Poster Collection 2014

Transcription

Poster Collection
2014
CMAC Posters 2014
Continuous Crystallisation ..................................................................................................................... 3
Investigation of Steady States of Concentration and Crystal Size in a Crystallisation from Melt
Juliet Adelakun* and Xiong-Wei Ni ............................................................................................................... 4
An Investigation Into Crystallization Kinetics In Batch STC and MB-OBC
Cameron Brown*, Natalia Falenta and Xiong-Wei Ni ........................................................................................ 5
Precise Temperature Control And Use Of Enthalpy Measurements During Crystallisation of
L-glutamic Acid In A Batch CoFlux Reactor/PAT Application In Hot Melt Extrusion (HME)
Natalia Dabrowska*, David Littlejohn and Alison Nordon .................................................................................. 6
Inducing Layered Solid Forms And Controlling Crystalline Defects In Multi-Component Continuous
Crystallization
Anneke R. Klapwijk*, Lynne H. Thomas and Chick C. Wilson ............................................................................... 7
Understanding Fouling Mechanisms In Continuous Crystallisation Processes
Fraser Mabbott*, Dimitrios A. Lamprou and Alastair Florence
........................................................................... 8
Laser-Induced Nucleation Development
Alasdair Mackenzie*, Andrew J. Alexander and Colin R. Pulham ......................................................................... 9
Design Approach for Moving from Batch To Continuous: Oscillatory Baffled Crystalliser (OBC)
Technology
Thomas McGlone*, Naomi Briggs, Vishal Raval, Craig Johnston and Alastair Florence
........................................... 10
An investigation into Parameters Affecting The Purity Of Crystals In OBC and STC
Hannah McLachlan* and Xiong-Wei Ni
....................................................................................................... 11
An Empirical Correlation For The Tube-side Nusselt Number For Oscillatory Flow In A Meso-Tube
With Smooth Periodic Constrictions
Iyke I. Onyemelukwe*, Chris D. Rielly and Zoltan K. Nagy ................................................................................ 12
Spherical Crystallisation of Ibuprofen
Francesca Perciballi*, Huaiyu Yang and Alastair Florence ................................................................................ 13
Monitoring and Control of Continuous and Periodic Flow Crystallization in MSMPR Using PAT and
An In-House Developed Information System Software (CryPRINS)
Keddon A. Powell*, Ali N. Saleemi, Qinglin Su, Chris D. Rielly and Zoltan K. Nagy .................................................. 14
Evaluation of Mixers for Operation in a Continuous Tubular Crystalliser
Karen Robertson* and Chick C. Wilson ........................................................................................................ 15
Monitoring Fouling in the Moving Fluid Oscillatory Baffled Crystalliser
Rachel Sheridan* and Jan Sefcik ................................................................................................................ 16
Establishment of Continuous Crystallisation Process in OBC Using Process Analytical Technologies
Humera Siddique*, Vishal Raval, Ian Houson, John Mack and Alastair Florence .................................................... 17
Towards Multi-component Crystallisation in a Continuous Flow Environment
Kate Wittering*, Sam Candy and Chick C. Wilson ........................................................................................... 18
1
Primary to Secondary Processing ........................................................................................................ 19
Process Analysis for Monitoring of Powder Drying
Denise Logue, Jaclyn Dunn*, David Littlejohn and Alison Nordon ...................................................................... 20
Continuous Spray Drying of ‘Novel’ Particles for Inhaled Drug Delivery
Rebecca Halliwell* and Alastair Florence
.................................................................................................... 21
Non-Invasive Monitoring of Powder Drying Processes by Acoustic Emission Spectrometry & Optical
Spectroscopic Techniques
Denise Logue*, David Littlejohn, Alison Nordon and Jaclyn Dunn ...................................................................... 22
Hot-Melt Extrusion for Bioavailability Enhancement of Poorly Soluble Drugs
Laura Martinez-Macros*, Dimitrios A. Lamprou and Gavin W. Halbert ............................................................... 23
Supply Chain ......................................................................................................................................... 24
How to Integrate Sustainability Metrics into the Overall Performance Measurement System of an
Organisation?
Georgi Aleksiev*, Umit Bititci and Kepa Mendibil
.......................................................................................... 25
Manufacturing Operations and Supply Chain Management Challenges in Continuous
Manufacturing
Jagjit Singh Srai, Tomás Harrington*, Leila Alinaghian and Mark Phillips ............................................................. 26
ICT-CMAC .............................................................................................................................................. 27
Identification of Particle Size and Shape Information From Multiple Sensor Measurements
(ICT-CMAC Work Package 2)
Okpeafoh S. Agimelen*, Jan Sefcik, Massimiliano Vasile, Anthony J. Mulholland .................................................. 28
ICT/CMAC Work Package 5: People and Processes
Blair Johnston and Murray Robertson* ....................................................................................................... 29
WP4: Plant-wide Modelling and Control – Mathematical Modelling and Optimisation of MultiSegment Multi-Addition Plug-Flow Crystalliser
Qinglin Su*, Chris D. Rielly and Zoltan K. Nagy
.............................................................................................. 30
2
Continuous
Crystallisation
3
INVESTIGATION OF STEADY STATES OF CONCENTRATION AND CRYSTAL SIZE IN A
CRYSTALLISATION FROM MELT
Juliet Adelakun* and Prof Xiongwei Ni
*jaa23@hw.ac.uk
School of Engineering and Physical Science, Heriot-Watt University, Scotland
MOTIVATION AND
KEY DRIVERS

Understanding the science of crystallisation from melt
(CfM)

Enhance and expand crystallisation science, technology
AIM
OBJECTIVES
CHALLENGES

Crystallisation from melt with high viscosity

Measurement difficulties for opaque solution
Difficulties in filtration
Difficulties in control of melt crystallisation

Limited study in a continuous platform

and control within CMAC

Develop operation and control for continuous CfM


Develop continuous filtration for CfM




Investigate the steady states of concentration and
crystal size in CfM
Evaluate and compare yield and purity with batch
operations
Establish control and operational protocols for CfM
Modelling and compare nucleation and growth
kinetics with these from solution crystallisation
INTRODUCTION—MODEL COMPOUND
PROPERTIES
FRACTIONS
GLYCEROL
triglycerides.
FATTY ACIDS
TRIGLYCERIDES

One of the most flexible vegetable oils

Contains a wide range of triglycerides

Each constituent fraction has different melting point

Separation is by fractionation
OLEIN
Liquid
MID-FRACTION
Soft-solid
STEARIN
Solid
Increasing saturation
Palm oil is the model compound, naturally-rich in
Increasing Melting point

PROJECT OVERVIEW
PROCESS ANALYSIS
CALIBRATION
SOLUBILITY
MSZW
BATCH
COOLING PROFILE
MIXING INTENSITY
ANALYSIS
PURITY
YIELD
CONTINUOUS

Fatty acids & Triglycerides content
Chromatography

Melting profile
Calorimetry

Solid fat content
Spectrometry

Iodine value
Titration

STEADY STATE
STABILITY

CURRENT WORK



Develop suitable method for iodine value (IV) analysis

Discourage continuous of titration method
Calibrate IR spectroscopy for IV analysis

Develop an olein/stearin solubility curve
Using online IR measurement





A measure of unsaturation in fatty acids
Measured as the amount (g) of the C = C present in the oil
sample consumed by a 100g of iodine compounds
The higher the value, the higher the degree of unsaturation
In a batch oscillatory reactor (OBC-MB)
Different mixing conditions at constant cooling rate
MSZW is independent of mixing conditions
Constant at ~20oC
FUTURE WORK
IMMINENT WORK
MSZW at different cooling rate and constant mixing conditions
FILTRATION STUDY

Yield and purity analysis of the resultant fractions
CONTINUOUS PROCESS


Steady state and stability study

MSZW in a stirred tank reactor (STC)

Crystal shape and size distribution

Of concentration and size profiles

Compare results from STC to OBC-MB’s

Separation methods

Of purity and yield
THE
AUTHORS WOULD LIKE TO THANK
EPSRC
EPSRC C ENTRE FOR I NNOVATIVE MANUFACTURING
C RYSTALLISATION FOR FUNDING THIS WORK
AND THE
4
IN
C ONTINUOUS M ANUFACTURING
AND
An investigation into crystallization kinetics in batch STC and MB-OBC
Dr Cameron Brown (cameron.brown.100@strath.ac.uk), Natalia Falenta, Prof. Xiong-wei Ni
Objective:
•
Evaluate nucleation and growth kinetics using adipic acid as the model compound
Previously…..
•
Good correlations between cooling rate and nucleation temperature from semi-empirical interpretations (Nývlt, Kubota and Sangwal) allowed nucleation
constants (kb & b) to be evaluated for the crystallization of adipic acid from water.
Application of 3D classical nucleation theory was inadequate for crystallization of adipic acid and sodium chlorate.
Distinct difference in trends between cooling in a STC and MB-OBC for adipic acid and sodium chlorate but not for urea.
•
•
Continuing from this:
•
•
•
Applied a rigorous interpretation based on population balance, leading to more accurate evaluation of nucleation kinetics and additionally growth kinetics.
A full factorial design of experiments model was implemented to study various STC and MB-OBC configurations and their effect on the estimated kinetics.
Estimated parameters consisted of: nucleation coefficient, kb, nucleation order, b, growth coefficient, kg and growth order g. Parameters were estimated by a
genetic algorithm to minimise the error between model and experimental data set. Each experimental data set consisted of three cooling rates, each repeated
three times.
Variation in solver estimated kinetic parameters are represented by the error bars shown below.
•
Despite initial
observations showing
changes in all
parameters with all
factors, the only
statistically significant
effect was found to be
the stirrer type on the
growth order, g.
8
6
6
4
4
2
0
8
3
2
2
1
1
0
0
SS
PVDF
Baffle material
8
6
6
4
4
2
2
0
ln (kb)/10
-ln (kg)
PTFE
Baffle material
4
3
3
2
2
1
1
ln (kb)/10
-ln (kg)
8
5
•
0
6
4
2
2
1
0
0
WF1
WF2
ln (kb)/10
-ln (kg)
8
Norm
WF1
WF2
5
6
3
4
2
2
1
WF2
0
8
•
b
g
4
WF1
SS
PVDF
Baffle material
4-turbine
SS
PVDF
Baffle material
2-paddle
Norm
WF1
Experiments of all stainless steel construction were utilized to
compare the estimated kinetic parameters of the STC and MBOBC.
• STC with 2-paddle and 4-blade turbine were compared
against a MB-OBC with and without a wall gap.
10
b
g
4
3
10
b
g
Stirred tank vs. Moving baffle OBC results
Initial observations would suggest that all changes in kinetic parameters
with waveform or material of construction are within the normal
variation of the solver. Confirmed by the full factorial model which
showed the only statistically significant effect was the influence of the
normal and reverse sawtooth waveform on the nucleation order, b
Stainless steel
5
Stirrer type
•
10
SS
PVDF
Baffle material
b
g
Stirrer type
Experiments carried out in a moving baffle OBC with three different
waveforms (normal, reverse sawtooth, WF1, and forward sawtooth,
WF2). Baffles were constructed of either stainless steel or PTFE.
Norm
4
SS
PVDF
Baffle material
4-turbine
•
0
5
b
g
0
SS
PVDF
Baffle material
0
0
SS
PVDF
Baffle material
2-paddle
Moving baffle OBC results
Norm
4
2
10
ln (kb)/10
-ln (kg)
4
5
b
g
3
SS
PVDF
Baffle material
10
5
ln (kb)/10
-ln (kg)
PTFE
PTFE
8
Stirrer material
Experiments carried out
in a baffled stirred tank
with two different types
of stirrer (2-paddle or 4blade turbine). Both
constructed of either
stainless steel or PTFE
• Baffles were made of
either stainless steel
or PVDF
Stainless steel
•
10
ln (kb)/10
-ln (kg)
Stainless steel
10
Stirred tank results
Stirrer material
•
ln (kb)/10
-ln (kg)
5
4
6
3
4
2
2
1
0
0
b
g
Only statistically significant effect was found to be the use of a
OBC with no wall gap on the nucleation coefficient, kb.
Observations and further work
WF2
•
Despite multiple repeats of experimental sets, only a few factors
showed a statistical effect on the estimate kinetics:
• Stirrer type on growth order, g.
• Waveform on nucleation order, b.
• OBC with no wall gap on nucleation coefficient, kb.
•
Does this apply to other crystallization systems? How does the
MF-OBC compare?
The authors would like to thank EPSRC and the EPSRC Centre for Innovative Manufacturing in Continuous Manufacturing and Crystallisation for funding this work. In addition they
would also like to thank Prof. Zoltan Nagy of Purdue University for his help in implementing the population balance framework.
5
Natalia Dąbrowska, David Littlejohn, Alison Nordon
Department of Pure and Applied Chemistry, University of Strathclyde,
Glasgow, Scotland
Precise temperature control and use of enthalpy
measurements during crystallisation of L-glutamic acid
in a batch CoFlux reactor
PAT application in hot melt extrusion (HME)
Aims of the project
Aims of the project
A project, which will be developed during the PhD, is to investigate
measurement techniques that can be used to monitor, optimize and
control the hot melt extruder (HME). This can improve the integration of
data from a variety of techniques to improve the modelling and control of
the process.
 Carry out crystallization of L-glutamic acid (LGA) in a 4 L CoFlux
reactor (Figure 1)
 Compare the crystal features (polymorph, particle size) to the results
obtained for LGA using other reactor types (STR, OBR)
 Assess if enthalpy measurement is an advantage of the CoFlux reactor
for monitoring the crystallisation of LGA
How to chose the PAT tools for HME3?
The following aspects need to be considered:
 If a chosen tool or combination of complementary tools will allow the
desired critical process and product parameters to be monitored
 If a chosen location for the implementation of the analysis will allow the
desired critical process and product parameters to be monitored
 If with the chosen conditions of measurements, the analyser will
provide useful data
CoFlux reactor1
 Designed to simplify the measurement of heat
(calorimetry)
 Jacket is a series of separate coils – multiple, small
heat transfer channels rather than a single jacket
 Prevents product damage from dry wall effects
 Improves yield through better temperature control
 Reduces energy consumptions
The challenges with PAT in hot melt extrusion
Raman measurements
 Difficulties with recording spectra from non-transparent melt (high
noise signal) – based on literature review
 Taking readings at multiple points along the extruder
Acoustic measurements
 Will signal be detected through the extruder material?
 Will different materials and forms of materials give a different acoustic
response?
Figure 1 Schematic
Co-Flux reactor2
Results
 No crystals were formed during the experiments where low
supersaturation (SS=2.1 and SS=4.5) was used, only for
supersaturation SS=6.5 did crystals appear
 Excellent temperature control permits a significantly higher yield
(≈80%) to be achieved than in the other types of reactor – stirred
tank reactor (STR) and oscillatory baffled reactor (OBR) (≈50%)
 Reactor allows achievement of different polymorphic form
(α/β (α>β)) compared with batch OBR (α/β) and STR (β)
 Application of very slow cooling rate (0.35°C/min) unexpectedly
produced pure alpha form of L-glutamic acid (Figure 2)
 The CoFlux reactor system is able to measure the actual adiabatic
power trend (Figure 3), which may be an indicator of nucleation
point
Application of passive acoustic measurements
 Scoping experiment to assess the usefulness of acoustic measurements
for HME was carried out with a Thermo® Process 11 twin-screw extruder
 The background signal was collected for 10 minutes, with the screws
speed 50 rpm (Figure 4a) then Kollidon VA 64 powder was added and
signal was collected for 1 minute (blockage of the instrument)
(Figure 4b)
a
b
Figure 4a Acoustic spectra for the background and 4b for the powder moving through the
barrel
Figure 2 X-Ray diffraction pattern of a product,
alpha and beta form of L-glutamic acid
Figure 3 Actual adiabatic power and
temperature trend
 Data obtained for the powder moving through the barrel clearly shows
a greater response than when no powder was present
Further work
Conclusions
 Verify if the acoustics measurements are
suitable for monitoring this process
 Design automated platform for Raman probe
to measure the product properties along the
barrel
 Develop standard measurement procedure
to carry out experiments
 CoFlux reactor produces a significantly higher yield of LGA crystals
 The product crystal size distribution is narrower when using the
CoFlux reactor, however crystals obtained in the CoFlux reactor are
bigger than in the other types of reactors
 Adiabatic power measurements may be an option for monitoring LGA
crystallisation, however it needs to be further investigated
Acknowledgments: David Morris from AM Technology, Kevin Pool from Autico,
EPSRC and the Doctoral Training Centre in Continuous Manufacturing and Crystallisation, CPACT
References:
[1] R. Ashe, The Chemical Engineering, Precision heating, July 2006, pp. 44-46
[2] AM Technology’s materials
[3] De Beer, T., et al., Near infrared and Raman spectroscopy for the in-process monitoring of pharmaceutical production processes. Int J Pharm, 2011. 417(1-2): pp. 32-47.
6
Inducing layered solid forms and controlling crystalline defects
in multi-component continuous crystallisation
Anneke R. Klapwijk1,2, Lynne H. Thomas2 and Chick C. Wilson2
1 - EPSRC Centre for Innovative Manufacturing in Continuous Manufacturing and Crystallisation at the University of Bath, 2 - Department of Chemistry, University of Bath, Bath, BA2 7AY
Email: a.r.klapwijk@bath.ac.uk
Background and aims
Targeting continuous crystallisation of layered and disordered functional materials and the control of these structural attributes for
Crystalline
Defect
Amorphous
optimising the physical properties, such as solubility1 and compressibility, as well as the processing behaviour of the bulk material2,3
-
Use of multi-component crystallisation and crystal engineering to design new solid forms using planar, symmetrical co-former
molecules to encourage the formation of layered or disordered structures
-
Assembling these systems effectively in the continuous environment and consistently producing specific solid forms
Multi-component layered solid forms of piroxicam
Piroxicam:trimesic acid solvate
• Piroxicam is a non-steroidal anti-inflammatory drug with low aqueous solubility
Solubility studies of piroxicam
complexes
• The API exists in its zwitterionic form in this structure which is characterised by
Solubility measurements of five piroxicam complexes
the yellow colour of the material
were carried out using the Avantium Crystal16 in
(a)
The two tautomers of piroxicam
methanol and ethanol and compared with the solubility
• A planar tetrameric unit of piroxicam and
trimesic acid molecules forms through
of piroxicam to determine whether solubility is
maintained or enhanced by complexation.
(c)
hydrogen bonding interactions (a)
Methanol
• The tetrameric units link to form sheets
Piroxicam:
Imidazole:
MeCN (1:1:1)
• Acetonitrile molecules sit inside discrete
pores within each sheet (b)
Piroxicam
• The sheets stack in layers held together
(b)
by weak interactions (c)
• Layered materials can have beneficial
physical properties
Thermal analysis of solvated forms of piroxicam:trimesic acid
Ethanol
Piroxicam:
Imidazole:
MeCN (1:1:1)
Piroxicam
(a) 31 °C
(b) 160 °C
(c) 243 °C
DSC correlates with HSM images showing a broad endotherm between
140 and 180 °C suggesting loss of acetonitrile. This pattern is also seen
for crystals grown from other solvents such as acetone.
Desolvation in the acetone
solvate was achieved by
heating to 170 °C at 1 °C
min-1.
Further PXRD and
DSC studies suggest that
re-uptake of the solvent
can be achieved by vapour
diffusion.
Polar Bear Plus Crystalliser
Enhanced solubility is found for most piroxicam
complexes, e.g. the piroxicam:imidazole solvate:
• In methanol 3.5 mg/ml from 3.1 mg/ml for pure
piroxicam at 25 °C
• In ethanol 1.5 mg/ml from 1.4 mg/ml for pure
piroxicam at 25 °C
• Solubility enhancement increases with temperature
• No obvious enhancement of solubility in water as
yet
Next steps…
Initial transfer of existing systems into cooling crystallisation • Continue initial cooling crystallisations of current and new systems in the Polar Bear
using the Polar Bear Plus crystalliser from Cambridge
Plus crystalliser and new small-scale continuous crystallisers that are ideal for such
Reactor Design.
multi-component studies
• Programmable for controlled heating
• Obtain the solvent-free complex of piroxicam:trimesic acid for a safer formulation and
and cooling in the range of -40 to 150°C
investigate hydration properties of this system
• Magnetic stirring
• Understand the homogeneity of the level of disorder in the bulk of 5-chlorouracil and
• Flexibility in scale with interchangable
maintain on transfer to continuous crystallisation
sample holder units for 2ml, 20ml,
• Identify new layered systems with physical property enhancement
100ml, 250ml and 500ml
1. A. Saleki-Gerhardt. et al., Int. J. Pharm., 1994, 101, 237-24
2. S. R. Byrn et al. Chem. Mater., 1994, 6, 1148-1158
7
3. R. C. B. Copley et al. Cryst. Growth Des., 2008, 8, 3474-3481
Understanding Fouling Mechanisms in Continuous Crystallisation Processes
Fraser Mabbott*, Dimitrios Lamprou, Alastair Florence
Fouling Mechanisms
Introduction to Fouling
• Fouling (or encrustation) is described as the unwanted
formation of deposits on a surface.
• A process which is not fully understood however authors
have proposed mechanisms (Vendel and Rasmuson)1
involved in encrustation.
• Consequences range between two extremes ranging from a
minor reduction in heat transfer to complete blockage.
• On the continuous paradigm, one factor influencing the
uptake of continuous crystallisation is the susceptibility of
encrustation/full plugging within equipment as expressed by
Schaber and co-workers2.
• There has been experience within the research centre of
encrustation within continuous crystallisation platforms
Figure 1 –
(Figure 1).
Figure 2 – Adapted figure from Geddert et al.3 highlighting key transfer processes and factors involved in
crystallisation fouling. There is great impetus to investigate the interplay between these factors to obtain a better
understanding into the fundamental processes involved in crystallisation and encrustation.
Encrustation on the
walls of an oscillatory baffled
crystalliser (OBC)
Investigate the effect of different materials of
construction (MOC) upon crystallisation fouling
Current Research
Topics
Introduction
• The influence of surfaces and their properties have been a main research area
within industrial crystallisation and are acknowledged to affect a number of
crystallisation processes e.g. nucleation, polymorphism.
• Research by Aizenberg et al.5 investigated SAMs with different functional
groups and demonstrated that COOH and OH presented strong crystal
orientation control whilst CH3 inhibited the crystallisation of calcite highlighting
the effect of different surface chemistries upon crystallisation events4.
• The present research topic investigates glass and altering its surface chemistry
as a means of mitigating crystallisation fouling.
Method
Introduction
• Within the pharmaceutical/fine chemicals industries, a variety of MOC are
utilised in manufacturing processes including crystallisation.
• The importance of MOCs in crystallisation processes has been highlighted by
Liang et al.4 who investigated the materials Perspex and stainless steel and
established that MOC have an influence upon both primary and secondary
nucleation in addition to notable differences in encrustation between MOC. Also
the properties of a material e.g. roughness, hardness are acknowledged to
influence crystallisation processes.
• This research investigates a variety of different MOC including glass, stainless
steel, polytetrafluoroethylene (PTFE), polyetheretherketone (PEEK), MACOR®
and Hastelloy®. The present research topic explores the properties of MOC
(through characterisation) and their importance to encrustation.
Method
A range of analytical techniques have been proposed (Table 1) to i) characterise
MOC and also ii) characterise the development of encrustation.
Method
• L-glutamic acid/ water (4g/100ml solution)
• Paracetamol/water (5g/100g solvent)
• A 250ml stir tank reactor (STR) was used for crash cooling crystallisation
(Figure 4a)
• A glass rod was submerged vertically (4cm) into the STR (Figure 4b) and
held at a fixed supersaturation for two hours using a programmed
temperature profile (Figure 4c) – the rod was then taken out and allowed
to dry.
Information
Atomic Force Microscopy (AFM)
Nanoscale surface topology, roughness, hardness
Raman mapping
2D rastering to assess chemical uniformity, also physical form
Contact Angle Goniometry (CAG)
Contact angle and surface energies
Time-of-Flight Secondary Ion Mass Spectrometry (TOF-SIMS)
Molecular composition at nanoscale
X-ray Photoelectron Spectroscopy (XPS)
Chemical characterisation at surface
Transmission Electron Microscopy (TEM)
• Bulk induction times, temperature profiles and encrusted mass were
determined.
• Characterisation methods
Surface defects, morphology
XRPD
Crystal orientation
Imaging
Morphology, rate of growth
Investigate surface chemistry modifications as a
means to mitigate crystallisation fouling
Table 1 – Characterisation techniques
Initial Results
Crash Cooling Temperature Profiles
90
85
80
LGA/Water
Paracetamol/Water
Temperature (OC)
75
70
65
60
55
50
45
a
b
40
c
35
30
a
d
e
g
h
b
-20
c
0
20
40
60
80
100
120
140
Time (Minutes)
Figure 4 – a) Crystalliser set up b) internal crystalliser set up including PTFE impeller, PTFE temperature
probe and the vertically positioned glass rod and c) crash cooling temperature profiles for both
solute/solvent systems
f
Initial Results
Figure
3 – (a
and b) wettability
representations of PTFE and PEEK,
respectively; (c) Calculated surface energy
values for MOC with literature comparisons;
(d-h) AFM images of glass, MACOR®, PEEK,
stainless steel and PTFE, respectively.
a
Next Steps
• Assess and confirm analytical techniques for surface characterisation and
additionally for fouling characterisation
• Raman mapping – determine capabilities and limitations.
• Flow cell development – examine encrustation under defined hydrodynamics
and employ suitable analytical techniques for characterisation.
b
c
Figure 5 – LGA/water results; a)encrusted rod after fouling run with determined encrusted mass
b)determined temperature profile (run 1 and 2) with determined bulk induction times c)calibration
masses for LGA/water
Next Steps
• Conduct chemically treated glass experimentation (functional groups to be
investigated – OH, NH2 and CH3.
• Conduct experimentation with different levels of supersaturation and stir rate
(shear).
• Physical and chemical surface characterisation.
Acknowledgements
I would like to express my gratitude to Dr Thomas McGlone for his input into my research
and its development. Additionally I would like to acknowledge Dr Jerry Heng for his
contribution to my project. I would also like to thank EPSRC for funding.
1.
2.
3.
4.
5.
Correspondence:
Professor
Alastair
Florence
(alistair.florence@strath.ac.uk);
Fraser
Mabbott
(fraser.mabbott@strath.ac.uk)
EPSRC Centre for Innovative Manufacturing In Continuous Manufacturing and Crystallisation, Strathclyde Institute of
Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow
8
References
Vendel, M.; Rasmuson, A. C. Chem Eng Res Des 2000, 78, 749.
Schaber et al. Ind. Eng. Chem. Res 2011, 50(17), 10083–10092.
Geddert, T.; Augustin, W.; Scholl, S. Heat Transfer Eng 2011, 32, 300.
Liang, K. P.; White, G.; Wilkinson, D.; Ford, L. J.; Roberts, K. J.; Wood, W. M. L. Cryst Growth Des 2004, 4, 1039.
Aizenberg, J.; Black, AJ.; Whitesides, GM. J. Am. Chem. Soc., 1999, 121, 4500.
160
Laser-Induced Nucleation Development
Alasdair M. Mackenzie, Andrew J. Alexander, Colin R. Pulham
EASTChem School of Chemistry, Joseph Black Building, West Mains Road, Edinburgh, Scotland EH9 3JJ
Aims
1. Continuous NPLIN - Demonstrate continuous
nucleation control with NPLIN
2. Screening - Find a pharmaceutical compound
that works under NPLIN
3. Polymorphism control - Develop polymorphic
control with NPLIN
4. Size Control - Develop size control with NPLIN
5. Other projects - Understanding
NPLIN
Laser pulse
Non-Photochemical L a s er
Induced Nucleation offers a
way of controlling nucleation
in time and space for a
number of chemical systems.
Controlled nucleation offers
the possibility of better
understanding nucleation and
enabling
crystal
growth
control.
Screening
5s

Paracetamol Sulfamerazine Malonamide
A simple Raman
spectrometer schematic
wa s fo l l o w e d a n d
a working spectrometer
built for < £1000 and
borrowed parts. Although
it is not able to resolve
polymorphs as yet, an
upgraded spectrometer
would. An estimated full
cost of this customizable
Raman microscope is £11k
vs. £80k commercial
system.
Next Steps
With the RPIF money I hope to get
equipment to build a small test COBC setup
in Edinburgh, imaging equipment to study
nucleation in detail using the spatiotemporal control NPLIN gives us and
upgrade the Raman spectrometer to full
functionality.
Studies into how NPLIN affects size in
batch will soon start as well as further
development of the NPLIN – flow test rig.
Scanner
images
over
time
Scanner setup with LED
lights and thermometer for
automatic image taking with
recorded temperature on
scan image.
Polymorph Detection
In order to study the effect LIN
has
on
polymorph
fo rm at io n a wa y to
differentiate the form of crystal
grown is needed. Although
XRPD is a great way of
measuring the presence of all
polymorphs in the sample, it
requires destruction of the
solution to recover the solid.
Raman spectroscopy offers a
non-invasive way of screening
polymorphs in situ.
60 s
       
      

   
nucleation starts only when pulsed laser
turned on and stops after it switches off.
This is cycled to demonstrate the principle.
The crystals are imaged downstream using
a laser pen to highlight smaller crystals.
 
Use smaller vials with
PTFE caps to prevent
accidental seeding and
enable faster cooling
rates for more stable
solutions and faster
experiments.
Choose compounds with
high enough solubility
and easily identifiable
polymorphs.

• Cool supersaturated solutions
• Avoid spontaneous nucleation
• Test under laser conditions
Flow

Want to replace
s ca n n er
a u to m at io n
w it h ca m era
automation for
better quality
images. Learn
lessons
from
scanner setup.
Camera
Image
Comparison of
grating resolutions
C. Mohr, C. L. Spencer, and M. Hippler,
J. Chem. Educ., 2010, 87, 326–330.
Comparison with literature shows need of higher resolution

Improve the
spectrometer,
fibre optic laser
delivery, COBC
integration.
Acknowledgments
Work performed by
Alasdair Mackenzie
More screening needs to be done of
compounds to establish patterns of
understanding of chemical properties to
approach understanding of the NPLIN
process. With development of screening
techniques and automation, it should be
possible to do this simultaneously with
other research.
Under advice and supervision of
Pro f. Co lin Pu lh a m
and
Dr. An d rew Alexa n d er
Special thanks to Martin Ward
and the CMAC community
9
Fu n d in g
provided
by EPSRC
Thomas McGlone*, Naomi Briggs, Vishal Raval, Craig Johnston and Alastair Florence
The stirred tank reactor has been the work-horse of the chemical industry for centuries:
Drive to develop lab-scale continuous crystallisation platforms:
15 mm
10 mm
The benefits of looking towards continuous solutions are clear:
d0
(m)
4.5
10
15
Flow rate
(m3 min-1)
10
50
100
Reactor length
(m)
20
20
20
Sharp-edge baffles
vs smooth periodic
constrictions (SPCs)
Residence time
(mins)
30
30
35
Volume
(ml)
300
1500
3500
𝐴𝐴 = 𝜋𝜋𝑟𝑟 2 𝛼𝛼
Operation conditions with increasing column diameter
For pharmaceuticals and fine chemicals continuous is NOT necessarily targeted at large
scale (8000 tpa) but may be applicable to smaller demands (3 - 20 tpa)
One of the key stages in the production of active pharmaceutical ingredients (APIs) and fine
chemicals is crystallisation:
Key scale-up parameters
Crystallisation involves a range of challenges:
Purity
Size/shape
Polymorphism
Yield
Optimise baffle spacing, L and baffle open cross sectional area, α using PIV
• Solubility data, MSZW, working concentration range
• Solvent compatibility/safety issues
• Nucleation and growth rate data
In order to succeed one has to optimise the process variables
whilst understanding the crystallisation basics: nucleation theory,
growth rates and mechanisms, agglomeration…
• Seeded/unseeded, attrition, agglomeration, polymorphism
• System stability
• PAT – what has been used and what would be beneficial
• Predicted residence time
• Initial comparison between oscillatory and stirred mixing
• Treatment of type II diabetes
• Define minimal oscillatory conditions for particle suspension
• Global demand approaching 85,000 tpa
• Identify process issues such as encrustation
• Driver for continuous…
Metformin hydrochloride
• Linking workup to crystallisation…
• Physical data – densities, viscosities available, dynamic slurries?
• Determine suitable flow rates based on predicted residence time AND oscillatory Reynolds
number/net flow Reynolds number ratio
Known impurities:
• Demonstration of process enhancement – completion time, yield, reproducibility?
• The poor solubility of metformin hydrochloride in most organic solvents limits working
solvents to alcohols and mixtures of miscible organic solvents with water
• Realisation of improved product quality via continuous crystallisation – particle attributes:
CSD, filterability, control of agglomeration, polymorph selection?
Apply standard workflow approach to deliver the process…
Correspondence:
Professor Alastair Florence (alastair.florence@strath.ac.uk); Dr. Thomas McGlone (thomas.mcglone@strath.ac.uk)
EPSRC Centre for Innovative Manufacturing in Continuous Manufacturing and Crystallisation, Strathclyde Institute of Pharmacy & Biomedical Sciences, University of Strathclyde, Glasgow
10
An investigation into parameters affecting the purity of crystals in OBC and STC
CMAC PhD student: Hannah McLachlan
Supervisor: Prof Xiong-Wei Ni, School of Engineering & Physical Science,
Heriot-Watt University, Edinburgh (www.cobra.hw.ac.uk)
Introduction and Objectives
• Generally, in cooling crystallization the product purity obtained decreases as the cooling rate and mixing intensity increase [1].
• Confidential results obtained show that crystals produced in the Oscillatory Baffled Crystallizer (OBC) gave higher purities than those produced in the traditional Stirred Tank
Crystallizer (STC) at the same condition.
• The main objective of this work is to verify and seek scientific explanations for the purity deviations and to explore ways of improving purity
Apparatus
Compound and Supersaturation
A moving baffle OBC (mb-OBC) with two stainless steel baffles was used.
In the STC, a stainless steel two-blade flat-paddle impeller was used, along with four
stainless steel wall baffles
Model compound of urea with an initial purity of 90 % is used, which is dissolved in
distilled water before re-crystallization.
Solution concentration of 1.33 g/ml is used for both vessels – giving a 5 wt.%
supersaturated solution at 30 oC.
How Accurately Could Purity be Measured
•
•
•
•
•
Sample filtration and washing is carried out in a ‘hot-box’ to minimise external temperature effects on the purity.
Washing was completed using two 50 ml isopropanol washes, to remove any remaining mother liquor.
Samples were dried in an oven overnight, to ensure consistent drying conditions.
FTIR solution analysis was carried out to find the concentration and consequently the purity of each sample.
Calibration was completed using very high purity urea (99.5 %) at different concentrations.
Table 1 - % Purity Values
Cooling Rate (oC min-1)
0.25
% Purity + Standard Error
Mixing Intensity
(W m-3)
0.50
0.75
mb-OBC
STC
mb-OBC
STC
mb-OBC
STC
20
94.9 + 0.24
94.4 + 0.32
95.3 + 0.32
94.5 + 0.84
96.2 + 0.29
95.6 + 0.36
170
96.0 + 0.54
95.8 + 0.22
95.8 + 0.12
94.8 + 0.69
96.9 + 0.54
95.4 + 0.42
750
97.2 + 0.26
96.6 + 0.35
96.4 + 0.05
95.9 + 0.44
97.1 + 0.17
96.5 + 0.03
Initial Results
• At all cooling rates and mixing intensities, the mb-OBC gave higher purity crystals than the STC (Table 1) – which agrees with previous unreported trials.
• For both the mb-OBC and the STC, the purity obtained increased as the mixing intensity was increased, within the range of applied conditions.
Why Purity Changes
a) Could Improved Mixing in the OBC be the reason for the Purity Improvement?
Statistically similar pairs can be seen between a higher mixing in the STC and a lower mixing in
the mb-OBC, at all fixed cooling rates (Arrows on Table 1).
mb-OBC
SEM imaging (Figure 1) suggests comparisons are due to similar crystal agglomeration levels and
crystal size. These images also suggest that the mb-OBC has lower levels of agglomeration than
the STC at the same conditions.
More agglomeration, could trap more impurity within the growing crystals, possibly leading to a
lower overall purity value. The higher levels of agglomeration in the STC, could be leading to the
lower purity values noted.
20 W/m3
170 W/m3
750 W/m3
STC
The higher agglomeration levels are possibly due to the poorer mixing within the STC compared
to the mb-OBC, allowing more time for bonds to form between growing crystals .
Figure 1 – SEM Imaging at Fixed Cooling of 0.5 oC/min
Table 2 – Rates of Supersaturation Generation and Depletion
(× 10-5)
Supersaturation
De-Supersaturation
Cooling Rate
mb-OBC
STC
mb-OBC
STC
0.5 oC min-1
1.85
1.13
2.68
1.99
0.75 oC min-1
4.46
2.84
9.52
4.53
b) Could the Rates of Supersaturation Generation and Depletion be Different?
Slower rates of supersaturation generation and depletion are observed in the STC (Table 2),
could be leading to higher agglomeration formation by allowing more time for crystal bonds
to form.
Temperature attainment in STC takes longer, due to poorer mixing and heat transfer [2],
especially evident at higher cooling rates, possibly affecting the supersaturation rates obtained
Future Work
Bibliography
Repeat STC work with retreat curve impeller, investigating effect improved STC mixing on purity.
Carry-out OBC work in moving fluid rig, to see if purity variations could occur.
[1] Givand, J. C. et al. (1999). Journal of Crystal Growth 198-199, Part 2(0): 1340-1344.
[2] Ni, X. et al (2003). Chemical Engineering Research and Design 81: 373-383
Repeat certain conditions with 5 wt.% added impurity to see effect of larger amount of added
impurity within the system.
11
An empirical correlation for the tube-side Nusselt number for oscillatory
flow in a meso-tube with smooth periodic constrictions
I.I. Onyemelukwe1*, C.D.Rielly1, Z.K. Nagy1,2
of Chemical Engineering, Loughborough University, Loughborough, LE11 3TU, UK.
2School of Chemical Engineering, Purdue University, USA.
*Email: I.I.Onyemelukwe@lboro.ac.uk
Introduction
The heat transfer behaviour exhibited by the meso-tube can be attributed to:
 The different baffle type and geometric parameters creating very different fluid
dynamics in the tube.
 Over-extending the correlation to operating conditions far below the
experimental range at which it was determined.
Heat transfer efficiency in tubular systems is described by the dimensionless
Nusselt number (𝑁𝑁𝑁𝑁𝑡𝑡), which is the ratio of convective to conductive heat transfer
across the fluid boundary layer. Conductive heat transfer in tubes is limited by the
available surface area, however, it is possible to enhance the rates of heat transfer
by applying oscillatory flow to tubes which are periodically baffled. This promotes
turbulent conditions and improves radial mixing in tubes subjected to low net flow
velocities. Mackley & Stonestreet (1995) developed a correlation for predicting the
tube-side Nusselt number for oscillatory flow in a periodically baffled tube (see
Figure 2) as a function of net flow (𝑅𝑅𝑅𝑅𝑛𝑛) and oscillatory flow (𝑅𝑅𝑅𝑅𝑜𝑜) Reynolds
numbers (with no effect of Strouhal number), based on data covering a range of
operating conditions (𝑅𝑅𝑅𝑅𝑛𝑛 = 100 – 1200; 𝑅𝑅𝑅𝑅𝑜𝑜 = 300 – 800) (see Figure 1):
9.0
Measured
8.0
7.0
6.0
5.0
5.0
4.0
4.0
3.0
𝑅𝑅𝑅𝑅𝑛𝑛 +800 1.25
3.0
2.0
2.0
1.0
1.0
0.0
0
50
100
150
200
Oscillatory Reynolds number, Reo
250
Fig. 4. Tube-side heat transfer as a function of oscillatory
Reynolds number. 𝑅𝑅𝑅𝑅𝑛𝑛 = 22, 𝑆𝑆𝑡𝑡 = 0.8.
9.0
Measured
8.0
250
9.0
Measured
Mackley Stonestreet
Nusselt number, Nut
Nusselt number, Nut
5.0
4.0
4.0
3.0
Experiments were carried out to determine the tube-side Nusselt number for a
previously unexplored experimental range of 𝑅𝑅𝑅𝑅𝑛𝑛 = 10 – 50; 𝑅𝑅𝑅𝑅𝑜𝑜 = 39 – 197; 𝑆𝑆𝑡𝑡 =
0.8 in the smooth periodic constriction (SPC) meso-tube. For all experiments,
 The annular-side water flow rate was maintained constant, and the annular-side
heat transfer coefficient (ℎ𝑠𝑠) was estimated using the Dittus Boelter turbulent
flow correlation for a 𝑅𝑅𝑅𝑅𝑛𝑛 of 4781:
ℎ 𝐷𝐷
𝑁𝑁𝑁𝑁𝑠𝑠 = 𝑠𝑠 ℎ = 0.023𝑅𝑅𝑅𝑅 0.8 𝑃𝑃𝑃𝑃 0.3
50
100
150
200
6.0
5.0
Methodology
0
7.0
6.0
Fig. 2. Geometries of the SPC meso-tube and the
sharp-edged annular baffled tube.
0.0
8.0
Mackley Stonestreet
7.0
Fig. 1. Experimental and predicted tube -side heat transfer
by Mackley & Stonestreet (1995).
Mackley Stonestreet
7.0
6.0
𝑅𝑅𝑅𝑅𝑜𝑜2.2
Measured
8.0
Mackley Stonestreet
Nusselt number, Nut
𝑁𝑁𝑁𝑁𝑡𝑡 = 0.0035𝑅𝑅𝑅𝑅𝑛𝑛1.3 𝑃𝑃𝑃𝑃 0.3 + 0.3
9.0
Nusselt number, Nut
1Department
3.0
2.0
2.0
1.0
1.0
0.0
0.0
0
50
100
150
200
250
0
50
100
150
200
250
Empirical Correlation
𝜅𝜅2
3.5
 Tube-side inlet temperature (𝑇𝑇1𝑖𝑖𝑛𝑛), tube-side outlet temperature (𝑇𝑇1𝑜𝑜𝑁𝑁𝑁𝑁), and
constant annular-side temperature (𝑇𝑇𝑤𝑤) were measured at steady-state.
𝑇𝑇 +𝑇𝑇
 𝐶𝐶𝑝𝑝, 𝜅𝜅, µ, 𝜌𝜌, 𝑃𝑃𝑃𝑃, 𝑅𝑅𝑅𝑅𝑛𝑛 , 𝑅𝑅𝑅𝑅𝑜𝑜 were calculated for the bulk temperature 1𝑖𝑖𝑛𝑛 1𝑜𝑜𝑜𝑜𝑜𝑜 of
2
the water across the length of tube.
 Negligible heat loss to the surroundings was assumed.
Smooth Tube
Baffled Tube
Re = 39
3
Methodology
o
Tube side Nusselt number, Nut
Reo = 79
Methodology
Re = 118
o
2.5
Reo = 157
Re = 197
o
2
Constricted Tube
1.5
Smooth Tube
1
Mackley & Stonestreet correlation
0.5
0
10
15
20
25
30
35
Net Flow Reynolds number, Re
40
45
50
n
Fig. 8. Experimental and predicted tube-side heat transfer as a function of both Ren and Reo for the SPC mesotube.
A correlation was fitted to the obtained experimental data (see Figure 8) using Nonlinear Least Squares method with 𝑅𝑅2 = 0.98, and 95% confidence bounds. The
obtained correlation is:
𝑁𝑁𝑁𝑁𝑡𝑡 = 0.01616𝑅𝑅𝑅𝑅𝑛𝑛 1.16 𝑃𝑃𝑃𝑃 0.3 + 0.0016 𝑅𝑅𝑅𝑅𝑜𝑜0.08 𝑅𝑅𝑅𝑅𝑛𝑛1.42
Fig. 3. Set-up for heat transfer experiments.
The overall heat transfer coefficient (𝑈𝑈0) and the tube-side Nusselt number were
calculated from:
𝑚𝑚̇𝐶𝐶𝑝𝑝 ∆𝑇𝑇1 1
1
κ1 1
𝐷𝐷1𝑖𝑖
𝐷𝐷1𝑖𝑖 ln(𝐷𝐷1𝑖𝑖 /𝐷𝐷1𝑜𝑜 )
𝑈𝑈0 =
,
=
−
−
𝐴𝐴
∆𝑇𝑇𝑙𝑙𝑙𝑙
2κ𝑔𝑔
𝑁𝑁𝑁𝑁𝑡𝑡 𝐷𝐷1𝑖𝑖 𝑈𝑈0 𝐷𝐷1𝑜𝑜 ℎ𝑠𝑠
The first term of the correlation describes a stronger steady-flow contribution to
heat transfer, while the second term describes a smaller effect of superimposed
oscillation than suggested by the Mackley & Stonestreet correlation.
Results
Conclusions
Application of the Mackley & Stonestreet correlation to predict the heat transfer
performance of the SPC meso-tube resulted in Nusselt numbers that were not in
good agreement with experimental observations (see Figures 4 – 7). Experimental
data indicated that oscillations do not strongly enhance heat transfer in the SPC
meso-tube, as otherwise suggested by the Mackley & Stonestreet correlation.
• With increasing net flow, the presence of oscillations has a growing effect on
heat transfer in the SPC meso-tube, and a diminishing effect in the annular
baffled tube.
• Heat transfer enhancement by oscillatory flow is affected by geometric
parameters, and should be specifically determined for different tube designs.
12
55
Spherical Crystallisation of Ibuprofen
Francesca Perciballi, Huaiyu Yang and Alastair Florence
Simultaneously crystallisation and
agglomeration
Droplets
size
7% ibuprofen,
2K/min, 100mL
Exp. 3 300 rpm
Exp. 5 400 rpm
Exp. 7 500 rpm
Improve micromeritic
properties and
dissolution rate
Direct
tableting
Stirring
rate
2K/min, 100mL,
400rpm
Exp. 4 4% Ibuprofen
Exp. 5 7% Ibuprofen
Exp. 6 14% Ibuprofen
• Tailoring product by controlling the process conditions
• Investigate the mechanism of spherical crystallisation
Ibuprofen
Concentration
Water
Cooling
rate
7% ibuprofen
100mL, 300rpm
Exp. 1* 0.3K/min
Exp. 2 1K/min
Exp. 3 2K/min
Region 2
Dispersed phase
Region 4
Region 4
Region 4
Region 4
Crystals
Region 3
Exp. 3
Exp. 4
Exp. 5
Region 3
Exp. 6
Exp. 7
00
00
30
00
00
80- 160 m
45 min
0
30 min
15
20- 40 m
10
40- 80 m
>160 m
0
0 min
15 min
45
20
Time
 From 50 °C to 20 °C solution of ibuprofen goes
from region 2 (above 40 °C), through region 4
(about 39 -37 °C) to region 3 (below 35 °C)
 Particles in all size range decrease at
nucleation
 Number of small crystals decreases while
number of big crystal increase
Spherical particles photos in petri dishes and 4X microscope images
Droplets size distribution
1000
800
Counts (No Weight)
00
Temperature
Exothermic peak: nucleation
Counts (No Weight)
60
Temperature
Flowability
Continuous phase
Desirable
Ethanol
Exp. 2
Volume
7% ibuprofen
2K/min, 400rpm
Exp. 5 100 mL
Exp. 8 500 mL
Exp. 9 1000 mL
75
0- 20 m
50
30
Compress
-ibility
40°C
Exp. 1
40
Spherical
particles
Temperature
Ternary Phase Diagram
37°C
Determination:
Adding the ibuprofen, water
or ethanol into solution, the
boundary curves can be
Ethanol
Water
determined by observing
phase transformation
35°C
between: clear solution
(region 1), solid in clear
solution (region 3 or 5),
liquid-liquid phase
Exp. 6
separation solution
Exp. 4 Exp. 5
(region 3), solid in
Water
Ethanol
liquid-liquid phase
Ternary
diagram
of
ibuprofen
in
water
and
ethanol
mixture,
separation solution
determined at 35 °C and extrapolated at 37 °C and 40 °C
(region 4).
60
Particle
shape
Emulsion
Strength
Continuous spherical crystallisation of poorly soluble drugs
Region 1 (Liquid Phase)
Region 2 (Liquid-liquid Phase)
Region 3 (Solid-liquid Phase)
Region 4 (Solid-liquid-liquid Phase)
Region 5 (Solid-liquid Phase 2)
Particle
size
400
Conclusion:
Exp. 7
150
Exp. 5
100
Exp. 3 Exp. 5
200
Particles size distribution
200
Exp. 7
600
250
50
Exp. 3
• Higher stirring rate lower droplets size and
particles are a little bigger at 400 rpm than 300
and 500 rpm
• Higher concentration of the ibuprofen induces
more agglomeration after drying
Particles size distribution
Particles size distribution • Particles obtained at cooling rate of 1K/min show
200
1200
greater agglomeration than higher and lower
Exp. 1
Exp. 6
150
cooling rates
900
0
1
10
100
1
10
100
1000
100
600
300
0
0
1000
1
50
Exp. 5
Exp. 4
10
100
1000
1
10
Size of particles (μm)
References: Kawashima, Y. et al. Powder technology 2003, 130, 283-289.
*: lower concentration of ibuprofen
Acknowledgement: Dr. Thomas McGlone, Vishal Raval and EPSRC CMAC for funding.
Future work:
Exp. 3 Exp. 2
0
100
1000
Experiments on MFOBC and COBC at 400rpm, under
1K/min cooling with 14% ibuprofen
Correspondence: Prof. Alastair Florence (alastair.florence@strath.ac.uk);
Dr. Huaiyu Yang (huaiyu.yang@strath.ac.uk);
Francesca Perciballi (francesca.perciballi@strath.ac.uk).
13
Monitoring and Control of Continuous and Periodic Flow
Crystallization in MSMPR Using PAT and an In-house
Developed Information System Software (CryPRINS)
*K.A.
Powell1, A.N. Saleemi1, Qing-Lin Su1, C.D. Rielly1, Z.K. Nagy1,2
1Department
of Chemical Engineering, Loughborough University, LE11 3TU, UK;
of Chemical Engineering, Purdue University, West Lafayette, IN. 47907, USA
*K.Powell@lboro.ac.uk
2School
ABSTRACT
METHODS
Measurement & Analysis
PAT and information systems are rarely used to monitor and control continuous
crystallization processes. In this study an integrated array of PAT sensors and
an in-house developed information systems software tool, CryPRINS were
combined and used within an Automated Intelligent Decision Support
Framework (IDS) to monitor and control the continuous and periodic flow
crystallization of the pain and fever relieving drug paracetamol. Studies were
carried out in a MSMPR crystallizer. Results indicate that the periodic flow
process attains steady-state more rapidly compared to the continuous process.
Furthermore, the periodic process did not suffer from the fouling, encrustation
or line blockage issues that were encountered in the fully continuous process.
The results further illustrate that the use of integrated PAT and CryPRINS
within an IDS can indicate when steady-state is reached, and also provides a
better understanding of the parameters and operating procedures that
influence both the continuous and periodic flow crystallization process.
M-P1
(S = 1.02)
Q1
QM1
(S = 1.24)
(46.69 g/min)
2
1
MSMPR
Filtration
Product
at 10 oC
Q1
(37.34 g/min)
(46.69 g/min)
Waste Stream
4
3
5
(9.35 g/min)
0.126 g PCM/min
With Recycle
Feed
1
MSMPR 1
Q1
(S = 1.24)
2
MSMPR 2
QM1
(S = 1.11)
Filtration
3
Product
at 10 oC
at 15 oC
(46.69 g/min)
(46.69 g/min)
4
QM2
5
(46.69 g/min)
Waste Stream
Periodic Flow Two-stage MSMPR
No Recycle
MSMPR
1
at 20 oC
(S = 1.39)
Recycle
at 45 oC
Q1
(S = 1.02)
(52.329 g/min)
T = 20 oC
3
Jacketed baffled tubing
used as heat exchanger
2
100
150
Time (min)
8000
6000
4000
2000
0.06
50
100
150
Time (min)
200
0
0
10
0
0
0.14
50
100
150
Time (min)
200
Steady-state Operation?
Temperature(C)
Total Counts/s
Concentration (g/g)
20
150
Time (min)
200
250
1
10
10
Chord Length (m)
2
10
3
M-P2b
Feed Vessel (2.5% Seed)
M-P2b (Steady-state Product)
60
40
40
Marginal crystal growth
relative to seed material
20
Significant crystal growth
20
10
x3 10
4
2.5
6000
FBRM Periodic Steady-state
Boundary
1.5
0
2000
Dampened temperature cycles
50
100
Time (min)
150
200
0.1
0.08
0.06
0.04
35
25
8000
Seed
added
Temperature(C)
Total Counts/s
Concentration (g/g)
20
15
10
5
0
10000
Growth and/or
Agglomeration
30 min.
Hold
Secondary
nucleation
100
200
300
Time (min)
6000
4000
2000
400
500
0
2
10
40
20
0 0
10
3
80
M-P4
60
40
Product SWCLD narrows
but mean crystal size is
marginally smaller than
seed material
0 0
10
12000
30
1
10
10
Chord Length (m)
M-P4 (Steady-state Product)
60
0
B-C1
0.12
80
8000
4000
Probes transferred to
nd
2 stage of MSMPR
0
0
0 0
10
M-P5
Temperature(C)
Total Counts/s
Concentration (g/g)
Secondary
Nucleation
30
20
200
Periodic Steady-state
Operation
Readings from 1st
stage of MSMPR
40
0.04
0.5
100
80
60
0
M-P6
1
50
0.06
0
2
Fouling on
Probes
30
0
0.1
0.08
50
Temperature Cycles
End of 1st RT
10
0.08
4000
2000
Start-up Phase Nucleation Phase
40
Total Counts/s
Boundary
Secondary
Nucleation
60 Addition / Withdrawal Cycle
Temperature ( C)
6000
Temperature(C)
Total Counts/s
Concentration (g/g)
10
100
150
Time (min)
th
Start-up & 1 to 4
0.12
Concentration (g/g)
20
50
st
8000
Temperature ( C)
30
50
0.1
Operation
Total Counts/s
40
0.16
0.12
th
2000
0 0
10
3
Temperature Cycles
0.04
0
Concentration (g/g)
g
st
Start-up & 1 to 5
Addition / Withdrawal Cycle
Boundary
4000
Temperature(C)
Total Counts/s
Concentration (g/g)
10
Temperature Cycles
End of 1 RT
50
Temperature ( C)
Concentration (g/g)
60
6000
Secondary
Nucleation
20
10
M-P2a
M-P2a (Steady-state Product)
40
30
80
8000
2
SWCLD (Counts / s)
0
0
0.1
0.08
1
10
10
Chord Length (m)
200
1
10
10
Chord Length (m)
2
1st RT
10
80
2
10
3
M-P5
M-P5 (1st Stage Slurry)
1
10
10
Chord Length (m)
2
10
3
B-C1
B-C1 (Batch Product)
3rd RT
60
4th RT
MSMPR Product
100
40
Evolution of crystal size
distribution with time in
continuous MSMPR
20
50
0 0
10
0 0
10
3
M-P6
2nd RT
150
1
10
10
Chord Length (m)
20
SWCLD (Counts / s)
Complex behavior
of FBRM counts
Operation
Total Counts/s
Operation?
10
th
20
0 0
10
Total Counts/s
th
Secondary
Nucleation
20
Total Counts/s
st
Start-up & 1 to 5
30
st
Start-up & 1 to 7
20
0
200
40
SWCLD (Counts / s)
50
50
Temperature ( C)
40
60
SWCLD (Counts / s)
1000
Temperature Cycles
0
0
0.12
12000
10000
2000
st
End of 1 RT
10
M-P2b
Concentration (g/g)
50
Temperature ( C)
Concentration (g/g)
Temperature (C)
Total Counts/s
Concentration (g/g)
60
Total Counts/s
20
M-P3
60
SWCLD (Counts / s)
30
0.04
0
SWCLD (Counts / s)
200
80
M-P1
Feed Vessel (2.5 % seed)
40
SWCLD (Counts / s)
Total Counts/s
0.06
Temperature ( C)
0.08
Boundary
Total Counts/s
150
80
60
40
SWCLD (Counts / s)
100
Time (min)
Crystal Size Distributions
4000
3000
Temperature(C)
Total Counts/s
Concentration (g/g)
1
10
10
Chord Length (m)
2
10
3
0 0
10
Product SWCLD
broadens and shifts to
the right, no growth
1
10
10
Chord Length (m)
2
10
3
Figure 4. Time diagrams (left) and FBRM SWCLD (right) for: M-P1 (single-stage MSMPR; no recycle; recrystallized seed), M-P3 (single-stage MSMPR with
recycle: recrystallized seed), M-P2a & M-P2b (single-stage MSMPR with Recycle; raw material seed) , M-P4 (single-stage MSMPR with concentrate recycle;
recrystallized seed), M-P5 (two-stage MSMPR; no recycle, recrystallized seed), M-P6 (Single-stage Continuous MSMPR), B-C1 (Batch Crystallizer).
Initial Modelling & Estimation
v
(S = 0.80)
Periodic Flow Single-Stage MSMPR
at 19 oC
50
M-P3
Operation
50
v
at 30 oC
(S = 1.02)
1000
Start-up & 1st to 5th
60 Addition / Withdrawal Cycle
The MSMPR unit was
reconfigured to operate either
as a single-stage or twostage MSMPR with and
without recycle stream.
Dissolver
Q4
0.1
Temperature Cycles
0
0
0.04
Periodic flow crystallization in
MSMPR
4
Q3 (37.34 g/min)
Recycle Stream
0.06
Model fitting
4
6
8
Number of PLS components
0.1
0.08
90
QM1
(S = 1.24)
v
2
0.12
3000
M-P4
0.12
92
No Recycle
Feed
PLSR factors selected
(model optimum)
94
Waste Stream
at 19 oC
st
End of 1 RT
10
st
96
Periodic Flow Single-Stage MSMPR
(S = 1.02)
0.06
98
4000
2000
20
0.04
5
(46.69 g/min)
30
Temperature ( C)
Percent Variance Explained in Y
Product
at 10 oC
0.1
0.08
Figure 2. Estimation of PLSR factors to be used in building the calibration model.
Filtration
1
0.12
100
MSMPR Configurations
Operation
Boundary
M-P2a
Model building then requires appropriate multivariate techniques
Figure 1. Pre-processed raw data - SNV & 1st Derivative .
Temperature (C)
Total Counts/s
Concentration (g/g)
40
0.04
88
th
Start-up & 1 to 5
50
Concentration (g/g)
0.06
st
60
Temperature ( C)
0.08
Concentration (g/g)
v
v
0.1
Concentration (g/g)
A multivariate calibration model was development using Raman. The first
step of the process involves mathematical pre-processing of the raw data.
MSMPR
v
Process Time Diagrams
0.12
2
Crystal size and shape monitored
with FBRM & PVM probes
RESULTS & DISCUSSION
Model Development
Feed
v
v
EXPERIMENTS
at 19 oC
Solution concentration monitored
with Raman & ATR-UV/vis probes
Intelligent Decision Support
System (IDS) with PAT Array.
QM1
(52.329 g/min)
Recycle Stream
Continuous Flow Single-stage MSMPR
With Total Recycle
Figure 3. Flow diagrams of MSMPRs used for periodic & continuous flow crystallization.
Periodic flow crystallization is
a novel method whereby
periodic, but controlled
disruptions are applied to the
MSMPR crystallizer.
v
v
Figure 5. gCrystal results for a seeded single-stage MSMPR crystallizer operated without recycle stream gives a good prediction of the system behaviour.
Conclusions
Continuous flow
crystallization in MSMPR
 Paracetamol is slow growing, use of recycle has only a small effect on CSD.
 Secondary nucleation: dominant crystallization mechanism affecting the CSD.
 Crystal growth: small seed crystals and no. of MSMPR stages affect growth.
A single-stage MSMPR was
configured for continuous flow
crystallization using a recycle
stream operated in a closed
loop.
ACKNOWLEDGEMENTS
The authors would like to thank the EPSRC, EPSRC CMAC and the European
Research Council (ERC) for funding this work.
14
K. Robertson, C. Wilson, EPSRC Centre for Innovative Manufacturing in Continuous Manufacturing and Crystallisation
University of Bath, Claverton Down, Bath, k.robertson@bath.ac.uk
Evaluation of Mixers for Operation in a Continuous Tubular Crystalliser
The solutions were pumped in through a 4.8 mm bore marprene tube at
10 rpm initially (low flow) as this was targeted as the standard operational
flow rate. This was then increased to 30 rpm (high flow) to evaluate the
mixing should an increased flow rate be used.
Figures for mixers 1 and 2 are shown for immiscible flow whilst mixer 3
shows miscible flow.
Three mixer designs were evaluated for mixing efficacy by mixing both
immiscible and miscible liquids with contrasting colours. CuSO4 was
dissolved in water or cyclohexane and pumped through the top port of the
mixer by a peristaltic pump. The bottom port of the mixer was fed untainted
water via a second channel on the same pump thereby linking the two flow
rates.
Mixer 1: jet in centre
The design of this mixer was intended such that the impinging jet flow was in-line
with the main flow. The rational behind this was to ensure any solid formation
would be instantly carried along with the main flow.
Low Flow
Mixing in-line with the jet is good as observed by high bubble distribution in the receiving tube.
Due to imperfect alignment of the jet a stagnation zone is observed at the base of the mixer.
At low flow rate there is an air bubble at the top of the mixer which could not be removed.
High Flow
Homogenous mixing is observed at high flow rate. The pulsing of the peristaltic pump causes
some back mixing at points prior to the jet inlet. No air bubble inclusion or stagnation zone is
observed at high flow.
Mixer 2: jet upstream
This mixer was designed for easier fabrication, increased durability and better
mixing within the unit. The jet is directed towards the bottom downstream wall
of the mixer, pushing the flow of liquid up the sides and radially inwards.
Low Flow
The angle of jet enables good radial mixing within the mixer which is highlighted by high
bubble distribution in the receiving tube. Due to the more streamlined design neither air
bubble inclusion nor stagnation zones were observed
High Flow
Homogenous mixing is observed at high flow rate throughout the entire mixer body.
Mixer 3: Y-piece
Y-pieces are a common method of joining two flows in-line. The y-line reduces
any turbulence of the product flow
Miscible Flow
The slope of the y-piece is such that, even with two aqueous solutions, no mixing is
evident at low or high flow. This can be beneficial for use in diffusion techniques.
Conclusions
Mixer 2 is designed to produce good mixing inside the mixer resulting in
a higher intensity of mixing than that experienced in mixer 1. Mixer 3
maintains laminar flow enabling slow diffusion of reagents between the
two solutions.
Three mixer types have been designed with very different strengths.
Mixer 1 ensures good mixing whilst maintaining flow direction. This may be
crucial if used as a nucleation aid is it ensures the propulsion of any
precipitate with the flow thereby protecting the CSD.
Diffusion Crystallisation
The slow diffusion of solvated material through the boundary of miscible solvents with
different density properties has been used widely for the slow controlled crystallisation
of products in batch environments (left). Using laminar flow this technique can be
adopted for flow environments by the introduction of side streams (right).
The slow diffusion of the two components can result in the formation of higher quality
and larger crystals. This is because the number of nuclei is reduced and so each
nuclei can grow independently without agglomeration.
Angew. Chem. Int. Ed. 2011, 50, 7502 – 7519
15
Monitoring Fouling in the Moving Fluid
Oscillatory Baffled Crystalliser
Rachel Sheridan, Jan Sefcik
Department of Chemical and Process Engineering,
University of Strathclyde, Glasgow, Scotland
Project Aims
Imaging Analysis with MATLAB
• To study the fouling phenomenon in the moving fluid oscillatory baffled
crystalliser (MFOBC) (Figure 3) during crystallisation under isothermal
conditions using imaging analysis
• To investigate the effect of operation parameters such as temperature
of crystallisation, solute concentration, solvent composition, thermal
history and oscillation frequency and amplitude on induction time of
fouling in the system.
Background
is
subtracted
(using
greyscale
first image)
Convert to
greyscale
(0-255
pixel
values)
Area of
image is
defined as
pixel
boundaries
Threshold
can be set
to
eliminate
noise
Average
pixel
intensity of
the area is
calculated
• Plots of average pixel intensity over time can then be obtained,
showing the progression of the fouling process
• An example of how the fouling can be seen by the camera is shown in
the raw images in Figure 6
Background
• Crystals often preferentially grow on surfaces because a surface
provides a lower energy pathway for nucleation (Figure 1). This is
called heterogeneous nucleation
74 minutes
Figure 6: LGA S=6 Low Oscillation Image 0 and Image 3555
Figure 1: Energy barriers for homogeneous and heterogeneous nucleation
• Heterogeneous nucleation is a function of homogeneous nucleation
and the mathematical relationship is shown in Figure 2
Results
∆Gheterogeneous = ∆Ghomogeneous. fθ
• Fouling induction times are obtained using the MATLAB thresholding
technique and through visual inspection of the raw images
• Induction time in this case is defined as the first crystal that is seen to
grow on the wall and remain for several subsequent images
• Figure 7 shows the results obtained for both cameras in each case
(2-3cosθ+cos3θ)/4[1]
Where: fθ =
[θ is the contact angle]
Figure 2: Heterogeneous and homogeneous nucleation free energy relationship
• Properties of the solution (e.g. solute concentration and solvent), the
surface topography and the chemical nature of the surface can
influence the fouling[2]
Induction Time (Hours:Minutes:Seconds)
Fouling Induction Times
• Fouling can cause system blockage, a reduction in heat transfer and
undesired crystal properties. This costs time and money through
cleaning and loss of product
Experimental Work
• Supersaturated L-glutamic solution
at 80°C pumped in to MFOBC
• S=3, 4, 6 used
• Temperature profile set (high
oscillation conditions example in
Figure 4)
• Cameras collect images during
crystallisation process
• Experiment stopped once fouling
has occurred
140
T5
Height of MFOBC (cm)
80
60
40
20
0
T1
0
20
40
60
Temperature (°C)
80
04:01:55
03:21:36
02:41:17
02:00:58
01:20:38
00:40:19
00:00:00
2
3
4
5
6
Figure 7: Fouling induction times obtained for L-glutamic acid under different conditions
• It can be seen that increasing supersaturation decreases induction
• The effects of oscillation conditions influence the induction time of the
highest L-glutamic acid concentration
• Figure 8 shows the trend, where higher frequency of oscillation yields
shorter fouling induction times
• This could be attributed to the greater shearing effect
• Figure 8 also shows lower camera values are generally longer due to
the temperature drift in the equipment
Figure 3: Diagram depicting the
MFOBC experimental set-up
Fouling Induction for L-glutamic acid S=6 (42.4 g/L)
7
6
5
00:00:00
00:05:02
00:10:05
00:15:07
00:20:10
Fouling Induction Time (Hours:Minutes:Seconds)
Upper LowOs
100
Lower LowOs
Upper HighOs
Lower HighOs
Figure 8: Fouling induction times obtained for l-glutamic acid S=6
Figure 4: Temperature vs. Height [2 Hz 45 mm]
• High oscillation: 2 Hz 45 mm
• Low oscillation: 1 Hz 45 mm
• Cameras boxed with LED torch to
help control lighting conditions
(Figure 5)
7
Supersaturation
• Upper camera position
used as basis for 20°C in
cold straight
• Temperature drift inside
system due to complete
mixing between regions
• T5 not undersaturated but
warmer to prevent fouling
at oscillation level
T4
Upper
Camera
Position
T3
Lower
Camera
Position
T2
100
S=3 Induction times are
greater than stated
value, i.e. no fouling
detected after this time
04:42:14
Supersaturation
120
05:22:34
Acknowledgments
• EPSRC Centre for Innovative Manufacturing in Continuous
Manufacturing and Crystallisation (CMAC)
• University of Strathclyde
• Jerzy Dziewierz
• Naomi Briggs
• Dr Christos Tachtatzis
Figure 5: Boxed camera with torch
References:
[1] Mullin, J. W. Crystallization. 4th Edition. Butterworth Heinemann, 2001
[2] Di Profio et. al. Crystal Growth and Design, 2012: 3749-3757
16
00:25:12
Establishment of Continuous Crystallisation Process in OBC Using Process Analytical Technologies
Humera Siddique1, Vishal Raval1, Ian Houson1, John Mack2, Alastair Florence1
1.Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow 2. Perceptive Engineering Limited
1) Introduction
A recent review of patent literature has revealed an increasing number of patents and published applications, demonstrating the intensity of activities in process engineering for the continuous manufacturing of chemicals [1]. Benefits,
often declared in continuous processing include: better product yields and quality; use of lower amount of solvent and other materials; less extreme operating conditions; efficient mixing; better control over process parameters;
improved safety; improved purity profiles; and ease of scaling up. In a manufacturing process; downstream processing stages used to be a bottleneck in making a quality product in an economical, safer and profitable way, as the
components and processes involved in downstream processing are usually expensive and determine final yield of product.
Crystallisation is one of the most important downstream process, governed by complex interacting variables – a simultaneous heat and mass transfer process with a strong dependence on fluid and partcle mechanics. A number of
continuous crystalliser designs are currently in use in the chemical industry. Mixed suspension mixed product removal (MSMPR), continuous stirred tank reactor (CSTR) cascade systems, plug-flow reactors (PFRs) and oscillatory baffled
reactors are the most commonly featured.
2) Background
4) Background of Project
Process Analytical Tools (PAT)
Oscilatory Baffle Crystalliser (OBC)
In addition to the recent advances in developing continuous
crystallisation systems, use of process analytical technologies (PAT)
for real time monitoring of crystallisation process is also
progressing well. For a crystallization process, it is important to
know at real-time the stories of particle size distribution, crystal
form, and the solution-phase concentration of active ingrediant,
and with recent advances in technology, more online analytical
tools have become available for these measurements. Among
these focussed beam reflectance measurements (FBRM), particle
vision measurement (PVM), Raman, UV and mid IR are most
commonly used analytical tools.
An oscillatory baffled crystalliser (OBC) is widely studied for reaction
system but less for crystallisation[2-7]. The basic principal comprises a
tubular network containing periodically spaced orifice baffles
superimposed with oscillatory motion of a fluid. Oscillatory flow mixing
has been developed and investigated as a process intensification
technology to achieve efficient and controlled mixing in tubular
reactors. Unlike conventional tubular reactors in which the mixing is
caused by the turbulent net flow, the mixing achieved in an oscillatory
baffle crystalliser(OBC) is mainly obtained by fluid oscillations and
thereby residence time distribution within the device can be tuned by
the oscillation and net flow rate [8].
3) Approach
5) OBC (Rattlesnake) Graphical interface
Collection of fundamental physical
and kinetic data
• Solubility curve
• MSZW
• Growth and dissolution kinetics
• Seed loading
Solvent screening
Optimisation of cooling
profile using process
analytical tools
•
•
•
•
New reactor technologies are set to change the operation of batch manufacture in the
process industries into the “new wave” of semi-continuous Make to Order Processing
Plants (MOPP). These have the potential to transform these sectors by reducing the
environmental burden, inventories and cost of manufacture and distribution.
This project develops an adaptive 'Dial a Product' control system to deliver the precise
control required for these high value low volume continuous manufacturing systems.
Bringing together control design and analytical techniques to complement these
reactors will enable the system to reach optimum performance and have commercial
impact. The solution will offer the chance to change the way the industry operates.
Instead of investing in a number of product specific batch reactors, continuous reactors
will be used for a number of applications, allowing companies to reduce CAPEX or work
on a rental basis, bringing in continuous systems as required.
•
•
•
•
Sonication probe
for nucleation
IR
FBRM
PVM
Feed
back
control
FBRM
Batch crystallisation
With similar geometry to
mimic mixing and heat transfer
Continuous
crystallisation
with real time monitoring to
assess product attributes
and steady state operation
Crystal habit
Crystal growth
Particle size distribution
Yield, polymorph purity
Mixing and flow
characterisation
to achieve plug
flow conditions
IR
6) Results
Batch run to optimise seeding and cooling profile
Crystallisation time: 4hr
Frequency of oscillation: 4Hz
Amplitude of oscillation: 1mm
Concentration (wt%)
50
literature solubility curve
2.5
0.12
with dissolution points
40
Experiment
30
Particle size
distribution
20
10
Batch run
(average of 3
experiments)
0
10
20
30
40
50
60
70
80
D (10) : 672
D (50) : 1580
D (90) : 2610
2
130 ml/min
0.08
1.5
0.06
Continuous crystallisation in OBC
Temperature profile during crystallisation
0.02
0.00
0
0
500
1000
1500
3
Residence time
3rd residence time
2nd residence time
0
1
2
3
Dimensionless time θ
4
Correlation between axial dispersion coefficient and oscillatory Reynolds
number
IR data representing process state
1st residence time
2.5
3rd straight
0.5
Reo
Particle size distribution for Lactose after batch crystallisation
1st straight
1
0.04
Temperature ( °C )
Comparison of literature solubility data with our equilibrium
solubility data
D/uL=.06
Amp:1mm
Frequency:4Hz
Ren=86
50 ml/min
0.10
Eθ
60
Mixing and flow characterisation for continuous crystalliser
Axial dispersion coefficient D/µL
Solubility study using Optimax with IR and FBRM
Particle size distribution
1st residence time
D (10) : 0.845
D (50) : 74.6
D (90) : 674
2nd residence time
D (10) : 0.88
D (50) : 610
D (90) : 2150
Peak Area
2
1.5
1
0.5
3rd residence time
0
0
Process temperature and set point comparison during crystallisation
2
4
6
8
Time (hr)
12
14
IR measuring solution concentration and predicting steady state
9) Conclusions and Future work
•
•
•
•
10
D (10) : 1.18
D (50) : 1130
D (90) : 2330
Process analytical tools play an important role in understanding and establishing a process
Continuous crystallisation of lactose was performed successfully for 12 hours in OBC
Seeded batch and Continuous Lactose crystallization will be performed in OBC
Lactose manufacturers will be contacted to get crude lactose (with impurities prior to crystallization)
and performing continuous lactose crystallisation with control on product attributes
Particle size distribution and crystal images for each residence time
Acknowledgement
Author would like to Acknowledge Naomi Briggs, of Centre for Continuous Manufacturing and Crystallisation, Bashir Harji of
Cambridge Reactor Design Ltd. The Technology Strategy Board (Project MOPP 101334) and EPSRC for funding. ABB and Metler
Toledo for providing equipment support.
10) References
[1] V. Hessel, C. Knobloch, H. Loewe, Review on patents in microreactor and micro process engineering, Recent Patents Chem Eng. 1, 1–16, (2008)
[2] M. R. Mackley,. Chem Eng Res Des, 69, 197, (1991)
[3] X. Ni, M. R Mackley, A. P. Harvey P. Stonestreet, M. H. I. Baird, N. V. R. Rao, Chem Eng Res Des, 81, 373, (2003)
[4] P. Stonestreet, A. P. Harvey, Chem Eng Res Des 31, 80, (2002)
[5] M. S. R. Abbott, A. P. Harvey, G. V. Perez, M. K. Theodorou, Interface Focus, 3, (2013)
[6] X. Nogueira, B. J. Taylor, H. Gomez, I. Colominas, M. R. Mackley, Comput Chem Eng, 1. 49, (2013)
[7] P.Stonestreet and P. M. J.Vander Veeken, Trans IChemE, Vol 77, Part A, 671 (1999)
[8] Crystallisation process and apparatus, WO 2011/051728 A1
17
Towards Multi-component Crystallisation in a Continuous Flow Environment
Kate Wittering*, #, Sam Candy* and Chick C. Wilson*, #
*Department of Chemistry, University of Bath, Bath, BA2 7AY
#
EPSRC Centre for Innovative Manufacturing in Continuous Manufacturing and Crystallisation, University of Strathclyde, Glasgow,
Email: kw245@bath.ac.uk
To date research into continuous multi-component crystallisation has involved using the DN15 COBC,
however, this is not the most suitable scale for the target active pharmaceutical ingredients (APIs)
under investigation at Bath. The move is towards small scale continuous crystallisation, with
implementation of a range of new small scale continuous crystallisers on the horizon. Detailed here are
the main threads of my research into continuous multi-component crystallisation and some of the materials under investigation . Key challenges and directions for future work are highlighted with a
particular emphasis on how collaboration is required to continue some lines of enquiry and how these
systems may be able to feed into other research within the centre.
Beta-cyclodextrin Host-guest Systems
Polymorph Control of Urea Barbituric Acid Co-crystals (UBA)
Beta-cyclodextrin (β-CD) is a 7-membered ring of
α-D-glucopyranoside sugar units with a toroidal geometry and a
cavity diameter of approx. 7 Å which is capable of hosting a variety
of small molecules including APIs such as pyrazine carboxamide and
nicotinic acid [4, 5]. β-CD is an FDA approved substance this is particularly important as complexation of the API with β-CD can lead to
increased solubility of the API as
Form I
Barbituric acid
Form II
Urea
β-CD is an FDA approved substance.
Form III
At Bath attempts have been made to crystallise two or more
complementary anti-tubercular APIs with β-CD, although complexes
prove difficult to analyse using PXRD due to little variation between
the pattern of β-CD alone and the inclusion complexes. SXRD data can
provide more detail although data proves difficult to model due to
variance in occupancy of the target within β-CD.
In a collaborative effort with Dr Ali Saleemi at Loughborough polymorphs I, II and III of UBA [1]
can now be isolated by altering the rate of cooling within an STR.
Translation to the COBC allowed polymorph I to be consistently produced at a yield of 15%.
Due to the size of the DN15 COBC the very slow cooling rates required ( 0.05 oC min-1) to
isolate form II and III could not be achieved.



Further characterisation of the UBA polymorphs will be carried out at Bath

Is it possible to achieve these slower cooling rates using a different type of continuous crystalliser? - can anyone help?

Is it possible to form complexes with more than one complementary API? - further
investigation needed.
Some materials will be difficult to characterise, SXRD expertise may be required — ask Bath
Could form II and III UBA be accessed using seeding in the COBC? - collaboration with Strathclyde where seeding is already operational?
Pyrazine Carboxamide (A WHO Essential Medicine)
Urea and Di-carboxylic Acids —Controlling the Stoichiometry of Co-crystals
This work explores multi-component
crystallisation of pyrazine carboxamide (PC)
[6, 7] a polymorphic anti-tuberculosis drug
listed as an essential medicinal compound
by the World Health Organisation (WHO) [8].
It is common for molecular complexes of the same target molecule and co-former to crystallise in more
than one stoichiometric ratio.
This can be observed in complexes of urea with a range of di-carboxylic acids such as succinic acid and
oxalic acid [2, 3]. These systems were previously obtained via evaporation.
Produced in trace
amounts via
evaporation
Urea (U)
Cooling crystallisations have been prepared using PC in combination with a range of co-formers
classified either as an API or as a generally regarded as safe (GRAS) substance. PXRD analysis has
shown that products are purely PC indicating that the more soluble co-formers were not in high
enough concentration. However, more interestingly different polymorphs of PC have been formed
in the presence of different co-formers. This is in the early stages more results are required.


Readily produced via
cooling crystallisation
Pyrazine Carboxamide (PC)
Four Polymorphs of PC - restrict polymorphic variation through co-crystallisation
1:1
(U : OA)
Oxalic Acid (OA)
APIs or GRAS
Molecules
Potential templating of a particular PC polymorph through addition of a co-former.
Further investigation is required.
This may be true for other materials—can Bath help you with polymorph control?
Continuous Materials Discovery
2:1
(U : OA)
All these investigations into the discovery of new multi-component materials are currently carried out
using batch methods of cooling crystallisation in the Polar Bear.
In small scale flow chemistry it is possible to vary additives, rates of addition and reactor conditions to
create new products.
Small scale cooling crystallisation studies using the Cambridge Reactor Design Polar Bear Plus have been
carried out. A variety of starting material ratios have been studied using a wide range of solvents and
several different cooling rates from 0.5 oC min-1 to 5 oC min-1. To date , it has only been possible to produce 1:2 UOA in the Polar Bear.
Our aim is to translate some of this know-how to small scale multi-component crystallisation to move
towards an intensified and more continuous materials discovery process.
Discussion and any ideas are welcome
[1] Gryl, M., Krawczuk, A., Stadnicka, K. (2008) Acta Cryst. B64, p623-632
[2] Harkema, S., Bats, J.W., Weyenberg, A.M., Feil, D. (1972) Acta Cryst. B28, p1646-1648


[3] Harkema, S., Ter Brake, J.H.M. (1979) Acta Cryst. B35, p1011-1013
Can 1:1 UOA be produced via cooling crystallisation? -Seeding? - further investigation needed.
[4] Aree, T., Chaichit, N. (2009). Supramol. Chem. 21, 5, p384-393
1:2 UOA system will be further characterised in order to transfer into continuous flow (either in
COBC or on a smaller scale) A continuous system with inline PAT would be useful .
[5] Grachev, M.K., Senyushkina, I.A., Kurochkina, G.I., Lyssenko, K.A., Vasyanina, L.K., Nifant'ev, E.E., (2010) Russ.J.Org.Chem. 46, p1501
[6] Cherukuvada, S., Nangia, A. (2012). Cryst. Eng. Comm. 14, 7, p2579-2588.
[7] Cherukuvada, S., Thakuria, R., Nangia, A. (2010) Cryst. Growth Des. 10, 9, p3931–3941
[8] WHO Model List of Essential Medicines: http://www.who.int/iris/bitstream/10665/93142/1/EML_18_eng.pdf
18
Primary to
Secondary
Processing
19
Process Analysis for Monitoring of Powder Drying
Denise Logue, Jaclyn Dunn, David Littlejohn, Alison Nordon
EPSRC Centre for Innovative Manufacturing in Continuous Manufacturing and Crystallisation/
WestCHEM Department of Pure and Applied Chemistry, University of Strathclyde
e: denise.logue@strath.ac.uk, jaclyn.dunn.100@strath.ac.uk
Introduction
• Changes in particle properties such as attrition,
agglomeration and polymorphic transformation
can occur during a drying process
• Pharmaceutical regulations dictate that such
critical quality attributes must fall within rigid
specifications
• Non-invasive
measurements,
e.g.
Raman
spectrometry or acoustic emission, offer the
opportunity to determine the end point of a drying
process, as well as detecting any changes in
particle characteristics in real-time
Vacuum Agitated Batch Dryer
Vacuum Sealed
Oil Jacket
Agitator
Acoustic
Transducer
(attached
below oil
jacket)
Filter Bed
Oscilloscope
Acoustic results
Raman Spectrometry
• Raman was used to monitor the filtration and
drying of particles in a slurry within the filter dryer
• By monitoring the methanol content and the
particles the drying could be monitored[1]
Future Work
• The success of implementing Raman spectrometry
and acoustic emission in monitoring batch drying
has been demonstrated
• This work will be transferred to continuous dryers in
order to assess if the same level of results can be
obtained
• Raman
offers
the
opportunity
to
make
measurements in-situ or through glass vessel walls
• In order for acoustic measurements to be
successful there must be an appropriate place to
attach the transducer where there are collisions
with the reactor walls
Current Research: Real-time Monitoring • The lab has acquired a
using Acoustic Emission Spectrometry
Buchi spray dryer and will
Drying Vessel
Oscilloscope
Signal
be receiving a filtration
Acoustic
dryer from Pfizer
Transducer
PC
•
Each of these has its own
Interface
challenges and specific
Preamplifier
attention will be given to
the monitoring of attrition
• Acoustic emission (AE) is generated when
or
agglomeration
of
particles collide with the inner walls of a process
particles throughout the
vessel
drying process
• Changes in AE can be correlated with changes in
Buchi Spray Dryer
both particle density and particle size
[1] Hamilton, P., Littlejohn, D., Nordon, A., Sefcik,
• Signals are collected by a piezoelectric Nano 30
J., Slavin, P., Andrews, J., Dallin, P., Chemical
transducer (Physical Acoustics Ltd) attached to
Engineering
Science, 2013, 101, 878
the outer wall of different drying vessels
Solvent out
20
Continuous Spray Drying of ‘Novel’ Particles for
Inhaled Drug Delivery
Rebecca Halliwell, Alastair Florence
Strathclyde Institute of Pharmacy and Biomedical Sciences
University of Strathclyde, Glasgow, Scotland
Project Aim
Develop continuous lab-scale spray drying processes to engineer particles
with desirable properties that overcome the problems associated with the
pulmonary delivery of poorly soluble API via inhalation
RQ1: Spray Drying for
Control of Product
Attributes
Can spray drying be used to
deliver enhanced
bioavailability of poorly
soluble drug products
through co-processing of
API and excipient to
produce composite or
coated particles?
Spray Drying Definition
‘Transformation of feed
from a fluid state into a
dried particulate form by
spraying the feed into a
gaseous drying medium’
(Cal, K. and Sollohub, K.,
2010)
RQ2: Accelerating Lab-Scale Spray
Drying Process Optimisation
• Can the standard spray drying technique
be fully characterised and optimised in
terms of the process understanding and
measurement for pharmaceutical
manufacturing?
• What are the online measurement,
automation and control opportunities for
the Büchi B-290 Mini spray dryer?
Carbamazepine
Spray Drying of
Carbamazepine
5 known polymorphic forms
• Form III most stable
polymorph at room
temperature
• Form IV has previously been
made by “desolvation of a
form from methanol”[2]
Conditions
• Inlet temperature: 120°C
• Outlet temperature: 63 - 70°C
• Pump percentage: 10%
• Solution concentration: 10.4g/L
• Aspirator rate: 100%
Figure 1 Büchi B-290 Mini Spray Dryer
Spray drying proposed as an
alternative route to gain uniform
carbamazepine seed particles of a
defined size and polymorphic form
Desired attributes
•Form III
•Particle size < 10µm
Büchi High performance cyclone
RQ1: Spray Drying for Control of Product Attributes
Carbamazepine form IV Solubility Study
Initial work will repeat recently published research paper that is
relevant to research questions
Solubility data for form IV will be tested using Crystal 16 and Crystalline
(Xu, Guo, Xu, Li, & Seville, 2014, Influence of excipients on
spray-dried powders for inhalation, Powder Technology)
Compounds
• API:
Salbutamol
Sulphate
• Excipients:
Lactose,
betacyclodextrin,
starch and
NaCMC
Conditions
• Inlet: 150°C
• Pump:
450ml/hr
• Spray flow
rate: 600l/hr
• Aspirator:
100%
• Outlet: 85°C
[3]
Characterisation
• HPLC: % drug content
• TGA: moisture content
• SEM: morphology
• Malvern: size
• NG Impactor:
aerolisation performance
• Dissolution test: API
release
[4]
Acknowledgements
EPSRC and the Doctoral Training Centre in Continuous Manufacturing and
Crystallisation
University of Strathclyde
Naomi Briggs
Dr Rajni Miglani Bhardwaj
Dr Thomas McGlone
References
[1] Cal, K. and Sollohub, K. (2009). Spray Drying Technique. I: Hardware and Process Parameters. Journal of Pharmaceutical Sciences, 99, 2
[2] Getsonian, A., Lodaya, R.M. and Blackburn, A.C. (2008).One-solvent polymorph screen of carbamazepine. Journal of Pharmaceutics, 348, 3-9
[3] http://www.crystallizationsystems.com/en/crystal16/
[4] http://www.scienceplease.com/products/crystalline
21
Non-Invasive Monitoring of Powder Drying Processes by Acoustic
Emission Spectrometry & Optical Spectroscopic Techniques
Denise Logue, David Littlejohn, Alison Nordon, Jaclyn Dunn
Department of Pure & Applied Chemistry
295 Cathedral Street, Glasgow, G1 1XL
e: denise.logue@strath.ac.uk
Acknowledgements: EPSRC, Centre for Innovative Manufacturing in Continuous Manufacturing and Crystallisation (CMAC) & University of Strathclyde for funding
35
Filtration
30
Vol. Distribution (%)
• Changes
in
particle
properties
(attrition,
agglomeration, polymorphic transformation) occur
during drying processes
• Pharmaceutical regulations dictate that such
attributes must fall within rigid specifications
• Non-invasive analysis tools are employed to
simultaneously determine the end point of
processes and detect changes in particle
characteristics
Batch Drying – Mannitol from Ethanol
(70 - 150)/(270 - 350)
Introduction
25 rpm
80 oC
25
LOD = 0.07%
20
15
10
5
0
0
Dry Mixing
100
Wet Mixing
Time (mins)
200
300
AE Peak Area Ratio vs. Time for Mannitol
Powder Dried from EtOH
9
8
7
6
5
4
3
2
1
0
Dry Powder
2 Hours Drying
4 Hours Drying
0
100
200
Particle Size (µm)
300
Particle Size Distributions from
Mannitol Powder Dried from EtOH
240 Mins Drying
Dry Mixing
Relative change in acoustic
emission intensity after 240 minutes
of drying
LOD = 0.1 %
LOD = 0.1 %
25 rpm
80 oC
0
Bench-top Powder Mixer with
Non-invasive NIR Spectrometer
50
Dry Mixing
100
Wet Mixing
150
200
Time (mins)
250
300
Blender Off
AE Peak Area Ratio / NIR 1st Derivative at 7162 cm-1
vs. Time for Aspirin Powder Dried from MeOH
Batch Drying – Aspirin from Ethanol
1)
18
16
(70 - 150)/(270 - 350)
Vacuum Sealed
Oil Jacket
Agitator
Filter Bed
Bespoke Vacuum
Agitated Batch Dryer
1) Dry Mixing
2) Wet Mixing
3) Filtration
LOD = 0.09%
14
12
10
8
6
4
0
0
3)
LOD = 0.07%
25 rpm
80 oC
2)
2
100
200
Time (mins)
300
AE Peak Area Ratio vs. Time for Aspirin Powder
Dried from EtOH
• No significant change in particle size occurred
(mean d(0,5) = 394 vs. 399 µm)
Raman Intensity
NIR 1st Derivative
(70 - 150)/(2750 - 350)
Signal Intensity (a.u)
Signal Intensity (a.u)
• Particle size data are
indicative of attrition
Theory
• AE spectra support
Drying Vessel
Oscilloscope
Signal
this - relative decrease
Nano 30
Transducer
in signal intensity at
0
100000
200000
300000
400000
PC
Frequency (Hz)
lower frequency
Interface
Comparison of AE Frequency Spectra for Dry
regions suggests a
Mixing of Mannitol / 240 minutes of Drying
Preamplifier
decrease in particle
• Acoustic emission (AE) Spray Drying
size
Audible Region
1.40
is
generated
when
(20 Hz – 20 kHz)
1.20
• Commonly used in food
particles collide with the
1.00
and pharmaceutical
Drying
0.80
Region of Interest
inner surfaces of a
(70 – 150 kHz)
industries to form small Chamber
0.60
vessel
0.40
spherical particles
Cyclone
0.20
• Changes in relative
• Challenging to measure
0.00
Collection
intensity can be
0
50000
100000
150000
200000
as particle size is small Vessel
Frequency (Hz)
correlated with changes
(up to 15 µm), pressure
Typical AE Frequency Spectra for the Dry
in particle size
Mixing of Aspirin Powder
& particle velocity are
Buchi Mini Spray-Dryer
high and the use of
Preliminary Experiments
probes
is
limited
• Performed in a powder blender, Non-invasive NIR
• Kaiser Raman
used to track solvent loss in combination with AE
PhAT probe used to
AE Area Ratio
7162 cm-1
160
5
collect data during
1) Formation of aggregates
4
140
3
mannitol spray
120
2
drying
100
1
Raman Shift (cm )
80
0
• Peak at 1038 cm-1
Average Raman Spectrum Collected
1)
-1
During Mannitol Spray Drying
60
confirms the
-2
40
• Shows promise as a tool for presence of
-3
20
-4
polymorphic identification
Gamma polymorph
-5
0
0.40
0.35
0.30
0.25
0.20
0.15
0.10
0.05
0.00
8000000
7000000
6000000
5000000
4000000
3000000
2000000
1000000
0
1038 cm-1
Region dominated
by glass peaks
0
500
1000
1500
-1
2000
Further Work
• Batch dryer will be used to determine if AE can
be used to highlight differences in drying
behaviour of materials with different physical
properties
• Explore use of
Raman, AE and
NIR as
techniques to
monitor spray
drying and Pfizer
filter-dryer
Pfizer Continuous Filtration & Drying System
(Work due to commence April 2014)
22
Hot-Melt Extrusion for bioavailability enhancement
of poorly soluble drugs
s
Laura Martinez-Marcos, Dimitrios A. Lamprou, Gavin W. Halbert
Strathclyde Institute of Pharmacy and Biomedical Sciences (SIPBS),
University of Strathclyde, 161 Cathedral Street, Glasgow, UK
Introduction
Hot-Melt Extrusion (HME) is one of the most widely used processing techniques within
the plastic and pharmaceutical industry. It is a process of pumping raw materials with a
rotating screw under elevated temperature through a die into a product of uniform
shape. Moreover, comprises one of the manufacturing processes that enables oral
bioavailability improvement due to the formation of a solid dispersion between a drug
and a polymer.
This project will focus towards the bioavailability enhancement of poorly soluble drugs
classified as Class II in the Biopharmaceutics Classification System (BCS). The
development of a continuous manufacturing process such as continuous granulation
by HME, will promote the transition from batch to continuous manufacturing processes
in the pharmaceutical industry.
Methodology
A Thermo® Process 11 co-rotating twin-screw extruder with an intermeshing capacity
was used as depicted in Figures 1 and 2.
Fig 4. DSC thermograms of formulations containing ABZ and P188 (left) and
formulations containing ABZ and PVP K12 (right)
X – Ray Powder Diffraction (XRPD):
XRPD patterns of ABZ, physical mixtures and the extrudates can be observed in
Figure 5. These were obtained using a Bruker D8 advanced diffractometer in order to
determine that an amorphous solid dispersion has been achieved.
Fig 1. Thermo Scientific® Process 11 twin-screw extruder
Fig 2. Co-rotating Process 11 twin-screws
Initial formulations containing Albendazole, a well-known antihelmintic drug, and
diverse pharmaceutical grade polymers previously sieved, were manually premixed
and then extruded using the process parameters described in Table 1 below.
API
Albendazole
(ABZ)
Solubility in
water:
0.0228 g/L
EXCIPIENTS
(CARRIERS)
TM
(°C)
Kollidon® K12
(PVP K12)
140
API
BARREL SCREW
SPEED
CONCENTRATIONS TEMP.
(°C)
(rpm)
(%)
Kolliphor® P188
50 - 57
(Poloxamer 188)
1.0
5.0
70 - 145
100
1.0
5.0
40 - 50
[1]
60 [1]
Fig 5. XRPD patterns of formulations containing ABZ and P188 (left) and formulations
containing ABZ and PVP K12 (right)
Dissolution Profile:
Formation of solid dispersions is one of the strategies used to increase the dissolution
rate and oral bioavailability of poorly water soluble drugs. Dissolution tests of
formulations containing ABZ (1%) were performed using a Sirius T3 measurement
system. The extrudates were grinded and tablet pressed in order to carry out the
assays.
Table 1. Formulations and HME parameters applied
Results
The formulations described above were produced by HME (see Figure 3) and
characterised using different analytical techniques such as Differential Scanning
Calorimetry (DSC), X-Ray Powder Diffraction (XRPD), Fourier Transformed Infrared
(FTIR) spectroscopy as well as GI Dissolution test.
As depicted in Figure 6, PVP K12 acts as a drug precipitation inhibitor or also called
parachute [2]. This is due to the effect on the reduction of crystal growth rate.
Poloxamers are also known to have this effect but P188 acts in the opposite manner
and precipitation of ABZ can be observed [3].
Fig 3. Extruded formulations where a: ABZ (1%) – P188, b: ABZ (1%) – PVP K12,
c: ABZ (5%) – P188, d: ABZ (5%) – PVP K12
Fig 6. GI Dissolution Profiles of formulations containing ABZ (1%) and P188 (left) and
formulations containing ABZ (1%) and PVP K12 (right) calculated at different pH
Differential Scanning Calorimetry (DSC):
Conclusions
Raw materials, physical mixtures (PM) of drug and polymer plus the extruded materials
were all characterised by DSC analysis using a Mettler Toledo differential scanning
calorimeter. Aluminium crucibles of 40µl and a heat rate of 10°C/min were used.
Amorphous solid dispersions of ABZ were formulated based on two carriers with
different properties and molecular behaviours. P188 shows a crystalline pattern that
will be further investigated.
Differences on the DSC curves of crystalline and amorphous carriers can be observed
in Figure 4.
Dissolution rate of ABZ can be increased by the use of hydrophilic carriers as well as
HME processing.
Acknowledgements
EPSRC Centre for Innovative Manufacturing in Continuous Manufacturing and Crystallisation
References
1. Park JB, et al., New investigation of distribution imaging and content uniformity of very low dose drugs using hot-melt extrusion method. Int. J. Pharm., 458(2): 245-53, 2013.
2. Brouwers J, Brewster ME, and Augustijns P, Supersaturating drug delivery systems: the answer to solubility-limited oral bioavailability? J. Pharm. Sci., 98(8): 2549-72, 2009.
3. Xu S, and Dai WG, Drug precipitation inhibitors in supersaturable formulations. Int. J. Pharm., 453(1): 36-43, 2013.
23
Supply Chain
24
25
According to Schaber et al (2011) continuous production
system provides opportunity to reduce costs, improve energy
consumption and solvent utilisation, and reduce
environmental impact while improving quality. It provides
better control of manufacturing processes and final goods
(Pollet et al., 2009; Plumb, 2005).
To model the manufacturing system from a sustainability
point of view we need to know how to integrate
sustainability metrics into the overall performance
measurement system of an organisation.
-
-
- The research has focused on the environmental dimension
of the sustainability
- According to WCED (1987) “Sustainable is defined as a
development that meets the needs of the present without
compromising the ability of future generations to meet their
own needs”.
These are the three main
pillars (Social, Economic and
Environment) we have also
included two other important
factors (Governance and
Health and Safety) this have
been adopted when taking the
pharmaceutical industry under
consideration.
The threats of generic companies and bulk drugs have
increased, reducing product lifecycles and putting pressure
on profit margins. As a result pharmaceutical companies
have started to restructure and improve their supply chains
and manufacturing processes. The pharmaceutical sector
has recognised sustainability (economic, social and
environmental) as one of their key challenges (Marcus,
2010).
-
Sustainability
To explore the research question literature review of the
current state of art has been conducted. We have
explored three main areas relevant to the research
question:
Pharmaceutical industry has been one of the most patented
and profitable industries (Boldrin and Levine, 2007).
-
Sustainability
In the balance scorecard we have
four main aspect (customers;
internal processes; learning and
growth and financial) that have to
be aligned with the business
strategy, vision and mission.
Alignment could be achieved by
careful investigation of the business
aspects using different sets of
metrics and measurements and by
doing qualitative and quantitative
sets of analysis.
Customers
Learning
& Growth
Strategy
Financial
Business
Processes
Figure 2 Balanced Scorecard
Performance Measurement System (PMS)
The research aims to integrate the environmental
sustainability indicators and metrics into the overall
performance measurement system of a company focusing on
the pharmaceutical industry. Performance measurement is a
subject that is often discussed but not very often defined.
According to Neely et al (1995) PM is defined as the process
of quantifying the efficiency and effectiveness of action. In
other words PMS is defined as the set of metrics used to
quantify the efficiency and effectiveness of action.
Aim
PM
CM
Figure 1: Research area of interest
1. Continuous Manufacturing;
2. Sustainability and Sustainability Metrics;
3. Performance Measurement;
Literature Review
Introduction
In conclusion we have decided to integrate environmental
metrics into a performance measurement system of a
pharmaceutical company. By choosing BSC we would
show how the BSC could be a prominent innovation in
strategic performance measurement systems in the
pharmaceutical industry. The core idea is to implement
already existing environmental indicators and metrics
from the sustainability perspective into balanced
scorecard of a pharmaceutical company and observe the
differences that might occur. The mode of production is
continuous.
Conclusion
- Investigate alternative methods that would enable
building a robust PM model, such as systems dynamics
modelling
- Experiment with different approaches and test with key
stakeholders… aligning our model with the needs, goals
and objectives of the pharmaceutical industry taking
continuous manufacturing as a mode of manufacturing.
- Using data taken from the industry to implement into the
model framework.
- We use different tools and software to test the
framework and proceed to analysis.
- Analyse the data and compare with existing model or
framework.
3. Formalise and document the model framework.
1. To understand the literature to further develop this area.
2. Build a robust performance measurement system model
with relationships based on the balanced scorecard model
that include sustainability metrics quoted in the literature.
In order to deliver this aim, the specific objectives are:
We will use the pharmaceutical sector and continuous
manufacturing as the context for this research.
Overall aim of the research is to answer to the following
research question:
“How do we integrate sustainability metrics in to the complex
performance measurement system of an organisation?”
Research Aims and Objectives
Department of Design Manufacturing and Engineering Management, University of Strathclyde
PhD Georgi Aleksiev, Professor Bititci, and Dr. Kepa
Georgi Aleksiev was supported by prof. Umit Bititci. By
Dr. Kepa and DMEM department. G. A. was also
supported and sponsored by EPSRC. G.A. was
supported by Dr. Jag Srai and Dr. Thomas.
Acknowledgements
Email: georgi.aleksiev@strath.ac.uk
PhD Georgi Aleksiev
Contact
[6] WCED, 1987, (World Commission on Environment and Development). Our
common future. Oxford: Oxford University Press
[5] Schaber, S. D., Gerogiorgis, D. I., Ramachandran, R., Evans, J. M. B.,
Barton, P. I., & Trout, B. L. (2011). Economic analysis of integrated continuous
and batch pharmaceutical manufacturing: A case study. Industrial & Engineering
Chemistry Research, 50, 10083–10092.
[4] Plumb, K. (2005). Continuous processing in the pharmaceutical industry –
Changing the mindset. Chemical Engineering Research and Design, 83, 730–
738.
[3] Pollet, P., Cope, E. D., Kassner, M. K., Charney, R., Terett, S. H., Richman, K.
W., et al. (2009). Production of (S)-1-benzyl-3-diazo-2-oxopropylcarbamic acid
tertbutyl ester, a diazoketone pharmaceutical intermediate, employing a small
scale continuous reactor. Industrial & Engineering Chemistry Research, 48,
7032–7036
[2] Neely, A., Gregory, M., Platts, K., 1995, “Performance measurement system
design: a literature review and research agenda”, International Journal of
Operations & Production Management, Vol. 15 No.4, pp. 80-116.
[1] Boldrin, M. and Levine, D.K. 2007, Against Intellectual Property, Cambridge:
Cambridge University Press.
References
How to Integrate Sustainability Metrics into the Overall Performance Measurement System of an Organisation?
Area of interest
Research theme II:
Manufacturing Operations and Supply Chain Management Challenges in CM
Dr Jagjit Singh Srai, Dr Tomás Harrington, Leila Alinaghian, Mark Phillips
Institute for Manufacturing, University of Cambridge
Overview
This research explores possible future global value network configurations for the
pharmaceutical industry that align with a disruptive switch in technology from
batch-based manufacturing processes to continuous process manufacturing.
Our research examines how complex, multi-tiered supply chains and value
networks, often managed as semi-independent sub-systems, can be better
integrated end-to-end. Within many process industries, such as pharma, many
sub-systems exist e.g.
It’s a long way to the patient
(Supply chains 1-2 years start-to finish)...
Outsourced Stages
Make Active Ingredient
Formulate and distribute
• Clinical, Commercial
• API, Formulation, Pack/Distribute, Patient Delivery Models
Approach
A four-step process is used to identify alternative value network opportunities.
Step 1: identifying potential opportunities, barriers. Initial research identified a
number of opportunities for the implementation of continuous manufacturing in
the pharmaceutical industry, and potential barriers to their adoption. The barriers,
are significant, and require a coordinated and systematic approach to redesigning
the entire value network.
Step 2: Current state mapping and definition of critical sub-systems. The second
step involves mapping the current state of the batch-based global value network.
This led to the identification of the critical sub-systems that would be affected by
the shift to continuous manufacturing. Initial analysis was conducted to identify
the potential of the shift for each sub-system, setting out the potential scale of the
benefit for each sub-system.
Step 3: Sub-systems analysis against desired benefits. Deeper analysis of the subsystems is then carried out to support a tailored future configuration aligned with
the specific benefits identified in step 2. This analysis supports deeper
understanding of the ideal future configuration for each sub-system.
Step 4: Integration of the critical sub-systems. The final step of the process
involves detailed examination of the interactions between the five areas (clinical,
primary/secondary manufacturing, packaging and distribution, end-to-end supply)
to identify target applications for continuous manufacturing that could work
within and across the sub-systems. The target applications were then assessed in
terms of different transformation scenarios, bringing together inputs on
technology readiness and business viability.
Future Work
• Develop next iterations of the Pharma value chain.
• Extending the preliminary analysis conducted for selected patient populations
and product-process archetypes identified as having attractive business/value
propositions and promising technological feasibility.
• Consideration of the behavioural changes and dynamic capabilities required to
make the transformation across the value network
26
Breaking News! Project REMEDIES
(RE-configuring MEDIcines End-to-end Supply)
- £23m project Looking to reconfigure existing pharmaceutical supply chains
in the UK, end-to-end, by exploiting the latest technology advances in
medicines and patient-centric delivery models.
ICT-CMAC
27
Identification of particle size and shape
information from multiple sensor measurements
(ICT-CMAC Work Package 2)
O. S. Agimelen, J. Sefcik, M. Vasile, A. J. Mulholland
Okpeafoh.agimelen@strath.ac.uk
Introduction
The ICT-CMAC project is concerned with the development of an intelligent decision support system for
real time monitoring and control of attributes (form, size and shape) of crystals during continuous
crystallisation processes. The ICT-CMAC project is structured into work packages (WP1 to WP5) each
with specified objectives.
Intelligent decision support and control system for continuous
manufacturing and crystallisation
WP5
System integration/
communication
interface
WP4: plant wide
control system
WP3: intelligent
decision support
platform
ELN/LIMS
(people and
process)
Quantitative attributes
(form, size and shape)
Data from
sensor
measurement
People
Continuous
particle/
crystal
manufacturing
process
WP2: sensor/
measurement
modelling
WP1: data capture and
conditioning
WP2
o Develop and/or apply
existing mathematical
models to describe the
sensor.
o Build computer algorithms
to recover size and shape
information from data.
Size and shape information
sensors
Sensor image from Mettler Toledo
Recovery of size and shape information from data
obtained with the focused beam reflectance measurement
(FBRM) sensor
Experimental data from five samples of needle shaped cellobiose
octaacetate
Range of recovered particle
sizes consistent with
measurement with
Mastersizer sensor
Recovered shape
information consistent
with shape of particles
Calculated FBRM data
match measured FBRM data
Particle aspect ratio
28
ICT-CMAC
Work Package 5: People and Processes
a
B. Johnston, M. Robertson
murray.robertson@strath.ac.uk
Intelligent Decision Support (IDS):
To make extensive use of computational
hardware and software to streamline
and maximise productivity.
Work Package 5:
Researcher focussed information and communication
technology (ICT) such as Electronic Lab Notebooks
(ELN) and an IDS web-portal.
•
•
•
•
IDS Portal
• Web-based reporting of data
• Analysis of community data
• Management tools
Pipeline Pilot
Graphical data analysis workflow application
Automation of manual and error-prone tasks
Improved access historical data and results
Interactive reports
Management
IDS Portal
www.smartlab.sibs.strath.ac.uk
Researchers
ELN
www.eln.sibs.strath.ac.uk
Electronic Lab Notebook
Databases / Storage
• User-orientated
• Easy sharing and searching between users
o Open sharing within the project
• Automation of routine calculations
o Reduce errors and time spent processing data
• Interactive/automated project workflows/decision trees
• Data capturing from instruments
29
Intelligent Decision Support and Control Technologies
For Continuous Manufacturing and Crystallisation of Pharmaceuticals and Fine Chemicals
(ICT-CMAC)
WP4:Plant-wide Modelling and Control
Mathematical Modelling and Optimisation of Multi-Segment
Multi-Addition Plug-Flow Crystalliser
University of
Strathclyde
Science Engineering
Qinglin Su, Chris D. Rielly and Zoltan K. Nagy
Loughborough University
Q.Su@lboro.ac.uk; C.D.Rielly@lboro.ac.uk; Z.K.Nagy@lboro.ac.uk
Introduction
Plug-Flow Crystalliser
Units of tubular crystalliser, which can be simplified as an ideal plug-flow
crystalliser, could be assembled together to form a multi-segment multi-addition
plug-flow crystalliser (MSMA-PFC) [1,2]. Potential benefits result from the narrow
residence time distribution, the convenience of concentration and solubility control,
and the ease of scaling-up for continuous antisolvent crystallisation processes.
Multi-Segment Multi-Addition Plug-Flow Crystalliser
Segment 1
Segment 2
Segment 3
Segment 4
Results and Discussion
For a N segment and N addition
points of PFC, the location (Li) and
antisolvent addition flowrate (Fi)
could be optimised as follows.
𝐌𝐌𝐌𝐌𝐌𝐌 𝐽𝐽𝑠𝑠𝑠𝑠 𝐏𝐏𝑁𝑁
𝐿𝐿𝑖𝑖 ,𝐹𝐹𝑖𝑖
𝑖𝑖 = 1,2, ⋯ , 𝑁𝑁
𝐏𝐏𝑖𝑖 = 𝐌𝐌𝑃𝑃𝐹𝐹𝑃𝑃,𝑖𝑖 (𝜕𝜕𝑖𝑖 , 𝐹𝐹𝑖𝑖 )
0 ≤ 𝜕𝜕𝑖𝑖 ≤ 𝜕𝜕 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇
Fi
Li
𝑛𝑛
6.82
0.00
17.39
25.98
0.78
47.26
𝑖𝑖=1
Dynamic system equations
𝜕𝜕𝑛𝑛
𝜕𝜕𝑛𝑛
𝜕𝜕𝑛𝑛
+ 𝑢𝑢𝑧𝑧
+ 𝐺𝐺
=0
𝜕𝜕𝑡𝑡
𝜕𝜕𝜕𝜕
𝜕𝜕𝜕𝜕
𝜕𝜕𝐶𝐶
𝜕𝜕𝐶𝐶
+ 𝑢𝑢𝑧𝑧
+ 3𝜌𝜌𝑠𝑠 𝐾𝐾𝑣𝑣 𝐺𝐺 � 𝜕𝜕2 𝑛𝑛𝑛𝑛𝜕𝜕 = 0
𝜕𝜕𝑡𝑡
𝜕𝜕𝜕𝜕
Boundary conditions
𝐵𝐵
𝑛𝑛 𝜕𝜕, 0, 𝑡𝑡 = 𝑛𝑛𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 (𝜕𝜕) 𝑛𝑛 0, 𝜕𝜕, 𝑡𝑡 =
𝐺𝐺
𝐶𝐶 0, 𝑡𝑡 = 𝐶𝐶𝑓𝑓𝑠𝑠𝑠𝑠𝑠𝑠
0.01
47.86
72.00
z=72m
Concentration
Solubility
𝐹𝐹𝑖𝑖 = 𝐹𝐹𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇
where Jss is the objective function
at steady state; Pi is the product
quality vector for ith segment;
MPFC, i is the vector function for ith
PFC model; LTotal is the total length
of tubular crystalliser; ΔL is the
minimum
distance
between
addition points; FTotal is the total
antisolvent feeding rate.
z
Crystallisation system: Paracetamol in Acetone and Water [3]
Segment and addition number N: 4
z=72m
Interested product quality P: L43
Tube diameter D: 12.7 mm
Total tube length LTotal: 72.0 m
Minimum distance ΔL : 0.60 m
Total antisolvent feeding FTotal: 25.0 ml/min
Mean residence time τmean≈ 120 min
CSD with equal tube segments and antisolvent distribution
𝜕𝜕𝑖𝑖 − 𝜕𝜕𝑖𝑖−1 ≥ ∆𝜕𝜕
�
r
o
Initial conditions for n(L, z, t) and C(z, t)
𝑛𝑛 𝜕𝜕, 𝜕𝜕, 0 = 0 𝐶𝐶 𝜕𝜕, 0 = 0
Coolant
Optimisation of MSMA-PFC
uz
Optimisation result of tube segments and antisolvent distribution
CSD with optimal tube segments and antisolvent distribution
Conclusion and Future Work
•
•
•
•
Larger mean crystal size could be obtained by optimising antisolvent distribution
Locations and amounts of antisolvent addition are important design variables for MSMA PFC
Seed loadings and total tube length would be optimised in the future work
Varying number of segments and addition points would be considered as well
References
[1] Alvarez AJ, Myerson AS. Continuous plug flow crystallization of pharmaceutical compounds. Crystal Growth & Design. 2010;10:2219-2228.
[2] Ridder BJ, Majumder A, Nagy ZK. Population balance model-based multiobjective optimization of a multisegment multiaddition (MSMA) continuous plug-flow antisolvent crystalliser.
Industrial & Engineering Chemistry Research. in press, 2014.
[3] Woo XY, Nagy ZK, Tan RBH, Braatz RD. Adaptive concentration control of cooling and antisolvent crystallization with laser backscattering measurement. Crystal Growth & Design. 2009;9:182191.
30
Prof Alastair Florence Centre Director
e: alastair.florence@strath.ac.uk
t: +44 (0)141 548 4877
Craig Johnston Industrial Director
e: craig.johnston.101@strath.ac.uk
t: +44 (0)141 548 2240
Dr Andrea Johnston Centre Manager
e: andrea.johnston@strath.ac.uk
t: +44(0)141 548 4506
General enquiries
e: info@cmac.ac.uk
www.cmac.ac.uk

Poster Collection 2014

Transcription

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