Scale-up of Safety Data using Dynochem. Tom Vickery.
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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.
Scale-up of Safety Data using Dynochem. Tom Vickery.
- 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.