The original continuous crystallization process used anti-solvent crystallization with heptanes and IPAc to crystallize an API. This led to varying composition, supersaturation and volumes, producing small primary particles prone to agglomeration. The process had long cycle times, low throughput and yielded primarily agglomerated particles. Dynochem modelling was used to improve the process by controlling crystallization parameters.
Generally, size reduction and size separation are combined to obtain powder with the desired particle size distribution (PSD) for acceptable flow and compressibility for downstream processing . The mechanical process of reducing the particle size of a solid is also called milling.
(No "Download lock")........... Study it, Download it, Understand it, Apply it and Serve the community.
رَبِّ زدْنيِ عِلْماً (Arabic)..............Ameen.
Generally, size reduction and size separation are combined to obtain powder with the desired particle size distribution (PSD) for acceptable flow and compressibility for downstream processing . The mechanical process of reducing the particle size of a solid is also called milling.
(No "Download lock")........... Study it, Download it, Understand it, Apply it and Serve the community.
رَبِّ زدْنيِ عِلْماً (Arabic)..............Ameen.
This presentation will help the students of Pharmacy in subjects like Pharmaceutics and industrial pharmacy. Hope you will find it better and helpful.
Regards
Amjad Anwar
email: amjadanwar77@gmail.com
Department of Pharmacy, University Of Malakand
Mo ch 1_properties of particulate solid_complete_10.12.2020Dhaval Yadav
Properties of Particulate Solids
Fundamentals of Unit operation and Unit process
Specific properties of solids
Particle density and Bulk density
Sphericity,
Equivalent diameter,
Specific surface area,
Volume surface mean diameter, mass mean diameter, and shape factor
Milling is mechanical process of reducing the particle size of
solids.
Various terms has been used cursing,
disintegration, dispersion, grinding, and pulverization
Heat is a form of energy. According to the principle of thermodynamics whenever a physical or chemical transformation occurs heat flow into or leaves the system.
A number of sources of heat are used for industrial scale operations steam and electric power is the chief sources to transfer heat. It is essential to cover steam without any loses to the apparatus in which it is used. The study of heat transfer processes helps in be signing the plant efficiently and economically
The purpose of this webinar is to highlight GSK's approach to:
- create a simple, mechanistically descriptive model
- verify its utility with clarity of objectives, and
- communicate understanding via creative but aligned metrics
... for a challenging chemical reaction.
This presentation will help the students of Pharmacy in subjects like Pharmaceutics and industrial pharmacy. Hope you will find it better and helpful.
Regards
Amjad Anwar
email: amjadanwar77@gmail.com
Department of Pharmacy, University Of Malakand
Mo ch 1_properties of particulate solid_complete_10.12.2020Dhaval Yadav
Properties of Particulate Solids
Fundamentals of Unit operation and Unit process
Specific properties of solids
Particle density and Bulk density
Sphericity,
Equivalent diameter,
Specific surface area,
Volume surface mean diameter, mass mean diameter, and shape factor
Milling is mechanical process of reducing the particle size of
solids.
Various terms has been used cursing,
disintegration, dispersion, grinding, and pulverization
Heat is a form of energy. According to the principle of thermodynamics whenever a physical or chemical transformation occurs heat flow into or leaves the system.
A number of sources of heat are used for industrial scale operations steam and electric power is the chief sources to transfer heat. It is essential to cover steam without any loses to the apparatus in which it is used. The study of heat transfer processes helps in be signing the plant efficiently and economically
The purpose of this webinar is to highlight GSK's approach to:
- create a simple, mechanistically descriptive model
- verify its utility with clarity of objectives, and
- communicate understanding via creative but aligned metrics
... for a challenging chemical reaction.
Scale-up and scale-down of chemical processesSeppo Karrila
Explains the path from for example synthesizing a useful appearing material in the lab to actual production of the same. Also explains what pilot machines are, how they are used, and why sometimes down-scaling of a unit operation is done to experiment in bench-scale.
Xennia's Tim Phillips describes the challenges and opportunities for using inkjet technology to deposit functional coatings, especially for printed electronics and solar energy applications. This talk was presented at the Advanced Functional Printing Conference, Dusseldorf, Germany in March 2011.
BCC Simulation Platform for Thermal Engineeringbccuk
The tool provides a universal simple method of representing the thermal character of electronics by Interpreting values from datasheet or measurement. It allows a power plot using a SPICE simulator to accurate represent package temperatures, which can be employed as vehicle to cross compare techniques.
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A presentation about innovative onsite wastewater treatment systems to remove nitrogen. Presented by Brian Baumgaertel, Environmental Project Assistant for the Barnstable County, Massachusetts, Department of Health and Environment, during the Buzzards Bay Coalition's 2013 Decision Makers Workshop series. Learn more at www.savebuzzardsbay.org/DecisionMakers
Watch this webinar here: https://bit.ly/32cbiHt
This webinar will introduce PVA as an optimized excipient for sustained release formulations. Combining direct-compression compatibility with a robust and reliable matrix formation, PVA has the potential to enhance sustained release formulations.
By modifying the drug release characteristics, significant therapeutic benefits can be achieved, such as improved efficacy of the therapeutic agent, reduced adverse effects, optimization of the dosing scheme and overall improvement in patient compliance. There are numerous approaches for modified release, each with its own benefits and drawbacks. This webinar will present PVA, a fully-synthetic polymer, for optimized sustained release matrix formulations. Combining robust and reliable gel-forming behavior with optimized tableting properties, PVA provides solutions for the most challenging sustained release formulations.
In this webinar, you will learn:
• How the gel-formation properties of PVA introduce sustained release
• Why compatibility with direct compression leads to simplified formulations
• That PVA can provide flexibility in sustained release formulation development
PVA for sustained release: theory and practiceMilliporeSigma
Watch this webinar here: https://bit.ly/32cbiHt
This webinar will introduce PVA as an optimized excipient for sustained release formulations. Combining direct-compression compatibility with a robust and reliable matrix formation, PVA has the potential to enhance sustained release formulations.
By modifying the drug release characteristics, significant therapeutic benefits can be achieved, such as improved efficacy of the therapeutic agent, reduced adverse effects, optimization of the dosing scheme and overall improvement in patient compliance. There are numerous approaches for modified release, each with its own benefits and drawbacks. This webinar will present PVA, a fully-synthetic polymer, for optimized sustained release matrix formulations. Combining robust and reliable gel-forming behavior with optimized tableting properties, PVA provides solutions for the most challenging sustained release formulations.
In this webinar, you will learn:
• How the gel-formation properties of PVA introduce sustained release
• Why compatibility with direct compression leads to simplified formulations
• That PVA can provide flexibility in sustained release formulation development
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Crystallization process improvement driven by dynochem process modeling. Flavien Susanne.
1. Crystallisation improvement driven by Dynochem
process modelling
Flavien Susanne Chemical Engineer
Moussa Boukerche, Thomas Dupont
Pfizer Confidential
2. Introduction
Crystallisation is a critical stage in the manufacture of an Active
Pharmaceutical Ingredient (API) where key attributes such as purity
together with physical and mechanical properties of the crystals are set.
Particle size distribution, polymorphic form and crystal habit, have a
direct impact on downstream processing (e.g. filtration, drying and
powder processing) and ultimately on the performance of the drug
product
Pfizer Confidential
3. Outline
2 case studies to illustrate the use of Dynochem to
Improve API crystallisation
1. Distillation/crystallisation process by constant anti-solvent addition
Original process performed by strip and replace cycles
Limitation and physical property issues
Improvement by control of crystallisation parameters
2. Continuous crystallisation by distillation and anti-solvent addition
Original process performed by anti-solvent crystallisation
Limitation and physical property issues
Principle, Advantage and Improvement
Pfizer Confidential
4. Case study 1: original process
Main issue: reliability of particle size distribution
Multiple Strip and Replace cycles (7-9 cycles)
80:20 % w/w THF:water to >95% acetonitrile
Large volume of solvent required
Long cycle time, potential decomposition of API
Concentration Addition of
by distillation anti-solvent
Pfizer Confidential
5. Limitation of the process
For each addition
Variation of composition and temperature
Sudden drop of solubility and increase of supersaturation when
addition is done
Uncontrolled increased of number of particle = uncontrolled
crystallisation
Pfizer Confidential
6. Results
Batch to batch variability
Crystallisation highly dependant to the process variability
Different particle size distribution and physical property
Pfizer Confidential
7. Crystallisation by continuous distillation/addition
Transfer from Strip and Replace addition to constant addition
Control of solubility evolution by avoiding sudden changes
80
g/L
70
1st event of crystallisation
triggered by aliquot addition 60 batch Solubility g/L
50
cst Solubility g/L
40
2nd event of crystallisation 30
triggered by aliquot addition 20
10
0
0 100 200 300 400 500
mins
Pfizer Confidential
8. Approach and principal
Addition of Addition of
Concentration anti-solvent anti-solvent distillation
by distillation
Improve efficiency
Better control of anti-solvent addition, less disruption of
temperature and composition
Better control of solubility and supersaturation
Benefit
Improvement of physical property
Additional benefit
Minimise solvent use
Cycle time
Pfizer Confidential
9. Equipment for POC: RC1-MP06
RC1MP06: Reactor for
distillation
Constant feed
Weight of distillate
recorded Weight of solvent
measured
Concentration of solvents and
component monitored by IR
Pfizer Confidential
10. RC1-MP06 Characterisation
Specific to reactor
Geometry, material of construction, HTF used (flow rate, Cp)
First use of the UA Dynochem estimation
Series of calibration run at different volume followed by heat up, cool
down and distillation experiments for validation
Prediction based on heat transfer and heat loss of the reactor used
90 35
80 30
% resistance
70
Temperature
25
60
50 20
0.085 40 15
30
10
20
10 5
0 0
Wall
Process
Lining
fouling
Outside
Outside
Inside
Service
Inside
fouling
film
fluid
fluid
film
Height (m)
0.035 10
8
UA (W/K)
6
4
-0.1 -0.05 0 0.05 0.1 2
0
0 0.5 1 1.5
Volume (user units)
Pfizer Confidential
11. Model in Dynochem and prediction
Temperature prediction
Liquid phase
Composition prediction
gas phase composition
prediction
Pfizer Confidential
12. Control of crystallisation parameters
Calculation and prediction of solubility and supersaturation
The solubility of the mixture THF:water:acetonitrile as a function
of temperature was determined experimentally using 13-run D-
optimal design
The supersaturation was calculated from the solubility
Pfizer Confidential
13. POC Results
Prediction of solvent evolution
Validation of the Proof Of Concept
Pfizer Confidential
15. Transfer to Large Scale
POC demonstrated in the lab using the RC1
reactor
automated 0.8L calorimeter reactor
Transfer to the Pilot Plant reactor, conical
250L type reactor with twin jacket.
Transfer to manufacture reactor, 1500L
bottom dish reactor
Pfizer Confidential
17. Large scale reactor Characterisation
Extract mathematical description of heat transfer using Dynochem
1 1 1 1 1 1 1
U hi hif hl hw ho f ho
TC
Jacket wall Reactor
r (m)
outside film lining inside film
Pfizer Confidential
18. Large scale reactor Characterisation
Measure heat up and cool down curves for different volumes and stirring
speeds
Analyze dynamics of reactors with respect to heat transfer
Calculation of resistance contribution for different reactors
From lab to
large scale
Pfizer Confidential
19. Large scale reactor Characterisation
Prediction heat transfer model specific to Pilot Plant reactor
Geometry, material of construction, HTF (flow rate and Cp)
Heat up and cool down experiment UA and Uloss
exp1 95kg Tj=60°C
70.0
exp5 130.4kg DT=30°C Jacket.Temperature (Imp) (C)
Bulk liquid.Temperature (Exp) (C)
Jacket.Temperature (Imp) (C) 150.0 Bulk liquid.Temperature (C)
Bulk liquid.Temperature (Exp) (C)
Bulk liquid.Temperature (C)
56.0
120.0
Process profile (see legend)
exp4 130.4kg Tj=20°C Jacket.Temperature (Imp) (C)
Bulk liquid.Temperature (Exp) (C)
42.0 70.0 Bulk liquid.Temperature (C)
90.0
28.0 56.0
60.0
Process profile (see legend)
42.0 30.0
14.0
28.0 0.0
0.0 0.0 21.97 43.94 65.91 87.88 109.85
0.0 43.527 87.053 130.58 174.107 217.633
exp6 166.2kg Tj=60°CTime (mins) Jacket.Temperature (Imp) (C)
Bulk liquid.Temperature (Exp) (C)
exp7 166.2kg Tj=20°CTime (mins) Jacket.Temperature (Imp) (C)
Bulk liquid.Temperature (Exp) (C)
75.0 Bulk liquid.Temperature (C) 70.0 Bulk liquid.Temperature (C)
14.0
60.0 56.0
0.0
Process profile (see legend)
0.0 41.03 82.06 123.09 164.12 205.15
Time (mins)
45.0 42.0
30.0
28.0
15.0
14.0
0.0
0.0 33.873 67.747 101.62 135.493 169.367 0.0
0.0 35.707 71.413 107.12 142.827 178.533
Time (mins)
Time (mins)
Pfizer Confidential
20. Large scale reactor Characterisation
Predictive model specific to the Pilot Plant reactor
Distillation trials partial reflux and N2 sweep effect
constant level trial
150.0
sumof f gasv olume (Exp) (L)
Bulk liquid.Temperature (Exp) (C)
Jacket.Temperature (Exp) (C)
v apour.THF (kg)
120.0 Jacket.Temperature (C)
Bulk liquid.Temperature (C)
sumof f gasv olume (L)
90.0
exp11 distillation from batch exp
150.0
sumof f gas (Exp) (kg)
Bulk liquid.Temperature (Exp) (C)
60.0 Jacket.Temperature (Exp) (C)
sumof f gas (kg)
120.0 Jacket.Temperature (C)
Process profile (see legend)
Bulk liquid.Temperature (C)
30.0
90.0
0.0
0.0 30.0 60.0 90.0 120.0 150.0 exp10 distillation test
60.0 250.0
Time (mins) sumof f gas (Exp) (kg)
Bulk liquid.Temperature (Exp) (C)
Jacket.Temperature (Exp) (C)
sumof f gas (kg)
30.0 200.0 Jacket.Temperature (C)
Process profile (see legend)
Bulk liquid.Temperature (C)
150.0
0.0
0.0 24.347 48.693 73.04 97.387 121.733
Time (mins)
100.0
Different distillation conditions 50.0
Match between experimental data and prediction 0.0
0.0 32.0 64.0 96.0 128.0 160.0
Time (mins)
Pfizer Confidential
21. Process transfer
Constant feed of MeCN : Flow rate between 32L/hour
Volume contained at 150L ± 10%
Variation of Cp and density affecting variation of volume
Distillation time 13h
10h time saving compare to batch for same end point
>10% solvent saving
More accurate control of solubility and supersaturation
Pfizer Confidential
22. Case study 1: Results and Conclusions
Prediction of solvent evolution
Validation of the model on large scale
t 100
n d
e
e t
v
l a
l
l
o i
t
s s
i
s d
s e
a m
m 80 u
l
o
% v
60 THFmassratio (Exp)
watermassratio (Exp)
acetonitrilemassratio
40 (Exp)
watermassratio
acetonitrilemassratio
THFmassratio
20
sumoffgasvolume
sumoffgasvolume
(Exp)
0
0 200 400 600 800
mins
Pfizer Confidential
23. Case study 1: Results and Conclusions
Repeatability from batch to batch
Process conducted in 250L and 1500L reactors
Pfizer Confidential
24. Outline
2 case studies to illustrate the use of Dynochem to
Improve API crystallisation
1. Distillation/crystallisation process by constant anti-solvent addition
Original process performed by strip and replace cycles
Limitation and physical property issues
Improvement by control of crystallisation parameters
2. Continuous crystallisation by distillation and anti-solvent addition
Original process performed by anti-solvent crystallisation
Limitation and physical property issues
Principle, Advantage and Improvement
Pfizer Confidential
25. Case study 2: original process
Main issue: reliability of particle size distribution
Anti-solvent crystallisation
65:35 % w/w heptanes:IPAc, 11mL/g
Long cycle time, low throughput
Physical property issues (high degree of secondary nucleation)
Addition of
anti-solvent
Pfizer Confidential
27. Continuous crystallisation
Concept
Design new process to enable better crystallisation
Increase the seed surface to promote rate of growth
Control the rate of nucleation Vs rate of growth
Starting volume with high seed concentration
The crystallisation is generated by addition of anti-solvent and distillation
to the right concentration solvent/anti-solvent
Continuous distillation of azeotropic solution
Pfizer Confidential
29. Continuous crystallisation
P-8
Start
+++++++++++++++++++++++++++++++
No flow
Heptane
P-10
E-6 Preparation of seed bed
13g API in 65g heptanes and 32g
IPAc
solubility ~6g/L
IPAc
Composition and concentration
stay constant
Continuous Distillation/crystallisation
Large surface of seed
Promote growth
Liquors
Pfizer Confidential
30. Continuous crystallisation
P-8
Flow in
+++++++++++++++++++++++++++++++
Start of flow in
7.5g/min Heptane
heptanes P-10
E-6 0.5g/min API
6g/min IPAc
7.5g/min heptanes
Solution of IPAc Start of vacuum at 80mbar
0.5g/min T= 25.5C
6g/min IPAC
Continuous Distillation/crystallisation
Distillation rate controlled by T
Heptanes: 4.1 – 6 g/min
IPAc: 3.65 – 5.3 g/min
Liquors
Pfizer Confidential
31. Continuous crystallisation
P-8
Flow in
+++++++++++++++++++++++++++++++
Start of flow in
7.5g/min Heptane
heptanes
P-10
E-6 0.5g/min API
6g/min IPAc
7.5g/min heptanes
Solution of IPAc Start of vacuum at 80mbar
0.5g/min T= 25.5C
6g/min IPAC
Continuous Distillation/crystallisation
Distillation rate controlled by T
Heptanes: 4.1 – 6 g/min
IPAc: 3.65 – 5.3 g/min
Liquors
Pfizer Confidential
32. Continuous crystallisation
P-8
Flow in
+++++++++++++++++++++++++++++++
Start of flow in
7.5g/min Heptane
heptanes P-10
E-6 0.5g/min API
6g/min IPAc
7.5g/min heptanes
Solution of IPAc Start of vacuum at 80mbar
0.5g/min T= 25.5C
6g/min IPAC
Continuous Distillation/crystallisation
Distillation rate controlled by T
Heptanes: 4.1 – 6 g/min
IPAc: 3.65 – 5.3 g/min
Liquors
Pfizer Confidential
33. Continuous crystallisation
P-8
Flow in
+++++++++++++++++++++++++++++++
Start of flow in
7.5g/min Heptane
heptanes
P-10
E-6 0.5g/min API
6g/min IPAc
7.5g/min heptanes
Solution of IPAc Start of vacuum at 80mbar
0.5g/min P-9
T= 25.5C
6g/min IPAC
Continuous Distillation/crystallisation
Distillation rate controlled by T
Heptanes: 4.1 – 6 g/min
IPAc: 3.65 – 5.3 g/min
Liquors
Pfizer Confidential
34. Continuous crystallisation
P-8
Flow in
+++++++++++++++++++++++++++++++
Start of flow in
7.5g/min Heptane
heptanes P-10
E-6 0.5g/min API
6g/min IPAc
7.5g/min heptanes
Solution of IPAc Start of vacuum at 80mbar
0.5g/min P-9
T= 25.5C
6g/min IPAC
Continuous Distillation/crystallisation
Distillation rate controlled by T
Heptanes: 4.1 – 6 g/min
IPAc: 3.65 – 5.3 g/min
Liquors
Pfizer Confidential
35. Continuous crystallisation
P-8
Flow in
+++++++++++++++++++++++++++++++
Start of flow in
7.5g/min Heptane
heptanes P-10
E-6 0.5g/min API
6g/min IPAc
7.5g/min heptanes
Solution of IPAc Start of vacuum at 80mbar
0.5g/min P-9
T= 25.5C
6g/min IPAC
Continuous Distillation/crystallisation
Distillation rate controlled by T
Heptanes: 4.1 – 6 g/min
IPAc: 3.65 – 5.3 g/min
Liquors
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36. Continuous crystallisation
P-8
Flow in
+++++++++++++++++++++++++++++++
Start of flow in
7.5g/min Heptane
heptanes P-10
E-6 0.5g/min API
6g/min IPAc
7.5g/min heptanes
Solution of IPAc Start of vacuum at 80mbar
0.5g/min P-9
T= 25.5C
6g/min IPAC
Continuous Distillation/crystallisation
Distillation rate controlled by T
Heptanes: 4.1 – 6 g/min
IPAc: 3.65 – 5.3 g/min
Liquors
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37. Continuous crystallisation
P-8
Flow in
+++++++++++++++++++++++++++++++
Start of flow in
7.5g/min Heptane
heptanes P-10
E-6 0.5g/min API
6g/min IPAc
7.5g/min heptanes
Solution of IPAc Start of vacuum at 80mbar
0.5g/min P-9
T= 25.5C
6g/min IPAC
Continuous Distillation/crystallisation
Distillation rate controlled by T
Heptanes: 4.1 – 6 g/min
IPAc: 3.65 – 5.3 g/min
Liquors
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38. Continuous crystallisation
P-8
Flow in
+++++++++++++++++++++++++++++++
Start of flow in
7.5g/min Heptane
heptanes P-10
E-6 0.5g/min API
6g/min IPAc
7.5g/min heptanes
Solution of IPAc Start of vacuum at 80mbar
0.5g/min P-9
T= 25.5C
6g/min IPAC
Continuous Distillation/crystallisation
Distillation rate controlled by T
Heptanes: 4.1 – 6 g/min
IPAc: 3.65 – 5.3 g/min
Liquors
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39. Continuous crystallisation
P-8
Flow in
+++++++++++++++++++++++++++++++
Start of flow in
7.5g/min Heptane
heptanes P-10
E-6 0.5g/min API
6g/min IPAc
7.5g/min heptanes
Solution of IPAc Start of vacuum at 80mbar
0.5g/min P-9
T= 25.5C
6g/min IPAC
Continuous Distillation/crystallisation
Distillation rate controlled by T
Heptanes: 4.1 – 6 g/min
IPAc: 3.65 – 5.3 g/min
Liquors
Pfizer Confidential
40. Continuous crystallisation
P-8
Flow in
+++++++++++++++++++++++++++++++
Start of flow in
7.5g/min Heptane
heptanes P-10
E-6 0.5g/min API
6g/min IPAc
7.5g/min heptanes
Solution of IPAc Start of vacuum at 80mbar
0.5g/min T= 25.5C
6g/min IPAC
Continuous Distillation/crystallisation
Distillation rate controlled by T
Heptanes: 4.1 – 6 g/min
IPAc: 3.65 – 5.3 g/min
Liquors
Pfizer Confidential
41. Continuous crystallisation
P-8
Flow in
+++++++++++++++++++++++++++++++
Start of flow in
7.5g/min Heptane
heptanes P-10
E-6 0.5g/min API
6g/min IPAc
7.5g/min heptanes
Solution of IPAc Start of vacuum at 80mbar
0.5g/min T= 25.5C
6g/min IPAC
Continuous Distillation/crystallisation
Distillation rate controlled by T
Heptanes: 4.1 – 6 g/min
IPAc: 3.65 – 5.3 g/min
API Liquors
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42. Continuous crystallisation
P-8
Flow in
+++++++++++++++++++++++++++++++
Start of flow in
7.5g/min Heptane
heptanes P-10
E-6 0.5g/min API
6g/min IPAc
7.5g/min heptanes
Solution of IPAc Start of vacuum at 80mbar
0.5g/min T= 25.5C
6g/min IPAC
Continuous Distillation/crystallisation
Distillation rate controlled by T
Heptanes: 4.1 – 6 g/min
IPAc: 3.65 – 5.3 g/min
API Liquors
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44. Continuous crystallisation
Advantage
P-8
4 plates columns
to recycle the
+++++++++++++++++++++++++++++++
Control of the crystallisation by modelling Heptane
Heptane
Only one reactor required for the P-10
E-6
crystallisation
Only half Heptane required for same
conditions
Green Chemistry approach
API in IPAc
No additional investment
solution
existing batch reactor can be used Solid out
Continuous Distillation/crystallisation
Liquors
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45. Results: continuous crystallisation
•Constant supersaturation and composition: optimisation of
crystal growth
•Bigger particles (~25m) than typical batch size
•Particles can be grown bigger if processed longer
•Particles are not prone to agglomeration
•>90%yield of recovery
•Throughput : 36kg/m3.hour
1.25
UK-453,061 API - Particle size distribution
1.20
Comparison of continuous crystallisation batches isolated in an AFD
1.15
Batch No. D[v,0.1] D[v,0.5] D[v,0.9] D[4,3]
1.10 µm µm µm µm
1.05 120782/109/1 2.24 9.06 21.95 10.96
120782/103/3 2.71 9.84 25.31 12.97
1.00
0.95
Neil Dawson
0.90
0.85
0.80
0.75
Density distribution q3*
0.70
0.65
0.60
0.55
0.50
0.45
0.40
0.35
0.30
0.25
0.20
0.15
0.10
0.05
0.00
0.6 0.8 1.0 2 4 6 8 10 20 40 60 80 100 200 400
particle size / µm
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46. Conclusion
Alternative to standard crystallisation process can be
developed
Dynochem was a fantastic tool to enable new
process crystallisation development
Dynochem makes innovative thinking possible and
easy!!!
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47. Acknowledgment
Thomas Dupont
Moussa Boukerche
Andrew Derrick
Julian Smith
Wilfried Hoffmann
Garry O’Connor
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