CRYSTALLIZATION AND PRECIPITATION ENGINEERING

Transcription

1 CRYSTALLIZATION AND PRECIPITATION ENGINEERING Alan Jones, Rudi Zauner and Stelios Rigopoulos Department of Chemical Engineering University College London, UK Acknowledgements to: Mohsen Al-Rashed, Andreas Schreiner and Terry Kougoulos; EPSRC, EU and GSK

2 Outline of talk! Introduction to crystals and crystallization! The ideal well-mixed crystallizer! Prediction of Crystal Size Distribution! Mixing effects in real crystallizers! Precipitation processes! Crystallization processes! Scale up! Scale out! Conclusions

3 Crystallization Processes! Crystallization is a core technology of many sectors in the chemical process and allied industries! Involves a variety of business sectors, e.g. Agrochemicals, catalysts, dyes/pigments, electronics, food/confectionery, health products, nano-materials, nuclear fuel, personal products & pharmaceuticals! Processes can involve complex process chemistry together with non-ideal reactor hydrodynamics Hence can be difficult to understand and scale-up from laboratory to production scale operation! Crystallization also forms part of a wider process system

4 Crystallization Process Systems Water Feed Clean air Recycle Liquor to recycle Liquor to recycle Slurry Liquor to recycle Hot air Convey Screen Mill oversize PRODUCT CRYSTALS Mix, convey, etc. Jones, A.G. Crystallization Process Systems, Butterworth-Heinemann, 2002

5 CRYSTAL CHARACTERISTICS Crystals appear in many:! sizes,! shapes and! forms, Which affect both:! performance during processing, and! quality in application

6 Phase Equilibria Understanding phase equilibria is crucial to crystallizer operation! Undersaturated - crystals will dissolve! Metastable - crystals will grow! Labile - solution will nucleate spontaneously Solubility-supersolubility diagram

7 Supersaturation! Thermodynamically, solute in excess of solubility Supersaturation = µ RT where µ = chemical potential!for practical use c = c c * or S = c / c * where c = concentration of solution c* = saturation concentration Supersaturation, c, is sometimes called the concentration driving force

8 Crystallization Kinetics! Nucleation rate - rate of formation of new crystals dn dt = B = k n c b nuclei/s m 3 where b = 'order of nucleation B = nucleation rate rate of increase of crystal number! Crystal growth rate of increase of crystal dimension dl g = G = k m/s g c dt where g = 'order of growth G = growth rate rate rate of increase in crystal size Corresponding expressions exist for crystal agglomeration and breakage. Thus particle formation processes all depend upon supersaturation

9 The Well-mixed Crystallizer IN OUT Precipitation reactions! Reactants flow into vessel and form a reaction zone! Particles form from reacting species via crystallisation! Process kinetics can be dominated by mixing process! Can get undesired product forms, e.g. solvates from solvent drown out Note: For batch operation: Outflow of product is zero Hydrodynamic ratio (W/D) varies as function of fill during reaction Reactant mixing & hence precipitation kinetics require optimisation

10 Designing for Crystal Size Distribution (CSD)! Key goal: Characterise inter-relationship between reactor residence time process kinetics product CSD! Understand relationship as function of reactor scale size! Design reactors and process operating conditions to yield desired CSD Kinetics Residence Time CSD The Crystallization Triangle

11 Mass balance Conservation Equations! concentration (inlet - outlet) Mass Yield! Only gives crystal yield not how mass distributed in crystal size the CSD! Need crystal number balance population balance Population balance! Accounts for number of crystals formed & their size! Hence CSD & mean particle size can be predicted! Incorporates terms for crystal nucleation, growth, agglomeration & breakage

12 Population Balance Model (PBM)! PBM (Randolph & Larson 1962) provides population of crystals as described by number density function n(l,t) L - crystal size and t -time Represents probability to have crystals with size L at moment t! Numerical solution of PBE produces Crystal Size Distribution (CSD)! G - growth rate n ( ng ) + + t L n n τ! B & D - Birth & Death functions for agglomeration & breakage! B 0 - nucleation rate! τ - residence time (for continuous crystallisation)! Indices a, d & 0 relate to agglomeration, breakage & nucleation o = A partial integro-differential equation solved by numerical methods eg finite element. For non well-mixed systems need to include velocity derivatives in addition to crystal growth rate. B a D a + B d D d + B 0

13 Problems with Reactive Precipitation! Spatial variation in reactant concentration & crystallizer performance thus sensitive to mixing conditions processing scale size! For fast supersaturation rises and large vessel sizes this gives variability in particle formation rates! Scale-dependant fluid mechanics also effect process kinetics through its impact on secondary nucleation! Mixing effects tends to be particularly pronounced for fast precipitation systems (Danckwerts, 1958) Danckwerts, P. V., The effect of incomplete mixing on homogeneous reactions. Chemical Engineering Science., 8,

14 Computational Fluid Dynamics Why use CFD?! To investigate localised mixing effects and fluid hydrodynamics 1. Local velocities 2. Local energy dissipation (ε loc ) 3. Solid volume fraction * 4. Heat transfer and temperature profile *! For the development of crystallizer compartmental modelling framework! To facilitate modelling, scale-up and design * Kougoulos et al., Scale-Up of Organic Crystallization Processes. In AIChE National Meeting, Recent Developments. In Crystallization and Evaporation. San Francisco, CA, USA, November 2003, (New York: AIChE), Paper 310B

15 Agitated Vessel Mixing! Real agitated vessels are not wellmixed except at small volumes and/or high power inputs, which may cause particle disruption! Uniformity of mixing decreases as vessel size increases!numerical solution of the Navier-Stokes Equations

16 Some CFD and Precipitation Studies! Seckler et al Precipitation of calcium phosphate in a 2-D CFD jet mixer! Van Leeuwen et al Zonal CFD model of BaSO 4 precipitation! Wei and Garside 1997 Precipitation of BaSO 4 in stirred tanks! Al-Rashed & Jones 1999 CFD modelling of gas-liquid precipitation! Bezzo et al Integration of CFD and process simulation! Baldyga and Orciuch, 2001 PDF CFD methods! Zauner and Jones 2002 CFD-Segregated Feed Model! Rigopoulos & Jones 2003 CFD-Reaction engineering model

17 Mixing Effects in Gas-liquid Precipitation 1.4E-8 Crystal Mean Size / (m) 1.2E-8 1.0E-8 8.0E-9 6.0E-9 4.0E-9 CFD Penetration Film 2.0E-9 0.0E Ti m e / (s) CFD + PBM simulations in qualitative agreement with experiment but v. slow compartmentalisation Al-Rashed, M.H. and A.G. Jones. "CFD modelling of gas-liquid reactive precipitation". Chem Engng Sci., 54 (1999),

18 Precipitated Calcium Carbonate Crystals Note presence of agglomerates and fines attrition?

19 Mixing Effects: Segregated Feed Model! Villermaux s (1989) Segregated Feed Model (SFM) based on physically meaningful mixing parameters involving diffusive micro-mixing time convective meso-mixing time! SFM particularly suitable for modelling mixing effects, as it combines advantages of both compartmental model physical model

20 Segregated Feed Model (SFM) reaction plume f 1 Q f1 Q f2 u 1,2 reaction plume f 2 u 1,3 u 2,3 SFM divides reactor into three zones: two feed zones f 1 and f 2 bulk b Feed zones exchange mass with each other & with bulk bulk b Process depicted by flow rates u 1,2, u 1,3 and u 2,3 respectively Q b According to time constants characteristic for micro-mixing & meso-mixing

21 Characteristic Mixing Times Meso-mixing bulk blending Micro-mixing molecular diffusion Based on time constants (Baldega et al 1995) t micro ν = 17.3 ε loc 1/ 2 t meso = A ε ε avg loc N Q d s Time constants t micro & t meso can be regarded as inverse coefficients for mass transfer by diffusion & convention, respectively Energy dissipation rate (ε) predictable from CFD

22 Precipitation Process: Scale-up Methodology Hydrodynamic model (CFD) Mixing model (Segregated Feed Model SFM) Large-scale reactor Laboratory-scale experiments Population balance! Carry out laboratory scale measurements (kinetics etc)! Model hydrodynamics via computational fluid dynamics (CFD)! Use population balance model for particle properties (number/csd)! Link two models via segmented feed model (SFM)! Predict precipitation performance as function of scale size

23 Process Scale-up: Semi-batch Precipitation! Note small particle sizes at low energy inputs! Results from local zones with high levels of supersaturation & nucleation L 43 [µm] E Specific power input ε [W/kg] 1 l reactor, exp. 5 l reactor, exp. 25 l reactor, exp. 1 l reactor, model 5 l reactor, model 25 l reactor, model!in contrast at high values of energy input breakage acts as size-reducing process!this leads to smaller particles Calcium Oxalate Precipitation: Particle Size vs Power Input Zauner, Rudolf and Alan G. Jones. "Scale-up of continuous and semi-batch precipitation processes." Ind Engng Chem Res, 39, (2000)

24 Precipitation in Bubble Columns! The formation of a solid product via a gas-liquid reaction! Common applications: inorganic salts (e.g. CaCO 3, CaSO 4 ), fine chemicals! Apart from yield, the Particle Size Distribution (PSD) of the product is very important

25 Conventional Approaches to Bubble Column Modelling and Scale-up! Experimental approach - use of empirical correlations Limited validity of correlations, often lead to contradictory conclusions! Hydrodynamic approach - entirely based on CFD Not yet possible to couple with the non-linear dynamics of fast reactions and crystallisation mechanisms that occur at the gas-liquid interface

26 A Trade-off: Hybrid CFD - Dynamic Reaction Engineering Model C Hydrodynamic scale (mesoscopic) x Interfacial scale (microscopic) Bulk scale (macroscopic)

27 Model Assumptions! Isothermal operation! Only primary processes of particle formation (i.e. no secondary processes that involve particle-particle interactions such as agglomeration)! Dilute suspension, i.e. negligible influence of solids presence on hydrodynamics! Homogeneous bubbly flow, i.e. no bubble coalescence

28 CFD Modelling of Gas-liquid Flow in a Bubble Column! Captures the gross hydrodynamic effects that determine the overall long-timeaverage gas hold-up and liquid circulation! Eulerian-Eulerian twodimensional dynamic model considered adequate for that purpose gas hold-up in riser, % riser, model riser, experiment downcomer, model downcomer, experiment gas flowrate, m 3 /s (x10-4 )! Use of CFX flow solver CFD and experimental gas hold-up Rigopoulos, Stelios and Alan G. Jones. "A hybrid CFD - reaction engineering framework for multiphase reactor modelling: Basic concept and application to bubble column reactors". Chem. Eng. Sci., 58, (2003),

29 Case Study: CaCO3 Precipitation via CO2 Absorption in Ca(OH)2 Solution mol fraction Equilibrium concentrations ph CO3 HCO3 CO2 CO 2 (g) CO 2(aq) CO 2(aq) + OH - HCO - 3 HCO 3- + OH - CO 3= + H 2 O Ca ++ + CO 3= CaCO 3(s) absorption sub-reaction i sub-reaction ii crystal formation

30 Time Course of Concentration Profiles ph concentration (mol/m 3 ) CO2 CO3 HCO time (min) time (min)

31 Evolution of Supersaturation concentration (gmol/m 3 ) CO3= gmol/m3 Ca++ gmol/m3 Supersaturation time (min)

32 Evolution of Nucleation Rate log nucleation rate (nuclei/sec) 1E+15 1E+13 1E+11 1E+09 1E time (min)

33 Experimental Results and Model Predictions E E-06 ph Agglomerate 1.0E E E E-07 Particle size (m) 7 2.0E time (min) 0.0E+00 ph (model) Size (model) ph (exper.) Size (exper.) Reasonable agreement up to the onset of agglomeration

34 SEM Micrographs of Calcium Carbonate Crystal Agglomerates:

35 Effect of Crystal Agglomeration 21 litres Ca(OH) 2 = 3 mol/m 3 ; : m 3 /s CO 2 : N 2

36 Current Work!Compartmental model of batch cooling crystallization at high solids content

37 Batch Cooling Crystallization Pre-processing! CFX-Promixus! Multiple Frames of Reference Simulations! Multi-Fluid Model (MFM)! Modified Drag coefficient (Brucato, 1998)! Monodisperse particle sizes! Standard k-ε turbulence model! Heat transfer (estimated liquid side heat transfer coefficient)

38 Computational Fluid Dynamics at High(er) Solids Content CFD clips of [1] velocity profile development and [2] particle concentration [1] Shows flow dampening [2] Shows solids segregation Illustration based on 5L batch cooling crystallizer operating at 300 rpm 200 µm 5 v/v% (7 % w/w)

39 Compartmental Model Flow (Rushton turbine) [1] [2] [1] Shows overall flow pattern on different horizontal planes [2] Overall flow pattern on vertical scale 45 o angle to baffles

40 Compartmental Model Heat (Rushton turbine) 1. Heat transfer coefficient Nu = hd = C Re a Pr b µ k 2. Simulate linear cooling profile (353K to 293K at -1 o C min -1 ) 3. Cooling zones evident 4. Cooling profile influences temperature gradients c Cooling Zone Uniform Bulk Temperature Cooling Zone Temperature profile after 360s simulation

41 Compartmental Model Slurry (Rushton turbine) Q 6, Q 8,9 Q 9,1 Q 9,2 8 9 Q 1,2 Q 2,3 Q 5,1 Q 5,2 Q 5,6 5 Q 7,8 7 Q 3,9 Q 3,7 Q 4,2 Q 3,4 Q 4,5 4 Network of Zones Green: Bulk Zone Orange: Cooling Zone Blue: Impeller Zone Red: High Solids Content Zone Based on CFD modelling at different crystallizer scales using a Rushton impeller

42 Process Modelling! gproms (Process Systems Enterprise Limited) 1. Dynamic Simulations 2. Compartmental facility available 3. Batch crystallization process can be simulated 4. Optimisation can be carried out 5. Population balance with crystallization kinetics! New technology 1. CFD (Fluent) and gproms interface 2. Simultaneous CFD simulation & modelling in gproms

43 Simulations! Initial boundary conditions 1. Seed distribution 2. Supersaturation 3. Temperature! Define time steps for batch process Theoretical CSD Prediction Experimental CSD! Define parameters, variables & algebraic expressions! Population, mass and energy balances are ODEs Mass distribution (% w/w) Crystal Size, (µm)

44 A better way..? Scale out, rather than up

45 Segmented Flow Tubular Reactor (SFTR). After Lemaître et al. Reagents are mixed and formed into well-mixed mini crystallizer droplets within a segmenting fluid, which are subsequently separated Donnet, M., P. Bowen, N.Jongen, J. Lemaître, H. Hofmann, A. Schreiner, A.G. Jones, R. Schenk, C. Hofmann and S. De Carlo. Successful scale-up from millilitre batch optimisation to a small scale continuous production using the Segmented Flow Tubular Reactor. Example of calcium carbonate precipitation. In Industrial Crystallization, September 2002, Sorrento, Italy. Chemical Engineering Transactions, 3, (2002),

46 Interdigital Micro Mixer. (After Schenck et al. ) Schenk, R., M. Donnet, V. Hessel, H. Hofmann, N. Jongen and H.Löwe, Suitability of various types of micromixers for the forced precipitation of calcium carbonate, In 5th International Conference on Microreaction Technology (IMRET 5), Strasbourg, France May 2001.

47 Predicted Mean Particle Sizes of Calcium Carbonate mean size d 1,0 [µm] m (seeds) = 0 mg / L m (seeds) = 0.1 mg / L m (seeds) = 7.5 mg / L m (seeds) = 10 mg / L initial concentration [mol/l] Schreiner, A. and A. G. Jones. Precipitation in the Segmented Flow Tubular Reactor (SFTR). In Industrial Crystallization, September 2002, Sorrento, Italy. Chemical Engineering Transactions, 3, (2002),

48 Crystals From the SFTR a). Vaterite b). Y-Ba oxalate. (Courtesy

49 Conclusions! New computational techniques for the analysis and design of systems for the manufacture of particulate crystals have become available! The more complex precipitation processes whereby crystallization follows fast chemical reactions have also been analysed more deeply! This progress has been aided by the growing power of the population balance and kinetic models, CFD and mixing theory, respectively! Further progress may reasonably be expected in the development of computer models, software and hardware! Alternative techniques are under development to avoid mixing problems and obtain efficient processes and high quality products