scale-up of safety data using dynochem. tom vickery

Scale-up of Safety Data using Dynochem3rd Process Safety Forum

Wyeth-AyerstPearl River, NJ Oct 14th, 2008

T.P. Vickery, Merck & Co. Inc.

Dynochem Overview

• Process Modeling and Simulation Tool• Currently Excel-based• Can do fitting, simulation, optimization,

vessel characterization, physical properties

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: with heat generation.

• Parameters would be Δ, amount of A, amount of solvent, φ, reaction start temperature, activation energy and pre-exponential factor

PA→

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

Why use Dynochem ?

• Integral Fit of Data – with Visualization

• Consistent Model for Scale-Up

• Modeling of What-if scenarios

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)

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

Decomposition Data for Aryllithium (from Omnical Z-3)

0 100 200 300 400500

0

500

1000

1500

Heat1jmW

Baselinej( )

mW

Timejmin

80 60 40 20 0 20 40500

0

500

1000

1500

Heat1jmW

Baselinej( )

mW

TempjK

Experiment using 2.3 millimoles of aryl substrate

Heat Flow vs. Time Heat Flow vs. Temperature

Model Fit vs. Data

• Fit of a first order reaction (k, Ea) to the scanning Z-3 experiment

• Other models tried – no real improvement

Effect of BuLi addition Time – Generic 100 gallon vessel

Temperature and Decompostion vs Add Time

-60

-50

-40

-30

-20

-10

0

0 2 4 6 8 10 12 14 16 18

Feed Time (hr)

Tem

p(°C

)

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%Tmax

Impurity

Effect of BuLi addition Time – Generic 1000 gallon vessel

Temperature and Decompostion vs Add Time

-60

-50

-40

-30

-20

-10

0

0 2 4 6 8 10 12 14 16 18

Feed Time (hr)

Tem

p(°C

)

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

TmaxImpurity

Feed Rate Control Case

To Control at For 50 gal in a 100 gal reactor

For 500 gal in a 1000 gal reactor

-50°C RateTime

0.093 L/min(16 hr charge)


-45°C RateTime



-40°C RateTime

0.402 L/min(3.75 hr charge)


Optimize Feed Time vs. Target Purity

Reactor Size

% Decomposition

100 gal 1000 gal

1% 87 min 232 min

0.2% 148 min 412 min

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.

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

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?

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.

Omit this slide

• The totalized gas flow was normalized to the theoretical: 0.11 moles total

Model before any fittingBulk liquid.Temperature (Imp) (C)Bulk liquid.Product (Exp) (mol)Bulk liquid.Qr (Exp) (W)Bulk liquid.Product (mol)Bulk liquid.Reagent (mol/L)Bulk liquid.Substrate (mol)Jacket.Temperature (C)Bulk liquid.Temperature (C)Bulk liquid.Volume (L)Bulk liquid.Qr (W)GasGen (mol/min)GasFlow (L/min)

Fit k> and dHr to data for Expt 1 (80 mL)

Time (min)

Proc

ess

prof

ile (s

ee le

gend

)

0.0 19.2 38.4 57.6 76.8 96.0-8.5E-4

0.0392

0.0792

0.1192

0.1592

0.1992

After fitting k and ΔHr

Bulk liquid.Temperature (Imp) (C)Bulk liquid.Product (Exp) (mol)Bulk liquid.Qr (Exp) (W)Bulk liquid.Product (mol)Bulk liquid.Reagent (mol/L)Bulk liquid.Substrate (mol)Jacket.Temperature (C)Bulk liquid.Temperature (C)Bulk liquid.Volume (L)Bulk liquid.Qr (W)GasGen (mol/min)GasFlow (L/min)


Time (min)

Proc

ess

prof

ile (s

ee le

gend

)

0.0 19.2 38.4 57.6 76.8 96.0-0.065

3.935

7.935

11.935

15.935

19.935

After the Ea Fit



Time (min)

Proc

ess

prof

ile (s

ee le

gend

)

0.0 19.2 38.4 57.6 76.8 96.00.0

0.04

0.08

0.12

0.16

0.2

After the Ea Fit



Time (min)

Proc

ess

prof

ile (s

ee le

gend

)

0.0 19.2 38.4 57.6 76.8 96.00.0

4.0

8.0

12.0

16.0

20.0

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

Gas flow from a ramp in a 100 gal- reactor

Rate of Temperature

Increase(K/m)

Peak Gas Flow (L/min)Tj ramp

Peak Gas Flow (L/min)Tr ramp

0.5 49 44

1 87 84

1.5 101 124

2 105 164

3 108 240

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

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)

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

Items Bulk Liquid Bulk LiquidBulk

Liq uid

Bulk Liquid Bulk Liquid

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

Comparison of the effect of addition time with a fixed jacket temperature

30 min

1 hr

Comparison of the effect of addition time with a fixed batch temperature

30 min

1 hr

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

Batch Mode – Stepwise heating

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)

A case study - peracid

• Highly exothermic decomposition

• Currently treated with sulfite (3 tanks)

• Thermal degradation (one tank)?

A case study – peracid

• Dynochem for Data Regression

A case study – peracid

• Dynochem for Destruction Profile

A case study – Peracid

• Vessel modeling and safe jacket temperatures (Dynochem and MathCAD)

10 20 30 40 500

50

100

150Heat GenerationHeat Transfer

Figure 2 - Semenov Plot for 25°C Jacket Temp

Reactor Temperature °C

Hea

t (kW

)

0.309

1.576

-1

0

1

2

3

4

5

6

-3 -2 -1 0 1 2 3

Hei

ght (

m)

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.

scale-up of safety data using dynochem. tom vickery

Technology