Estimation of the growth kinetics for the cooling crystallisation of paracetamol and ethanol solutions

Mitchell, Niall A.; Ó'Ciardha, Clifford Thomás; Frawley, Patrick J.

doi:10.1016/j.jcrysgro.2011.06.016

Cited by 60 publications

(89 citation statements)

References 17 publications

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“…23,24 In this study, however, not only the MSZW data, but the whole courses of various batch cooling crystallizations were used to estimate the crystallization kinetics, including primary nucleation, secondary nucleation, and crystal growth. A population balance based process model 25,26 was developed and applied to estimate the kinetic parameters from four representative batch experiments, as shown in Table 2.…”

Section: Materials and Experimentsmentioning

confidence: 99%

Mathematical modelling and experimental validation of a novel periodic flow crystallization using MSMPR crystallizers

Rielly

Powell

et al. 2016

AIChE Journal

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The challenges of insufficient residence time for crystal growing and transfer line blockage in conventional continuous mixed‐suspension mixed‐product removal (MSMPR) operations are still not well addressed. Periodic flow crystallization is a novel method whereby controlled periodic disruptions are applied to the inlet and outlet flows of an MSMPR crystallizer to increase its residence time. A dynamic model of residence time distribution in an MSMPR crystallizer was first developed to demonstrate the periodic flow operation. Besides, process models of periodic flow crystallizations were developed with an aim to provide a better understanding and improve the performance of the periodic flow operation, wherein the crystallization mechanisms and kinetics of the glycine‐water system were estimated from batch cooling crystallization experiments. Experiments of periodic flow crystallizations were also conducted in single‐/three‐stage MSMPR crystallizers to validate the process models and demonstrate the advantages of using periodic flow operation in MSMPR stages. © 2016 American Institute of Chemical Engineers AIChE J, 63: 1313–1327, 2017

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Section: Materials and Experimentsmentioning

confidence: 99%

Mathematical modelling and experimental validation of a novel periodic flow crystallization using MSMPR crystallizers

Rielly

Powell

et al. 2016

AIChE Journal

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“…There is a large number of flux limiter functions [26]. In this work the Koren [28] flux limiter was selected: (23) where r represents the so-called upwind ratio of two consecutive solution gradients [28]: (24) The domain was discretized using 200 elements. The process studied was crystallisation of paracetamol from ethanol solution in the 1 lit Labmax reactor and using the kinetics data from Mitchell et al [23].…”

Section: Case Study: Coupled Cfd and Population Balance Equationsmentioning

confidence: 99%

“…In this work the Koren [28] flux limiter was selected: (23) where r represents the so-called upwind ratio of two consecutive solution gradients [28]: (24) The domain was discretized using 200 elements. The process studied was crystallisation of paracetamol from ethanol solution in the 1 lit Labmax reactor and using the kinetics data from Mitchell et al [23]. An initial supersaturation was set to 1.5667, an initial temperature to 20 0 C, rpm to 150 rot/min, the radius of the stirrer to 2cm (4 blade paddle turbine used) and the impellers clearance was 50mm (approximately 1/3 of the height).…”

Section: Case Study: Coupled Cfd and Population Balance Equationsmentioning

confidence: 99%

“…It was assumed that the wall temperature was constant and equal to 0 0 C. The ideal mixing model coupled with the DPB equations was developed in Python using DAE Tools software [29]. The model equations are identical to the model equations presented in [23]. The only difference is that the method of moments equations were replaced by the DPB and that the temperature profile in the ideal mixing case was set to the calculated average temperature of the SPH+DPB case as presented in Fig.…”

Section: Case Study: Coupled Cfd and Population Balance Equationsmentioning

confidence: 99%

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Application of the Lagrangian meshfree approach to modelling of batch crystallisation: Part I—Modelling of stirred tank hydrodynamics

Nikolic

Frawley

2016

Chemical Engineering Science

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Crystallization phenomena in stirred reactors are influenced by local hydrodynamic conditions and these must be taken into account for successful process scale-up and optimization. This article is the first of two presenting the application of the Smoothed Particle Hydrodynamics (SPH) method to modelling of batch crystallisation in stirred tanks.The benefits of the Lagrangian meshfree methods were discussed and the SPH method was proposed as an efficient method for the rapid prediction of the global mean flow in stirred reactors. Various aspects of the simulation results were discussed such as quality of the fluid prediction, computational requirements and availability of the crystal size distribution without reconstruction. It has been shown that the computational requirements and accuracy of the fluid flow prediction can be controlled through the particle size and that the particles with a radius of 1.50-1.75mm in the reactor of 2.65 liters provide a good balance between the quality of the prediction and the computational requirements.The developed Smoothed Particle Hydrodynamics model was applied to a numerical solution of coupled computational fluid dynamics and discretised population balance equations to model a batch crystallization process. Due to the specific formulation of the SPH equations the resulting ODE system is solved using the weighted contributions rather than numerically by solving a linear system of equations. Therefore, a large number of additional transport equations resulting from the discretisation of the population balance leads to only a minor increase in computational requirements (around 60% for 200 equations). The non-idealities in the reactor result in non-uniform mixing and contribute to the dispersion of the CSD. The 1 effect of the hydrodynamics on the local temperature/supersaturation and the resulting crystal size distribution was captured and compared to the ideal mixing case.The developed SPH method served as a basis for the Part II of the series where a methodology for solution of population balance equations using high-resolution finite volume schemes or method of characteristics computed in parallel and independently from the Navier-Stokes equations is presented.

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“…This simulation was run for a total of 2hrs (7200s). The kinetics for growth were obtained from the work of Mitchell et al [14]. A constant agglomeration kernel of 10 -14 was used to introduce a skewing of the distribution.…”

Section: Dynamic Simulation Examplementioning

confidence: 99%

Particle size distribution reconstruction: The moment surface method

Hutton¹,

Mitchell²,

Frawley³

2012

Powder Technology

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The novel technique presented in this paper involves the pre-calculation of the moments of a pre-defined 2-parameter Probability Density Function (PDF) for a range of values of each parameter. This pre-calculation results in moment surfaces where the surfaces are a function of the two defining parameters. The intersection of constant moment contour lines (termed moment iso-lines) on these surfaces using simulation moment outputs results in the recovery of the defining parameters. Knowledge of the PDF and the total particle count or solids loading allows for the reconstruction of the full PSD. This technique proves to be very efficient which makes it ideal for the reconstruction of large numbers of distributions, for example in transient population balance models or model-based control algorithms, without the need for repeated application of optimisation algorithms.

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Estimation of the growth kinetics for the cooling crystallisation of paracetamol and ethanol solutions

Cited by 60 publications

References 17 publications

Mathematical modelling and experimental validation of a novel periodic flow crystallization using MSMPR crystallizers

Mathematical modelling and experimental validation of a novel periodic flow crystallization using MSMPR crystallizers

Application of the Lagrangian meshfree approach to modelling of batch crystallisation: Part I—Modelling of stirred tank hydrodynamics

Particle size distribution reconstruction: The moment surface method

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