? * Optimal process/product
Raw Products?
Materials * Process/product/operation
Generate better alternatives for an alternatives
existing process/product * Design of control system
* Operating conditions
purge
How? Make decisions based
sepa- on the available knowledge
mixer reactor rator
(data) and/or calculations
Raw Products using the appropriate
Materials methods & tools
Symposium on Science Collaborations, AstraZeneca, 2002 (R.Gani) 20How do we use models in process design?
Design
parameters Synthesis &
Simulation Design
Raw Results
material Iterative (trial &
Process Model error) approach
Product
T, P, X,
compounds Properties
Constitutive Model
Forward Problem: Service Role for
Process & Phenomena Model
Symposium on Science Collaborations, AstraZeneca, 2002 (R.Gani) 21Current Approaches: Limited Search Space
Constitutive Model
Constitutive Model
Constitutive Model
* Choice of a constitutive model
implicitly defines the search space
* Do we know enough to derive a
single model?
* Can we solve simulation and/or
optimization problems with multiple
constitutive models representing the
same variable?
Symposium on Science Collaborations, AstraZeneca, 2002 (R.Gani) 22More efficient method of solution
Design
parameters Synthesis &
Simulation Design
Raw Results
material Iterative approach
Process Model
Product
T, P, X,
compounds Properties
Design targets &
Constitutive Model feasibility
Forward Problem: Service & Advice Role
for Constitutive Model(s)
Symposium on Science Collaborations, AstraZeneca, 2002 (R.Gani) 23Integrated Process and Product Design
Molecular Design
Discrete Decisions
(e.g. type of compound,
Given set of molecular number of functional groups) REQUIRED
groups to be screened
(building blocks)
A framework for
Continuous Decisions
(e.g. operating conditions) tracking properties
Designed components Constraints on property values
(e.g. raw materials, MSA's) obtained by targeting optimum
process performance
Process Design
Discrete Decisions
(e.g. structural modifications) Desired process
INTERFACE
performance
Constitutive equations, Continuous Decisions (e.g. recovery, yield, cost)
(e.g. operating conditions)
e.g. property relations
Symposium on Science Collaborations, AstraZeneca, 2002 (R.Gani) 24Problem Definition: Reverse Problems
Reverse Simulation Problem: Given, Inputs
& Outputs, calculate property clusters
(design targets)
Reverse Property Calculation Problem: Given,
Property (design targets), determine molecules,
mixtures and/or conditions (T, P, x)
Symposium on Science Collaborations, AstraZeneca, 2002 (R.Gani) 25C APEC
Other examples of reverse problem
Consider the reverse problem of simulation – given the
design variables, solve the process model equations to
determine the corresponding property values
Mass Exchange Operation: Given flow, mass of solute
to be recovered, determine needed property (design)
target value for solubility
For given solubility, generate list of solvents and the
operating temperature that satisfy the desired target
Note that in the 1st step, calculation is composition
independent & 2nd step is reverse of property prediction
Symposium on Science Collaborations, AstraZeneca, 2002 (R.Gani) 26An example of product design
.
Structure Can we find better
solvents than
benzene, toluene
or cyclohexane?
Can we “design”
drugs/chemical
Name Morphine products with
Tm (K)
½
528 desirable
δ (MPa) 26.3
properties?
Figure 1: Molecular structure and properties of morphine
Define the product needs and then identify the molecules that
match these needs!
Symposium on Science Collaborations, AstraZeneca, 2002 (R.Gani) 27CAMD Framework
Start
• Solutions of
CAMD problems No suitable
solutions (due to
are iterative. Candidate
selection
performance,
economic or
safety concerns)
Problem
(final verification/
• Problem comparison
using rigorous
formulation
(identify the
goals of the
formulation simulation and
experimental Promising design operation)
candidates have
controls the procedures) been identified
success. Result Method and
analysis and constraint
• Essential qualities Finish verification
(analyse the
selection
(specify the
vs. (un)desirable suggested
compounds
design criteria
based on the
qualities. using external
tools)
problem
formulation)
• Connectivity with CAMD
external tools, Solution
(identify
data and methods. compound
having the
desired
properties)
Symposium on Science Collaborations, AstraZeneca, 2002 (R.Gani) 28Example: Design of solvent for Phenol
SLE Diagram for Phenol/Solvent system
320
Benzene
• Structure is used as Methyl sec-Butyl for
a solvent Ether dissolving
2,2-Dimethyl-1-propanal
Phenol.
Because of environmental considerations, it is desirable
to300replace Benzene.
Benzene (experimental Tfusion )
280
• Specifications:
T (K)
– Should dissolve Phenol at ambient temperature conditions at
260
least
Solvation as well as Benzene.
energy -3.863 kcal/mol -7.081 kcal/mol
Methyl sec-Butyl Ether (estimated T fusion )
– Have physical properties similar to Benzene.
240 Benzene (estimated Tfusion )
– A stripper column is used to regenerate Water used for
equipment cleaning between batches.
–220Not
0
pose the0.2 same environmental0.4
problems
0.6
as benzene.
0.8 1
x(Phenol)
Symposium on Science Collaborations, AstraZeneca, 2002 (R.Gani) 29Computer Aided Molecular (Product) Design
Pre-design Design (Start)
Interpretation to A set of building blocks: A collection of group
"I want acyclic input/constraints
CH3, CH2, CH, C, OH, vectors like:
alcohols, ketones, CH3CO, CH2CO, CHO, 3 CH3, 1 CH2, 1 CH,
aldehydes and ethers CH3O, CH2O, CH-O 1 CH2O
with solvent properties +
similar to Benzene" A set of numerical All group vectors
constraints satisfy constraints
Design (Higher levels) Start of Post-design
2.order Refined property CH2 CH2 CH3
CH2 CH2 CH3
group CH3 O CH estimation. Ability to CH3 O CH
estimate additional CH3
CH3
properties or use
CH3
CH3 alternative methods.
Group from CH2 CH CH3
CH2 CH CH3
other GCA CH3 O CH2
Rescreening against CH3 O CH2
method constraints.
Feasible alternatives found but do we have a sustainable
process to manufacture it?
Symposium on Science Collaborations, AstraZeneca, 2002 (R.Gani) 30Synthesis & Design of Distillation Trains
A B
Secondary Separation Efficiencies at P=1 atm.
0.3
A
B
0.25
C
D
E
0.2
Propane iButane
iButane nButane
C
SSE
0.15
iPentane nButane
nPentane iPentane
0.1
D
0.05
0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Liquid composition, mole fractions of the lightest com pounds
E
Order the driving force diagrams in terms of
fij| max; configure the distillation train in terms
of fij| max; design each distillation column in
terms intersection on DyDx line.
Symposium on Science Collaborations, AstraZeneca, 2002 (R.Gani) 31Visualization of Synthesis & Design
- System : H2O - (l)Asparagine -
Alanine - Serine
- Products : (l)Asparagine,
Alanine, Serine
- Solubility description:
Solubility product-
(l)Asparagine, Alanine, 8
Serine 11
- Number of chemical species: 12 4
2
- Phase diagram type: quaternary 1
- Thermodynamic model:
Electrolyte NRTL
3 6 10
Simultaneous problem 1
Mixer
2 Cryst. 4
Mixer
7 Cryst. 8 Cryst.
373.15 K 313.15 K 343.15 K
solution and visualization 5 9 12 11
for batch & continuous Alanine L-Asp Serine
operations/processes 14
Splitter
13
Trans IChemE (2000), C&CE (2000) PurgeExample: Flowsheet synthesis/design
Flowsheet Synthesis
C 2
The operator based reduced Feed 1
Mixer Product
0.1 0.9
models derived from the
original mass balance
0.2 0.8
models are linear,
0.3 thereby 0.7
making them applicable to Feed 2 Splitter
0.4 0.6
simple graphical techniques
0.5 0.5
Byproduct
0.6 0.4
Target
0.7 0.3
Feed 1
Visual Simulation
0.8 0.2
Composition free design
0.9
Feed 2
0.1 calculations, but the
compositions can be back
C3 C
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 calculated directly since the
1
flowsheet satisfies
fundamental balances
Symposium on Science Collaborations, AstraZeneca, 2002 (R.Gani) 33Examples : Blending/mixing Problem
Composition free
reduced model
C1m = c1C11 + c2C12
C2m = c1C21 + c2C22
C3m = 1 - C1m + C2m
Symposium on Science Collaborations, AstraZeneca, 2002 (R.Gani) 34PROBLEM DEFINITION TOOL BOXES
ICAS Flowsheet
Components / Reactions
Design / Synthesis Analysis
ADD TO THE SYSTEM Solvent/Fluid Energy
Units of Measure
New Components
(Property Prediction)
Constitutive Models Equipment Environmental
What to Solve
Method of Solution Flowsheet Control
New Reactions
Set/Initialize Variables Control
Output (Detail/Form) Thermodynamic
New Models
(Model Generation)
Parameter Estimation Property
Thermo-model Phase Diagrams
DATABANKS INFORMATION Expert System
Kinetic Model
STORAGE
SIMULATOR
MANAGER
Model Equations Adaptation Analysis Solvers
Balance Equations Linearization Degrees of Freedom AE / ODE / DAE
Constraint Equations Reduction Index / Sparse Pattern PDE
Constitutive relations Identification Partitioning / Ordering LP / NLP
Symposium on Science Collaborations, AstraZeneca,
RHS2002 (R.Gani) MILP / MINLP
35
RHS for the units that are solved together XIndustrial Collaboration: Examples • Computer Aided Molecular Design - solvents, process fluids, product qualities, … • Modeling & analysis of electrolyte systems • Optimization of a crystallization process • Modeling, simulation & control of complex distillation operations • Synthesis of batch operations (process route selection) • Properties of new products (CAPEC Database) & molecule structure generation • Reaction system analysis - alternative reaction paths • Solubility of complex (new) chemicals & downstream separation ……. Symposium on Science Collaborations, AstraZeneca, 2002 (R.Gani) 36
Process-Product Development in Industry
Research route
Generate route
options Outline f/s &
costs
Product
specification
Early
Selection criteria market
formulation
forecast
business
targets
SHE impact
Market requirements
(quality; tox) ROUTE SELECTION
Design, f/s,
Activity cost estimate
VPC / margin
Capital
FF&P development
THE KEY DECISION
POINT
Process Ongoing
development development
FF & P = formulation, fill Decision to
and pack invest
Small Field trials;
manufacture registration
Research Results of KT
Symposium on Science Collaborations, AstraZeneca, 2002 (R.Gani) 37CAPEC Member Companies C APEC • Current membership: 22 companies (+ IMP, Mexico) • Access to research reports, databases & software • Specialized collaborative research projects & exchange visits/training • Annual discussion meeting 38
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