1) Process modeling allows prediction of interactions between chemical and physical rates as a function of scale and equipment to enable safe and cost-effective scale-up from lab to production.
2) A case study reaction is used to demonstrate how kinetic data, heat of reaction, and process safety data can be combined in a model to optimize temperature profiles and process conditions at different scales.
3) Process safety is considered by simulating what would happen in case of cooling failure, and including a thermal risk metric in the optimization to safely transfer the process across scales.
Fluid Flow, Heat and Mass Transfer at Bodies of Different Shapes: Numerical Solutions presents the current theoretical developments of boundary layer theory, a branch of transport phenomena. Also, the book addresses the theoretical developments in the area and presents a number of physical problems that have been solved by analytical or numerical method. It is focused particularly on fluid flow problems governed by nonlinear differential equations. The book is intended for researchers in applied mathematics, physics, mechanics and engineering.
Fluid Flow, Heat and Mass Transfer at Bodies of Different Shapes: Numerical Solutions presents the current theoretical developments of boundary layer theory, a branch of transport phenomena. Also, the book addresses the theoretical developments in the area and presents a number of physical problems that have been solved by analytical or numerical method. It is focused particularly on fluid flow problems governed by nonlinear differential equations. The book is intended for researchers in applied mathematics, physics, mechanics and engineering.
INCLUDES THE INTRODUCTION TO CRYSTALLIZATION, FOLLOWED BY MECHANISM LIKE SUPER SATURATION, NUCLEUS FORMATION, CRYSTAL GROWTH, IN DETAIL ACCOUNT HOMOGENOUS AND HETEROGENOUS NUCLEATION AS PRIMARY AND SECONDARY NUCLEATION.
Stability and Shelf Life
Introduction
Stability Testing Methods:
Real Time Stability Testing
Accelerated Stability Testing
Retained Sample Stability Testing
Cyclic Temperature Stress Testing
Expiration Date/Shelf Life
Estimation of Shelf Life
Plant layout of capsules contains the defination, types, layout of hard and soft gelatin capsule and the environmental condition concluding with the references.
Presentation describes on reasons to conduct stability studies, effect of physical and chemical drug decomposition, effect of light and temperature on drug decomposition and storage of drug
Upfront Thinking to Design a Better Lab Scale DoEplaced1
Presentation Given at AIChE 2009 and the Dynochem User meeting. Discussion on using mechanistic modeling to support DoE investigations and QbD initiatives for single reaction steps.
This slide completely describes you about the stuff include in it and also everything about chemical engineeringThis slide completely describes you about the stuff include in it and also everything about chemical engineering. Fluid Mechanics. Thermodynamics. Mass Transfer Chemical Engineering. Energy Engineering, Mass Transfer 2, Heat Transfer,
INCLUDES THE INTRODUCTION TO CRYSTALLIZATION, FOLLOWED BY MECHANISM LIKE SUPER SATURATION, NUCLEUS FORMATION, CRYSTAL GROWTH, IN DETAIL ACCOUNT HOMOGENOUS AND HETEROGENOUS NUCLEATION AS PRIMARY AND SECONDARY NUCLEATION.
Stability and Shelf Life
Introduction
Stability Testing Methods:
Real Time Stability Testing
Accelerated Stability Testing
Retained Sample Stability Testing
Cyclic Temperature Stress Testing
Expiration Date/Shelf Life
Estimation of Shelf Life
Plant layout of capsules contains the defination, types, layout of hard and soft gelatin capsule and the environmental condition concluding with the references.
Presentation describes on reasons to conduct stability studies, effect of physical and chemical drug decomposition, effect of light and temperature on drug decomposition and storage of drug
Upfront Thinking to Design a Better Lab Scale DoEplaced1
Presentation Given at AIChE 2009 and the Dynochem User meeting. Discussion on using mechanistic modeling to support DoE investigations and QbD initiatives for single reaction steps.
This slide completely describes you about the stuff include in it and also everything about chemical engineeringThis slide completely describes you about the stuff include in it and also everything about chemical engineering. Fluid Mechanics. Thermodynamics. Mass Transfer Chemical Engineering. Energy Engineering, Mass Transfer 2, Heat Transfer,
A complete introduction to all things chemical kinetics designed specifically for non-chemists to understand. Fair warning: The presentation is very rigorous in its mathematical treatment, which is makes it a useful reference for looking up equations, but this can unfortunately make it less polished and flowing then a typical presentation. I tried my best to spell everything out clearly, but despite my best efforts it's still pretty dense.
Scale Up Methodology for the Fine Chemical Industry - The Influence of the Mi...Aldo Shusterman
Abstract- In this article the authors, based on the VisiMix Software, the experience of VisiMix users and personal knowledge from more than ten years of experience using VisiMix for API, Fine Chemicals and others, processes simulation, show a Method for Scale Down – Scale Up of Batch – Semi Batch operations built under Hydrodynamics study of the Mixing procedure in the reactor system. The use of the recommended method will offer the user the possibility to achieve the best results during production stage with saving among time and currency, and at the same time increasing the knowledge of the performed process. Several examples at the end of the article show the benefits of the proposed VisiMix Method Loops for Scale Down - Scale Up and Hydrodynamics Considerations.
Scale Up Methodology for the Fine Chemical Industry - The Influence of the Mi...Aldo Shusterman
Chemical production is a result of several chemical reactions and purification steps. Purification steps and processes yield are a direct function of the level of understanding of the reaction system. Reaction quality results have a tremendous impact in separation technology.
Chemical production is frequently performed on stirred vessels that are operated at batch or semi-batch configuration. The choice process configuration is determined at the development stage of the project. Therefore, if the chemical reaction and mixing are not well understood, wrong selections will be adopted in the process development
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.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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
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
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
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.
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.
Unsubscribed: Combat Subscription Fatigue With a Membership Mentality by Head...
Use of DynoChem in Process Development. Wilfried Hoffmann.
1. Use of DynoChem in Process Development
by Wilfried Hoffmann
Old: Chemical R&D, Sandwich, UK
Worldwide Pharmaceutical Sciences
New: Scale-up Systems, Dublin, Ireland
DynoChem Scale-up Systems 1
2. The Fundamental Problem of Scale-up
The major objective of Process Development is the design of a sequence of
operations, which allow the safe and ecologically responsible manufacturing
of Active Pharmaceutical Ingredients at a scale demanded by market, in a
quality demanded by Regulatory Authorities, and at the lowest achievable
cost
This development is based on lab scale experiments
?
3. The Fundamental Problem of Scale-up
Traditional approach:
Lab Reaction Development Pre Scale-up Scale-up
Lab Robustness testing Risk of
Design Process Safety testing
Experiments Failure
This approach underestimates the effects of physical rates
on the overall performance
are functions of scale
- rate of heat transfer
and equipment and
- various rates of mass transfer
can compete with
- various rates of mixing
chemical rates
4. The Fundamental Problem of Scale-up
Process Development needs to consider scale and equipment
As large scale development experiments are prohibitive with respect to
cost, safety, and time but large scale performance information is required
the solution is:
Process Modelling
Process Modelling allows the prediction of the interactions of chemical and
physical rates as a function of operating conditions, scale, and equipment
5. The Fundamental Problem of Scale-up
Lab Design approach => Model based approach:
Process Understanding based
Design Scale-up
Model Generation
Model + Equipment data
Lab Data Predicted
Large Scale Process
Experiments (Model) Performance
Optimization
Experiments are performed to generate Process Understanding, not
necessarily to get good yields in the lab.
This Process Understanding is then captured by First Principles
Mechanistic Models
A software package used by Pfizer which supports the generation and
capture of this information is Scale-up Systems’ DynoChem
6. Process Understanding
What is Process Understanding?
In this context Process Understanding is the necessary required knowledge
to allow predictions on the process behaviour on scale
How can we access this knowledge?
The first action is an analysis of the different rate processes (elements)
in our process (for illustration I am using a chemical reaction, but the
same principles can be applied to other unit operations)
DynoChem uses a visualisation tool, which is very useful for the early part
of this modelling approach
In the following this tool will be demonstrated for a semibatch reaction in a
jacketed reactor with a solid phase present
7. Process Understanding
FEED TANK BULK LIQUID Solvent, Tr0 Element 1:
The chemical rxn system
Chemistry
Including heat generation
Solvent, A,
T dos Element 2:
Heat transfer
Flow rate
B
UA Element 3:
(kLa)1 Heat Dosing mass transfer
B (s) out
H
SOLID
Element 4:
Solid/liquid system
8. Process Understanding
Small Scale Large Scale
Analysis Translation Construction
Process Understanding
Element 1 Element 1
Element 2 Element 2
Large Scale
Lab Process
reaction
Element 3 Element 3
Element 4 Element 4
9. Process Understanding
First Principles Mechanistic Models are using Basic Rate Laws and
Thermodynamics combined with fundamental conservation of mass and
energy to present these elements
(in contrast to empirical or DoE type models)
Chemical Rate Laws:
Chemical Reactions are best described by a set of elementary reactions, i.e.
reactions on a molecular level. These reactions are either unimolecular (bond
scissions or rearrangements) or bimolecular (by collision of two species)
The advantage of this approach is that all unimolecular reactions are first order and all
bimolecular reactions are second order. The disadvantage is that a complex reaction
system will require a set of elementary reactions, each with a rate constant and an
Energy of Activation.
This approach may be very attractive to chemists, as rate models can be constructed
directly from their knowledge about mechanisms.
10. Process Understanding
Physical Rate Laws:
In general are proportionate to a driving force
mass transfer rate: kLa ([A]∞ - [A]) (unit [conc/time])
Heat flow rate through jacket: AU (Tr-Tj) (unit [energy/time)
Thermodynamics:
Equilibrium and its temperature dependence is described by:
RT ln K - H TS
Conservation of mass and energy:
For example:
Mol balances in chemical reactions
Heat generation and heat removal control the degree of heat accumulation
(temperature change)
11. Process Understanding
The conservation of mass sounds trivial, but for the description of chemical reactions
this appears to be one of the critical items in modelling
The reason for this is that most of the information of chemical reactions is generated
by LC based methods with UV-based detectors.
Raw data from these methods will only generate area% information of the detectable
species and no information about the mass balance
Before such data can be used for modelling they have to be converted to absolute
mol data. This can be done by using Relative Response Factors and reaction mol
balances of at least 95% accuracy
The consequences of not doing this homework will be shown by a simple example
12. Process Understanding
The importance of the mol balance is demonstrated by a drastic example
Mass balance Analytical data
A+B→C
These data will not match
Either we have to change the mass balance (for example adding a rxn A → D),
or the analytical data are wrong and have to be corrected
13. Process Understanding
The basis for modelling are time resolved profiles of experimental data
1) Analytical profiles
2) Heat generation rates
3) Additional online info (ReactIR, pH, gas generation, H 2 uptake, etc...)
4) Accurate temperature profiles
Experimental Data: Kinetic model:
moles
moles
time time
14. Example System
The following example system, which has been used in several DynoChem training
courses at Pfizer and which was related to real processes, will demonstrate the data
flow and the way of model generation for the scale-up of an exothermic semi-batch
reaction
Starting point is a simple reaction
k1
A + B P r1 = k1 [A] [B]
k2
A + P SP r2 = k2 [A] [P]
This reaction was run in the lab at 60oC and there were seen these 4 species
with a mass balance close to 100% . An analytical method was developed and
Relative Response Factors were measured. The reaction was followed against
an Internal Standard and so the HPLC data could be converted to absolute mol
data
15. Example System
This reaction system element in DynoChem is presented by a block of lines
Reactions in Bulk liquid
k> 1.00 E-03 L/mol s at 60 C Ea> 60 kJ/mol dHr 0 kJ/mol * A + B > P
k> 1.00 E-04 L/mol.s at 60 C Ea> 60 kJ/mol dHr 0 kJ/mol * A + P > SP
In this block there are estimated values for the rate constants and the Activation Energy and there
is no value of the exotherm (dHr = 0 kJ/mol) available, which will probably be the knowledge in an
early development stage.
If not otherwise indicated (it can be done if required), DynoChem assumes that the reactions after the * are
elementary reactions, so the rate laws are strictly first order in each component
i.e. d[P]/dt = k [A] [B] and d[SP]/dt = k [A] [P]
To get real rate parameters (k1 , k2 ,Ea1 , Ea2 ), a set of 4 experiments were performed
with a different ratio of [A]o / [B]o and at 4 different temperatures (40 o C, 50o C, 60o C,
and 70o C)
16. Example System
After fitting all the experimental data can be reproduced with just four rate parameters
k1, k2, Ea1 , Ea2
17. Example System
With a calorimetric experiment the individual heat of reactions can be determined
as well:
Reactions in Bulk liquid
k> 2.7 E-03 L/mol s at 60 C Ea> 60 kJ/mol dHr -150 kJ/mol * A + B > P
k> 5.0 E-04 L/mol.s at 60 C Ea> 90 kJ/mol dHr -80 kJ/mol * A + P > SP
18. Example System
Process Safety data were generated directly together with the calorimetric run, when
a sample of the reacted mixture was subjected to a thermal stability investigation with
an ARC (Accelerating Rate Calorimeter)
This revealed a dangerous decomposition reaction at a higher temperature. The
kinetics of this decomposition was evaluated from the ARC data with DynoChem
and the result could be included in the kinetic description:
Reactions in Bulk liquid
k> 2.71E-03 L/mol.s Tref 60 C Ea> 59.997 kJ/mol dHr -149.86 kJ/mol * A + B > P
k> 5.02E-04 L/mol.s Tref 60 C Ea> 90.011 kJ/mol dHr -80.60 kJ/mol * A + P > SP
k> 5.00E-07 1/s Tref 60 C Ea> 140.000 kJ/mol dHr -420.00 kJ/mol * P > Dec
These data will not have a big impact on the reaction at 60o C, but are of major
importance for the safe scale-up:
19. Example System
These data allow now the prediction of the product composition for any ratio
of A and B (where A can be added by a dosing system over any given time)
at any given reasonable temperature as a function of time
For scale-up there is no given temperature, but the reaction temperature is the
result of the interplay between heat generation and heat removal.
Here we need to add a jacket to our model, and provide the parameters
As we want to predict temperature changes, we need to use reasonable good values
for the physical properties of the reaction mixture and the feed, for example cp
These data can be estimated or measured by the same calorimetric experiment where
the heat flows were obtained
20. Example System
The following lines describe the heat exchange between a reaction mass
and a jacket
Cool Bulk liquid with Jacket
UA 310.3 W/K
UA(v) 0.82 W/L.K
Temperature C
Cp 2.2 kJ/kgK
coolant 5.5 kg/s
Here the heat transfer UA is defined as a linear function of the liquid phase
volume with an intercept of 310.3 W/K and a slope of 0.82 W/L.K, so that
AU can be adjusted in case of a semi-batch reaction
These heat transfers can be measured or calculated by DynoChem with
a heat transfer tool
21. Example System
The following simulation shows the temperature profile of a 1000 L run with a simple
Tr-controller implemented with a feed time of 1 hr
22. Example System
With a feed time of 2 hrs and less excess of B the result looks like this
23. Example System
It appears that we are now in a position to design our process to get a combination
of the best temperature, the best feed time, the best ratio of A/B, and the best use
of reactor time as a function of scale and equipment
This is indeed possible and DynoChem has a built in functionality, which can
optimize any given process outcome or user provided functionality, for example
a whatever complex cost function.
This is tempting, however, we need to consider Process Safety as well
One of the standard scenarios in Process Safety is the question of the system
behaviour in case of a loss of cooling capacity in the worst possible moment.
This question can be answered by setting the cooling capacity to 0 and calculating
the temperature profile for this adiabatic system
25. Example System
A simulation run at 60 oC with a loss of cooling capacity at the end of the feed
(this is the stoichiometric point and the worst point in our system) will give a thermal
explosion (run-away) about 3 hrs later!!
This 3 hrs time is called Time to Maximum Rate (TMR) and can be used as a
quantitative measure of thermal risk
Once we agreed to an acceptable thermal risk (may be 8 hrs), we can then include
this in the optimization
At a first view this risk is likely to be a function of the reaction temperature, and we
might think that lowering the temperature will reduce the risk
This might be wrong! A simulation with a starting temperature of 20 oC will give the
result shown on the next slide
27. Example System
Keeping all other parameters constant, there is usually a temperature where TMR
Is a maximum, as shown below (for a 2 hrs feed time)
10
rxn time after end of feed for 99% conv [h]
15
8
6 10
TMR [h]
4
5
2
0 0
20 30 40 50 60 70
Tr set [C]
28. Summary
It is now possible to include the thermal risk into the optimization of the large scale
operation conditions
As a result we will get a process optimized with the consideration of scale and
equipment, i.e. a change of scale and equipment will change this optimum
This concept can be used to transfer a process from
Lab to Kilo Lab
Kilo Lab to Pilot Plant
Pilot Plant to small scale Manufacturing
Transfer within Manufacturing between different scales
Transfer between different equipment types, i.e.
Batch / Semibatch to Plug Flow or CSTR !
This is a significant advantage over traditional Process Development