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Mixing, Crystallization and Separation

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Mixing, Crystallization and Separation CRYSTALLIZATION AND PRECIPITATION ENGINEERING Alan Jones, Rudi Zauner and Stelios Rigopoulos Department of Chemical Engineering University College London, UK www.chemeng.ucl.ac.uk Acknowledgements to: Mohsen Al-Rashed, Andreas Schreiner and Terry Kougoulos; EPSRC, EU and GSK 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 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 Crystallization Process Systems Water Feed ‘Clean’ air Liquor Recycle to recycle Liquor to recycle Slurry Convey Liquor to recycle Screen Mill Hot air oversize PRODUCT CRYSTALS Mix, convey, etc. Jones, A.G. Crystallization Process Systems, Butterworth-Heinemann, 2002 CRYSTAL CHARACTERISTICS Crystals appear in many: ! sizes, ! shapes and ! forms, Which affect both: ! performance during processing, and ! quality in application 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 Supersaturation ! Thermodynamically, solute in excess of solubility ∆µ Supersaturation = 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 Crystallization Kinetics ! Nucleation rate - rate of formation of new crystals dN = B = k ∆cb nuclei/s m3 dt n where b = 'order‘ of nucleation B = nucleation rate –rate of increase of crystal number ! Crystal growth – rate of increase of crystal dimension dL = G = k ∆c g m/s dt g 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 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 Designing for Crystal Size Distribution (CSD) ! Key goal: Characterise Kinetics inter-relationship between – reactor residence time – process kinetics – product CSD CSD ! Understand relationship as function of reactor scale size ! Design reactors and process operating conditions to yield Residence Time – desired CSD The Crystallization Triangle Conservation Equations Mass balance ! ∆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 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) ∂n ∂ ( nG ) n − no + + = B − D + B − D + B ∂t ∂L τ a a d d 0 ! G - growth rate ! B & D - ‘Birth’ & ‘Death’ functions for agglomeration & breakage ! B0 - nucleation rate ! τ - residence time (for continuous crystallisation) ! Indices a, d & 0 relate to agglomeration, breakage & nucleation 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. 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., 1958. The effect of incomplete mixing on homogeneous reactions. Chemical Engineering Science., 8, 93-99. Computational Fluid Dynamics Why use CFD? ! To investigate localised mixing effects and fluid hydrodynamics 1. Local velocities 2. Local energy dissipation (εloc) 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, 16-21 November 2003, (New York: AIChE), Paper 310B Agitated Vessel Mixing ! Real agitated vessels are not well- mixed 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 Some CFD and Precipitation Studies ! Seckler et al. 1993 Precipitation of calcium phosphate in a 2-D CFD jet mixer ! Van Leeuwen et al. 1996 Zonal CFD model of BaSO4 precipitation ! Wei and Garside 1997 Precipitation of BaSO4 in stirred tanks ! Al-Rashed & Jones 1999 CFD modelling of gas-liquid precipitation ! Bezzo et al. 2000 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 Mixing Effects in Gas-liquid Precipitation 1.4E-8 CFD 1.2E-8 Penetration Film 1.0E-8 ) ze / (m 8.0E-9 i S an e 6.0E-9 ystal M r C 4.0E-9 2.0E-9 0.0E+0 0123456 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), 4779-4784 Precipitated Calcium Carbonate Crystals Note presence of agglomerates and fines – attrition? 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 Segregated Feed Model (SFM) SFM divides reactor into three Qf1 Qf2 zones: • two feed zones f1 and f2 reac tion u1,2 reaction • bulk b plume f1 plume f2 u1,3 u2,3 Feed zones exchange mass with each other & with bulk bulk b Process depicted by flow rates u1,2, u1,3 and u2,3 respectively According to time constants Qb characteristic for micro-mixing & meso-mixing Characteristic Mixing Times Meso-mixing – bulk blending Micro-mixing – molecular diffusion Based on time constants (Baldega et al 1995) 1/ 2 1 3 ν ε avg Q t =17.3× t meso = A 4 micro ε ε loc 3 loc N d s Time constants tmicro & tmeso can be regarded as inverse coefficients for mass transfer by diffusion & convention, respectively Energy dissipation rate (ε) predictable from CFD Precipitation Process: Scale-up Methodology ! Carry out laboratory scale Laboratory-scale experiments measurements (kinetics etc) ! Model hydrodynamics Hydrodynamic Population balance model (CFD) via computational fluid dynamics (CFD) Mixing model ! Use population balance (Segregated Feed Model SFM) model for particle properties (number/CSD) ! Link two models via segmented feed model (SFM) Large-scale reactor ! Predict precipitation performance as function of scale size Process Scale-up: Semi-batch Precipitation ! Note small 1 l reactor, exp. !In contrast at 5 l reactor, exp. particle sizes at 30 25 l reactor, exp. high values of 1 l reactor, model low energy 25 5 l reactor, model energy input inputs 25 l reactor, model 20 breakage acts m] µ [ 43 as size-reducing ! Results from L 15 local zones with process 10 high levels of 5 !This leads to supersaturation 1E-3 0.01 0.1 1 10 smaller particles & nucleation Specific power input ε [W/kg] 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). 2392-2403. Precipitation in Bubble Columns ! The formation of a solid product via a gas-liquid reaction ! Common applications: inorganic salts (e.g. CaCO3, CaSO4), fine chemicals ! Apart from yield, the Particle Size Distribution (PSD) of the product
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