Use of DynoChem in Process Development. Wilfried Hoffmann.


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Use of DynoChem in Process Development. Wilfried Hoffmann.

  1. 1. Use of DynoChem in Process Development by Wilfried HoffmannOld: Chemical R&D, Sandwich, UK Worldwide Pharmaceutical SciencesNew: Scale-up Systems, Dublin, Ireland DynoChem Scale-up Systems 1
  2. 2. The Fundamental Problem of Scale-upThe major objective of Process Development is the design of a sequence ofoperations, which allow the safe and ecologically responsible manufacturingof Active Pharmaceutical Ingredients at a scale demanded by market, in aquality demanded by Regulatory Authorities, and at the lowest achievablecostThis development is based on lab scale experiments ?
  3. 3. The Fundamental Problem of Scale-upTraditional approach:Lab Reaction Development Pre Scale-up Scale-up Lab Robustness testing Risk of Design Process Safety testingExperiments FailureThis approach underestimates the effects of physical rateson the overall performance are functions of scale- rate of heat transfer and equipment and- various rates of mass transfer can compete with- various rates of mixing chemical rates
  4. 4. The Fundamental Problem of Scale-up Process Development needs to consider scale and equipmentAs large scale development experiments are prohibitive with respect tocost, safety, and time but large scale performance information is requiredthe solution is: Process ModellingProcess Modelling allows the prediction of the interactions of chemical andphysical rates as a function of operating conditions, scale, and equipment
  5. 5. The Fundamental Problem of Scale-up Lab Design approach => Model based approach:Process Understanding based Design Scale-up Model Generation Model + Equipment data Lab Data Predicted Large Scale Process Experiments (Model) Performance OptimizationExperiments are performed to generate Process Understanding, notnecessarily to get good yields in the lab.This Process Understanding is then captured by First PrinciplesMechanistic ModelsA software package used by Pfizer which supports the generation andcapture of this information is Scale-up Systems’ DynoChem
  6. 6. Process UnderstandingWhat is Process Understanding?In this context Process Understanding is the necessary required knowledgeto allow predictions on the process behaviour on scaleHow can we access this knowledge?The first action is an analysis of the different rate processes (elements)in our process (for illustration I am using a chemical reaction, but thesame principles can be applied to other unit operations)DynoChem uses a visualisation tool, which is very useful for the early partof this modelling approachIn the following this tool will be demonstrated for a semibatch reaction in ajacketed reactor with a solid phase present
  7. 7. Process UnderstandingFEED TANK BULK LIQUID Solvent, Tr0 Element 1: The chemical rxn system Chemistry Including heat generationSolvent, A,T dos Element 2: Heat transfer Flow rate B UA Element 3: (kLa)1 Heat Dosing mass transfer B (s) out H SOLID Element 4: Solid/liquid system
  8. 8. Process Understanding Small Scale Large Scale Analysis Translation Construction Process Understanding Element 1 Element 1 Element 2 Element 2 Large Scale Lab Processreaction Element 3 Element 3 Element 4 Element 4
  9. 9. Process UnderstandingFirst Principles Mechanistic Models are using Basic Rate Laws andThermodynamics combined with fundamental conservation of mass andenergy to present these elements(in contrast to empirical or DoE type models)Chemical Rate Laws:Chemical Reactions are best described by a set of elementary reactions, i.e.reactions on a molecular level. These reactions are either unimolecular (bondscissions or rearrangements) or bimolecular (by collision of two species)The advantage of this approach is that all unimolecular reactions are first order and allbimolecular reactions are second order. The disadvantage is that a complex reactionsystem will require a set of elementary reactions, each with a rate constant and anEnergy of Activation.This approach may be very attractive to chemists, as rate models can be constructeddirectly from their knowledge about mechanisms.
  10. 10. Process UnderstandingPhysical Rate Laws:In general are proportionate to a driving forcemass transfer rate: kLa ([A]∞ - [A]) (unit [conc/time])Heat flow rate through jacket: AU (Tr-Tj) (unit [energy/time)Thermodynamics:Equilibrium and its temperature dependence is described by: RT ln K  - H  TSConservation of mass and energy:For example:Mol balances in chemical reactionsHeat generation and heat removal control the degree of heat accumulation(temperature change)
  11. 11. Process UnderstandingThe conservation of mass sounds trivial, but for the description of chemical reactionsthis appears to be one of the critical items in modellingThe reason for this is that most of the information of chemical reactions is generatedby LC based methods with UV-based detectors.Raw data from these methods will only generate area% information of the detectablespecies and no information about the mass balanceBefore such data can be used for modelling they have to be converted to absolutemol data. This can be done by using Relative Response Factors and reaction molbalances of at least 95% accuracyThe consequences of not doing this homework will be shown by a simple example
  12. 12. Process UnderstandingThe importance of the mol balance is demonstrated by a drastic example Mass balance Analytical data A+B→CThese data will not matchEither we have to change the mass balance (for example adding a rxn A → D),or the analytical data are wrong and have to be corrected
  13. 13. Process UnderstandingThe basis for modelling are time resolved profiles of experimental data 1) Analytical profiles 2) Heat generation rates 3) Additional online info (ReactIR, pH, gas generation, H 2 uptake, etc...) 4) Accurate temperature profiles Experimental Data: Kinetic model: moles moles time time
  14. 14. Example SystemThe following example system, which has been used in several DynoChem trainingcourses at Pfizer and which was related to real processes, will demonstrate the dataflow and the way of model generation for the scale-up of an exothermic semi-batchreactionStarting point is a simple reaction k1 A + B P r1 = k1 [A] [B] k2 A + P SP r2 = k2 [A] [P] This reaction was run in the lab at 60oC and there were seen these 4 species with a mass balance close to 100% . An analytical method was developed and Relative Response Factors were measured. The reaction was followed against an Internal Standard and so the HPLC data could be converted to absolute mol data
  15. 15. Example System This reaction system element in DynoChem is presented by a block of linesReactions in Bulk liquid k> 1.00 E-03 L/mol s at 60 C Ea> 60 kJ/mol dHr 0 kJ/mol * A + B > P k> 1.00 E-04 L/mol.s at 60 C Ea> 60 kJ/mol dHr 0 kJ/mol * A + P > SP In this block there are estimated values for the rate constants and the Activation Energy and there is no value of the exotherm (dHr = 0 kJ/mol) available, which will probably be the knowledge in an early development stage. If not otherwise indicated (it can be done if required), DynoChem assumes that the reactions after the * are elementary reactions, so the rate laws are strictly first order in each component i.e. d[P]/dt = k [A] [B] and d[SP]/dt = k [A] [P] To get real rate parameters (k1 , k2 ,Ea1 , Ea2 ), a set of 4 experiments were performed with a different ratio of [A]o / [B]o and at 4 different temperatures (40 o C, 50o C, 60o C, and 70o C)
  16. 16. Example SystemAfter fitting all the experimental data can be reproduced with just four rate parametersk1, k2, Ea1 , Ea2
  17. 17. Example System With a calorimetric experiment the individual heat of reactions can be determined as well:Reactions in Bulk liquid k> 2.7 E-03 L/mol s at 60 C Ea> 60 kJ/mol dHr -150 kJ/mol * A + B > P k> 5.0 E-04 L/mol.s at 60 C Ea> 90 kJ/mol dHr -80 kJ/mol * A + P > SP
  18. 18. Example SystemProcess Safety data were generated directly together with the calorimetric run, whena sample of the reacted mixture was subjected to a thermal stability investigation withan ARC (Accelerating Rate Calorimeter)This revealed a dangerous decomposition reaction at a higher temperature. Thekinetics of this decomposition was evaluated from the ARC data with DynoChemand the result could be included in the kinetic description:Reactions in Bulk liquid k> 2.71E-03 L/mol.s Tref 60 C Ea> 59.997 kJ/mol dHr -149.86 kJ/mol * A + B > P k> 5.02E-04 L/mol.s Tref 60 C Ea> 90.011 kJ/mol dHr -80.60 kJ/mol * A + P > SP k> 5.00E-07 1/s Tref 60 C Ea> 140.000 kJ/mol dHr -420.00 kJ/mol * P > Dec These data will not have a big impact on the reaction at 60o C, but are of major importance for the safe scale-up:
  19. 19. Example SystemThese data allow now the prediction of the product composition for any ratioof A and B (where A can be added by a dosing system over any given time)at any given reasonable temperature as a function of timeFor scale-up there is no given temperature, but the reaction temperature is theresult of the interplay between heat generation and heat removal.Here we need to add a jacket to our model, and provide the parametersAs we want to predict temperature changes, we need to use reasonable good valuesfor the physical properties of the reaction mixture and the feed, for example cpThese data can be estimated or measured by the same calorimetric experiment wherethe heat flows were obtained
  20. 20. Example SystemThe following lines describe the heat exchange between a reaction massand a jacket Cool Bulk liquid with Jacket UA 310.3 W/K UA(v) 0.82 W/L.K Temperature C Cp 2.2 kJ/kgK coolant 5.5 kg/sHere the heat transfer UA is defined as a linear function of the liquid phasevolume with an intercept of 310.3 W/K and a slope of 0.82 W/L.K, so thatAU can be adjusted in case of a semi-batch reactionThese heat transfers can be measured or calculated by DynoChem witha heat transfer tool
  21. 21. Example SystemThe following simulation shows the temperature profile of a 1000 L run with a simpleTr-controller implemented with a feed time of 1 hr
  22. 22. Example SystemWith a feed time of 2 hrs and less excess of B the result looks like this
  23. 23. Example SystemIt appears that we are now in a position to design our process to get a combinationof the best temperature, the best feed time, the best ratio of A/B, and the best useof reactor time as a function of scale and equipmentThis is indeed possible and DynoChem has a built in functionality, which canoptimize any given process outcome or user provided functionality, for examplea whatever complex cost function.This is tempting, however, we need to consider Process Safety as wellOne of the standard scenarios in Process Safety is the question of the systembehaviour in case of a loss of cooling capacity in the worst possible moment.This question can be answered by setting the cooling capacity to 0 and calculatingthe temperature profile for this adiabatic system
  24. 24. Example System
  25. 25. Example SystemA simulation run at 60 oC with a loss of cooling capacity at the end of the feed(this is the stoichiometric point and the worst point in our system) will give a thermalexplosion (run-away) about 3 hrs later!!This 3 hrs time is called Time to Maximum Rate (TMR) and can be used as aquantitative measure of thermal riskOnce we agreed to an acceptable thermal risk (may be 8 hrs), we can then includethis in the optimizationAt a first view this risk is likely to be a function of the reaction temperature, and wemight think that lowering the temperature will reduce the riskThis might be wrong! A simulation with a starting temperature of 20 oC will give theresult shown on the next slide
  26. 26. Example System
  27. 27. Example SystemKeeping all other parameters constant, there is usually a temperature where TMRIs a maximum, as shown below (for a 2 hrs feed time) 10 rxn time after end of feed for 99% conv [h] 15 8 6 10 TMR [h] 4 5 2 0 0 20 30 40 50 60 70 Tr set [C]
  28. 28. SummaryIt is now possible to include the thermal risk into the optimization of the large scaleoperation conditionsAs a result we will get a process optimized with the consideration of scale andequipment, i.e. a change of scale and equipment will change this optimumThis concept can be used to transfer a process from Lab to Kilo Lab Kilo Lab to Pilot Plant Pilot Plant to small scale Manufacturing Transfer within Manufacturing between different scales Transfer between different equipment types, i.e. Batch / Semibatch to Plug Flow or CSTR !This is a significant advantage over traditional Process Development