This document summarizes two case studies where Dynochem, a process modeling and simulation tool, was used to analyze experimental safety data and scale-up risks. In the first case, Dynochem was used to model the temperature-dependent decomposition of an unstable cryogenic reaction. The tool fitted the experimental heat flow data and allowed modeling of different reaction scenarios. In the second case, Dynochem fitted gas generation data from a reaction and allowed simulation of varying heat rates to understand their effect on runaway risks. The tool provided a consistent model for safety scale-up evaluations in both cases.

1. Scale-up of Safety Data
using Dynochem
3rd Process Safety Forum
Wyeth-Ayerst
Pearl River, NJ
Oct 14th, 2008
T.P. Vickery, Merck & Co. Inc.

2. Dynochem Overview
• Process Modeling and Simulation Tool
• Currently Excel-based
• Can do fitting, simulation, optimization,
vessel characterization, physical
properties

3. One key point!
• In order to use this (or any) modeling tool, draw a
model of your process and list the key parameters
that describe your model.
• For example for an ARC run, a typical model might
be: A → P with heat generation.
• Parameters would be Δ, amount of A, amount of
solvent, φ, reaction start temperature, activation
energy and pre-exponential factor

4. Overview
• Safety Investigation and scale-up risks
• Potential gas generation on heat-up
• Cold feed to hot batch at scale
• Catalyzed destruction of a peracid

5. Why use Dynochem ?
• Integral Fit of Data – with Visualization
• Consistent Model for Scale-Up
• Modeling of What-if scenarios

6. Case 1: Dynochem modeling of an
unstable cryogenic reaction
• Incident in small-scale prep lab believed
related to decomposition of ArLi
• Aryllithium solutions are known to be
unstable
• Possibly 2 ArXLi X-Ar-Ar-Li + LiX
• 2 Exotherms – Heat of Addition (feed-limited)
and Heat of decomposition (T-dependent)

7. Approach
• Use the OmniCal Z-3 to obtain the heats
of reaction
– Heat of ArLi formation: -160.2 kJ/mole
– Heat of ArLi Decomposition: -524 kJ/mole
• Use Dynochem to model the temperature-
dependent portion of the reaction

8. Decomposition Data for Aryllithium
(from Omnical Heat Flow vs. Temperature
Heat Flow vs. Time
Z-3)
1500
1500
1000
1000
Heat1
j Heat1
j
mW
mW
500
Baselinej)
( 500
Baselinej)
(
mW
mW
0
0
500
0 100 200 300 400 500
80 60 40 20 0 20 40
Time
j Tempj
min
K
Experiment using 2.3 millimoles of aryl substrate

9. Model Fit vs. Data
• Fit of a first
order reaction
(k, Ea) to the
scanning Z-3
experiment
• Other models
tried – no real
improvement

10. Effect of BuLi addition Time –
Generic 100 gallon vessel
Temperature and Decompostion vs Add Time
0 6.00%
Tmax
-10 Impurity 5.00%
-20 4.00%
Temp(°C)
-30 3.00%
-40 2.00%
-50 1.00%
-60 0.00%
0 2 4 6 8 10 12 14 16 18
Feed Time (hr)

11. Effect of BuLi addition Time –
Generic 1000 gallon vessel
Tem perature and Decom postion vs Add Tim e
0 6.00%
Tmax
-10 Impurity 5.00%
-20 4.00%
Temp(°C)
-30 3.00%
-40 2.00%
-50 1.00%
-60 0.00%
0 2 4 6 8 10 12 14 16 18
Feed Time (hr)

12. Feed Rate Control Case
To Control at For 50 gal in a 100 gal For 500 gal in a 1000
reactor gal reactor
-50°C Rate 0.093 L/min 0.45 L/min
Time (16 hr charge) (32 hr charge)
-45°C Rate 0.252 L/min 1.25 L/min
Time (6 hr charge) (12 hr charge)
-40°C Rate 0.402 L/min 2.11 L/min
Time (3.75 hr charge) (7 hr charge)

13. Optimize Feed Time vs. Target
Purity
Reactor Size 100 gal 1000 gal
%
Decomposition
1% 87 min 232 min
0.2% 148 min 412 min

14. How Dynochem Helped
• Modeling the data, which had an imposed
temperature
• Ability to simulate various run conditions to
determine effect of parameters
• Ability to optimize to determine target
addition time.

15. Case 2 - Gas Generating Reaction
• A malonate ester is heated to drive off
CO2
• Gas data was collected off-line using a
mass flow meter with totalizer during an
RC-1 run
• Heat flow data was available from the RC-
1 experiment

16. The problem
• First analysis showed that heating to 80°C
was too hot – not needed.
• What effect does heat rate have on gas
generation?
– Peak gas generation rate constrained by vent
piping (850L/min)
• Fixed Jacket Rate – can it lead to a
dangerous runaway?

17. Approach
• Use Dynochem to fit a first-order reaction
model to the combined heat / gas data.
• Use simulator to test the effect of reactor-
temperature controlled heat rate
• Use simulator to raise the jacket
temperature at a fixed rate.

18. Omit this slide
• The totalized gas flow was normalized to
the theoretical: 0.11 moles total
Fit k> and dHr to data for Expt 1 fitting
Model before any (80 mL)
0.1992
Bulk liquid.Temperature (Imp) (C)
Bulk liquid.Product (Exp) (mol)
Bulk liquid.Qr (Exp) (W)
Bulk liquid.Product (mol)
0.1592
Bulk liquid.Reagent (mol/L)
Process profile (see legend)
Bulk liquid.Substrate (mol)
Jacket.Temperature (C)
Bulk liquid.Temperature (C)
0.1192 Bulk liquid.Volume (L)
Bulk liquid.Qr (W)
GasGen (mol/min)
GasFlow (L/min)
0.0792
0.0392
-8.5E-4
0.0 19.2 38.4 57.6 76.8 96.0
Time (min)

19. After fitting k and ΔHr
Fit k> and dHr to data for Expt 1 (80 mL)
19.935
Bulk liquid.Temperature (Imp) (C)
Bulk liquid.Product (Exp) (mol)
Bulk liquid.Qr (Exp) (W)
Bulk liquid.Product (mol)
15.935
Bulk liquid.Reagent (mol/L)
Process profile (see legend)
Bulk liquid.Substrate (mol)
Jacket.Temperature (C)
Bulk liquid.Temperature (C)
11.935 Bulk liquid.Volume (L)
Bulk liquid.Qr (W)
GasGen (mol/min)
GasFlow (L/min)
7.935
3.935
-0.065
0.0 19.2 38.4 57.6 76.8 96.0
Time (min)

20. After the Ea Fit
Fit k> and dHr to data for Expt 1 (80 mL)
0.2
Bulk liquid.Temperature (Imp) (C)
Bulk liquid.Product (Exp) (mol)
Bulk liquid.Qr (Exp) (W)
Bulk liquid.Product (mol)
0.16
Bulk liquid.Reagent (mol/L)
Process profile (see legend)
Bulk liquid.Substrate (mol)
Jacket.Temperature (C)
Bulk liquid.Temperature (C)
0.12 Bulk liquid.Volume (L)
Bulk liquid.Qr (W)
GasGen (mol/min)
GasFlow (L/min)
0.08
0.04
0.0
0.0 19.2 38.4 57.6 76.8 96.0
Tim (m
e in)

21. After the Ea Fit
Fit k> and dHr to data for Expt 1 (80 mL)
20.0
Bulk liquid.Temperature (Imp) (C)
Bulk liquid.Product (Exp) (mol)
Bulk liquid.Qr (Exp) (W)
Bulk liquid.Product (mol)
16.0
Bulk liquid.Reagent (mol/L)
Process profile (see legend)
Bulk liquid.Substrate (mol)
Jacket.Temperature (C)
Bulk liquid.Temperature (C)
12.0 Bulk liquid.Volume (L)
Bulk liquid.Qr (W)
GasGen (mol/min)
GasFlow (L/min)
8.0
4.0
0.0
0.0 19.2 38.4 57.6 76.8 96.0
Tim (m
e in)

22. Comparison of ΔHr
• RC-1 – Integration of Heat Flow:
• -157.2 kJ/mole
– Automatically a “good fit” as it is just a numerical
integration of the heat flow
• Dynochem – Model fitting
• -140.2 kJ/mole
– The good fit and the good agreement between the
two values give confidence that the model is
reasonable, and that the integration is working

23. Gas flow from a ramp in a 100 gal-
reactor
Rate of Peak Gas Flow (L/min) Peak Gas Flow (L/min)
Temperature Tj ramp Tr ramp
Increase
(K/m)
0.5 49 44
1 87 84
1.5 101 124
2 105 164
3 108 240

24. How Dynochem Helped
• Fitting to two different data sets
• Graphical representation of fits
• Use of data in actual reactor model
• Able to demonstrate reasonable heat-up profiles
could not generate excessive gas flow

25. N-Oxide Formation
• Heat of Reaction and 1st-order rate
constant at 52°C available from a CRC
experiment
• Charge of cold reagent to warm batch
• Avoid overcooling (reaction stalling) and
overheating (potential gas generation)

26. Approach
• Use vessel estimation tools to calculate
heat transfer parameters
– 1000L vessel UA=(1.04*V(liter)+159) W/K
• Estimate the activation energy as 125
kJ/mole (30 kcal/mole)
• Set up a “Universal” model in Dynochem
– Allows for specification of a wide variety of
parameters in Excel

27. Bulk
Items Bulk Liquid Bulk Liquid Liq Bulk Liquid Bulk Liquid
uid
Variables Volume Temperature Substrate Reagent Solvent
Units L C kg kg kg
Imposed Jacket Scale-up 450 55 30 0 420
Imposed Tr data 2 450 55 30 0 420
Batch Mode data 3 660 25 30 30 420
Adiabatic Batch data 4 660 25 30 30 420
Feed tank Feed tank Feed tank Feed tank Dosing Jacket Jacket Jacket Jacket
Volume Temperature Reagent Solvent Qv UA UA(v) coolant Temperature
L C kg kg L/min W/K W/L K kg/s C
210 20 30 180 3.5 154.19 1.04 2 55
210 20 30 180 3.5 154.19 1.04 2 55
210 20 30 180 0 154.19 1.04 2 55
210 20 30 180 0 0 0 2 55

28. Comparison of the effect of addition
time with a fixed jacket temperature
1 hr
30
min

29. Comparison of the effect of addition
time with a fixed batch temperature
30 min
1 hr

30. Temperature profile if run in batch
mode – 55°C Jacket

31. Batch Mode – Stepwise heating

32. How Dynochem helped
• Incorporate reaction data from other
sources
• Run multiple studies in one simulation
• Visual comparisons of scenarios
• Easy varying of parameters (feed rate,
jacket temperature)

33. A case study - peracid
• Highly exothermic decomposition
• Currently treated with sulfite (3 tanks)
• Thermal degradation (one tank)?

34. A case study – peracid
• Dynochem for Data Regression

35. A case study – peracid
• Dynochem for Destruction Profile

36. A case study – Peracid
• Vessel modeling and safe jacket temperatures
(Dynochem and MathCAD)
6
Figure 2 - Semenov Plot for 25°C Jacket Temp
150
5
Heat Generation
Heat Transfer
4
100
Height (m)
3
Heat (kW)
2
1.576
50
1
0.309
0
-3 -2 -1 0 1 2 3
0
-1 10 20 30 40 50
Reactor Temperature ° C

37. Conclusions
• Dynochem is very useful for generating a kinetic
fit from temperature-scanning data
• Dynochem provides direct visual feedback
during the fitting in addition to the fitting statistics
• The kinetic model parameters from Dynochem
can be plugged directly into the real equipment
model
• The data needed for Dynochem is obtained as
part of Merck’s standard testing.