Enabling increased productivity with DynoChem by targeting applications with highest business impact. Batch distillation example given.

1. Development of a high performance,
company specific DynoChem front-end
DynoChem User Meeting, 13-14 May, 2009
Jason Nyrop

2. Wouldn’t it be nice…
If lab/pilot plant/factory specific
DynoChem simulations were possible
in less than 5 minutes?
With a bit of tinkering and a few Excel
tricks, it is possible to create powerful
front-ends for DynoChem templates.
2

3. (Front-end)2?
But doesn’t DynoChem already have a front-end?
Excel sheets provided by
DynoChem are technically
already a font-end to the
DynoChem engine.
Company specific “front-ends”
developed in Excel can be laid
on top of the DynoChem
templates to further optimize
operations.
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4. Where specific front-ends are useful
There is a balance
between model
development time and
future use of the model
High Impact
To manage this,
models were developed
for unit operations that
have the following High POS
High
qualities: Occurrence
High impact
High probability of
model success
High occurrence
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5. Where specific front-ends are useful
In a survey distributed 160 100
within Merck, 140
Weighted Importance
distillations exhibit
% of Department who have modeled
Percent of department who have modeled
80
both a high rate of 120
occurrence and
Weighted Importance
importance for 100
60
modeling 80
40
60
40
20
In addition, distillation
20
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5

6. Business Drivers
Distillation optimization
has the potential to:
Cycle Solvent
Decrease cycle time
Time Usage
Decreases process costs
Decrease solvent usage
Decreases the E Factor
Decreases process costs
Distillations fall into the category of
useful front-ends
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7. Inputs required for a solvent switch
Feed Solvent
• Mass
Distill Solvent • Heat Capacity
• Density
• Composition
Distillation Volume
Distillation temperature
Vapor Liquid Equilibrium
Distillation pressure
Starting Solvent Heat of vaporization
Jacket temperature
• Mass
• Heat Capacity
• Density
• Composition Target Final Solvent Composition
Vessel UA
7

8. Distillation Front-End
The custom front-end asks what is required (yellow),
and then populates then DynoChem template
Distillation Tool Front-End a
Beta Version 0.5
Vessel: ST-200H
Target Distillation Volume: 400 L
Bulk Liquid Temperature 35°C
Distillation Pressure 100 mmHg
Feed Tank Temperature 25°C
Jacket and Batch Temperature Difference 40°C
Components, Bulk Tank, Feed Tank and Target Specifications
Components Alias Bulk Tank Weight [kg] Feed Tank Weight [kg] Target Spec [wt%]
1 MTBE MTBE 1000 0 1%
2 water Water 50 0
3 i-propyl acetate IPAc 0 2000
4 BLANK BLANK
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9. Example - Distillation
After an extraction, 1000L of IPAc contains 2% water.
The next reaction requires methanol with less than
5% IPAc and less than 800 ppm water with a final
volume of 300L.
Distillation Tool Front-End a
Beta Version 0.5
Vessel: ST-200H
Target Distillation Volume: 300 L
Bulk Liquid Temperature 25°C
Distillation Pressure 100 mmHg
Feed Tank Temperature 25°C
Jacket and Batch Temperature Difference 40°C
Components, Bulk Tank, Feed Tank and Target Specifications
Components Alias Bulk Tank Weight [kg] Feed Tank Weight [kg] Target Spec [wt%]
1 i-propyl acetate IPAc 850 0 5%
2 water Water 17 0 0.08 %
3 Methanol Methanol 0 2000
4 BLANK BLANK
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10. Step 1: Vessel lookup
Distillation Tool Front-End a
Beta Version 0.5
Vessel: ST-200H
Target Distillation Volume: 300 L
Bulk Liquid Temperature 25°C
Distillation Pressure 100 mmHg
Feed Tank Temperature 25°C
Jacket and Batch Temperature Difference 40°C
The tool uses the lookup() function to pull
the vessel heat transfer information from a
table on another sheet
Vessel Information
Heating Cooling Vessel Side area dish area dish
Building Vessel Capacity [L] Btu/f2hrF Btu/f2hrF ID" m2/100L m2 L
MSO ST-200H 1892.7 41.4 49.5 48 0.33 1.61 237.0
ST-200H UA = 378 W/K 0
ST-200H UA(v) = 1.26 W/LK 0
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11. Step 2: Tank properties
The tool uses the lookup() function to
retrieve physical properties of the solvents
A simple weighted average is used to
predict bulk heat capacity, density and
volume
Bulk Tank Starting Liquid Information:
Component: i-propyl acetate water Methanol BLANK
Mass: 850 17 0 0
Density: 874 998 791 0
Heat Capacity 1.906331814 4.2 2.5162668 0
Predicted Composite Heat Capacity: 1.951 kJ/kg K
Predicted Composite Density: 876 kg/m3
Predicted Bulk Volume: 989 L
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12. Step 3: The Components Tab
Name MW Groups
IPAc 103.13 2 CH3 1 CH 1 CH3COO
Water 18.015 1 H2O
Methanol 32.042 1 CH3OH
Name MW
=IF(Cmpd1="BLANK","",Alias1) =IF(A4="","",LOOKUP(Cmpd1,Name,MW))
=IF(Cmpd2="BLANK","",Alias2) =IF(A5="","",LOOKUP(Cmpd2,Name,MW))
=IF(Cmpd3="BLANK","",Alias3) =IF(A6="","",LOOKUP(Cmpd3,Name,MW))
=IF(Cmpd4="BLANK","",Alias4) =IF(A7="","",LOOKUP(Cmpd4,Name,MW))
Use aliases for the solvents to remove spaces and
non-letter characters
Use names within the Excel sheet to make referencing
cells easier
For lookup() to work correctly, the column or row must
be sorted in ascending alphabetical order
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13. Step 4: The Process Tab
The solvent switch library template was used as a starting
point
The top section of the process tab uses the same tricks as
the components tab
Phase Bulk liquid liquid
Temperature C plot
IPAc kg plot
Water kg plot
Methanol kg plot
volume L
Phase vapour gas
pressure mmHg plot
Mass transfer vapour and Bulk liquid
kLa 0.1 1/s
IPAc phase eqm Antoine 16.11809565 2885.139122 211.43 mmHg hvap 8885.067355 cal/mol
Water phase eqm Antoine 18.3036 3816.44 227.02 mmHg hvap 9717 cal/mol
Methanol phase eqm Antoine 18.5875 3626.55 238.86 mmHg hvap 8426 cal/mol
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14. Step 4: The Process Tab
Checks to see if
there is a specified
target for the
component
Concatenate pulls
together strings to
create the correct
syntax for the
DynoChem
simulation engine
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15. Step 4: The Process Tab
Calculates the wt% of the
component
Calculates whether the spec
is made
Calculates if all specs are
made
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16. Step 5: The Scenarios Tab
The Scenarios tab simply pulls
information from other calculations
Solvent Mass and composition
Vessel UA
Distillation Temperature
Distillation Pressure
Etc.
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17. Conclusions
Developing company
specific DynoChem front-
ends will take time for
validation and trouble-
shooting
High Impact
Once in place, front-ends
can significantly reduce
time required for model
development High POS
High
Occurrence
Front-ends are likely best
for common operations
that will be performed by
many people
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