Poster Collection 2014
Transcription
Poster Collection 2014
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 *[email protected] 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 ([email protected]), 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: [email protected] 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 ([email protected]); Fraser Mabbott ([email protected]) 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 ([email protected]); Dr. Thomas McGlone ([email protected]) 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: [email protected] 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 Fig. 5. Tube-side heat transfer as a function of oscillatory Reynolds number. 𝑅𝑅𝑅𝑅𝑛𝑛 = 32, 𝑆𝑆𝑡𝑡 = 0.8. 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 Oscillatory Reynolds number, Reo 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 Oscillatory Reynolds number, Reo 250 Fig. 6. Tube-side heat transfer as a function of oscillatory Reynolds number. 𝑅𝑅𝑅𝑅𝑛𝑛 = 43, 𝑆𝑆𝑡𝑡 = 0.8. 0 50 100 150 200 Oscillatory Reynolds number, Reo 250 Fig. 7. Tube-side heat transfer as a function of oscillatory Reynolds number. 𝑅𝑅𝑅𝑅𝑛𝑛 = 54, 𝑆𝑆𝑡𝑡 = 0.8. 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 ([email protected]); Dr. Huaiyu Yang ([email protected]); Francesca Perciballi ([email protected]). 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 *[email protected] 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 relative to seed material 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) Feed Vessel (2.5% Seed) 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 FBRM Periodic Steady-state 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 Periodic Steady-state 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 FBRM Periodic Steady-state 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 Feed Vessel (2.5% Seed) 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) M-P5 (Steady-state Product) Marginal crystal growth relative to seed material 1 10 10 Chord Length (m) 2 10 3 B-C1 Feed Vessel (2.5% Seed) 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) Feed Vessel (2.5% Seed) 20 SWCLD (Counts / s) Complex behavior of FBRM counts Periodic Steady-state Operation Total Counts/s Periodic Steady-state Operation? 10 th Marginal crystal growth relative to seed material 20 0 0 10 Total Counts/s th Secondary Nucleation 20 Total Counts/s st Start-up & 1 to 5 Addition / Withdrawal Cycle 30 st Start-up & 1 to 7 Addition / Withdrawal Cycle Marginal crystal growth relative to seed material 20 0 200 40 SWCLD (Counts / s) 50 50 Temperature ( C) 40 60 M-P3 (Steady-state Product) 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 Feed Vessel (2.5% Seed) 60 SWCLD (Counts / s) 30 0.04 0 M-P1 (Steady-state Product) 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 FBRM Periodic Steady-state 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 relative to seed material 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 Periodic Steady-state 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 Periodic Steady-state Operation FBRM Periodic Steady-state 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 Addition / Withdrawal Cycle 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, [email protected] 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: [email protected] 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: [email protected], [email protected] 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 University of Strathclyde 295 Cathedral Street, Glasgow, G1 1XL e: [email protected] 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 University of Strathclyde 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: [email protected] PhD Georgi Aleksiev University of Strathclyde 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 [email protected] 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 [email protected] 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 [email protected]; [email protected]; [email protected] 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: [email protected] t: +44 (0)141 548 4877 Craig Johnston Industrial Director e: [email protected] t: +44 (0)141 548 2240 Dr Andrea Johnston Centre Manager e: [email protected] t: +44(0)141 548 4506 General enquiries e: [email protected] www.cmac.ac.uk