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SIMULATION TOOLS FOR FINE CHEMICAL INDUSTRY Moshe Bentolila

SIMULATION TOOLS FOR FINE CHEMICAL INDUSTRY€¦ · Ppt0000007 [Read-Only] Author: Administrator Created Date: 7/25/2011 1:15:49 PM

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Page 1: SIMULATION TOOLS FOR FINE CHEMICAL INDUSTRY€¦ · Ppt0000007 [Read-Only] Author: Administrator Created Date: 7/25/2011 1:15:49 PM

SIMULATION TOOLS FOR

FINE CHEMICAL

INDUSTRY

Moshe Bentolila

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Contents

• Common Questions

• The Goal

• Pharmaceuticals Industry – Quality By Design

• Typical Mixing Parameters

• Data and Results Management

• Data Base Motivation

• R&D, Design, QbD and Process Safety Examples

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Common Questions

�Did we cover the main parameters during the process

development?

�Will our facilities will be appropriate for the developed process?

�Does the equipment offer is good for the process?

�What about safety and runaway scenario?

�Do our process is robust?

�Does the operational range parameters are large enough for

the manufacture facilities?

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The Goal Once the Science of the process (Chemestry, Biology or physics) is

known well, a common situation during the process transfer from lab to

production or from site to site is the gap between the old and new

results.

�Our first goal is to develop a process that will run properly in the

first trial on a new scale or site, similar to our successful results in the

lab or in the old facility.

In order to achieve this, we need to evaluate the process with the same

conditions we will have in the production phase.

�The main parameters we change are the hydrodynamics of the

system. If we are able to identify and control these parameters we

will be able to achieve to the available and optimal solution.

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Pharmaceutical Industry

• Active Pharmaceutical Ingredient (API)

11 י ולי 25

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Pharmaceutical Quality by Design• This FDA imperative is best outlined in its report

“Pharmaceutical Quality for the 21st Century: A Risk-Based

Approach.”[*]

• In the past few years, the Agency has made significant

progress in implementing the concepts of "Quality by

Design" (QbD) into its pre-market processes.

• The focus of this concept is that quality should be built into a

product with a thorough understanding of the product and

proceess by which it is developed and manufactured along

with a knowledge of the risks involved in manufacturing the

product and how best to mitigate those risks.

• This is a successor to the "quality by QC" (or "quality after

design") approach that the companies have taken up until

1990s.* Pharmaceutical Quality for the 21st Century: A Risk-Based Approach http://www.fda.gov/oc/cgmp/report0507.html

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QbD activities within FDAOverall implementation of QbD:

•In FDA’s Office of New Drug Quality Assessment (ONDQA), a

new risk-based pharmaceutical quality assessment system

(PQAS) was established based on the application of product and

process understanding.

•Implementation of QbD for a Biologic License Application (BLA)

is progressing.

While QbD will provide better design predictions, there is also a

strong recognition that industrial scale-up and commercial

manufacturing experience provides new and very important

knowledge about the process and the raw materials used

therein. This vigilant and nimble approach is explained by FDA to

be essential to best protect the consumer (patient).

http://en.wikipedia.org/wiki/Quality_by_Design

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API Batch Process Simulation – Scale Up Methodology. Roberto Novoa and Moshe Bentolila

QUALITY BY

DESIGN

Establish:

Critical

Quality

Attributes

Determine:

Critical

Process

Parameters

Define:

Design

Space

Set:

Control

Strategy

Quality by Design – Statistical Challenges

Yi Tsong,.

CDER, FDA 2007 FDA/Industry Statistics Workshop

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Typical Mixing ParametersReview of the Main Mathematical Models Used in VisiMix – www.visimix.com

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Mixing Simulation Software

R&D

Production

Design

QbD

Data and Results Management

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Data Base Motivation

o The need for maximizing efficiency of a mixing process by compiling all its elements into a common database.

o The need for better understanding between the mixing process and its impact on the entire process and a simple solution to achieve this

o The need to create a common language within the company in order to achieve better communication with regards to these processes.

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Integration of DataBase and Language

How does it work?

Equipment Data

New Process

VisiMix

VisiMix

Create Project for Equipment Data

Excel File Data

Equipment DataBase

Create Project for Process

Excel File Data

Process Knowledge DataBase

VisiXcel

VisiXcel

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Common Language

Designing Engineer

PilotEngineer

ProductionEngineer

QA

DevelopmentChemist

Equipment “Database”

Process Knowledge “Database”

Common Language

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Data Base Typical Spreadsheets

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R&D - Example This example is based on a practical case application of VisiMixTurbulent by one of our clients. It is related to development of a new process of organic synthesis in a chemical plant that belongs to one of the big international pharmaceutical corporations.

One of the stages of this synthesis - a relatively fast chemical reaction between two reactants accompanied with a side reaction.

Schematically: the main reaction as A + B > C with the side reaction as B+B > D.

On the development stage, a low quantity of by-product and acceptable degree of purity of the main product was obtained in a batch pilot reactor (total volume - about 2 liter).

The process was transferred to a production glass lined 1230 liter reactor.

Scaling-up was based on reproduction of mixing time value. However, a significant deterioration of the product purity and increase of by-product concentration occurred.

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R&D - Example

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R&D - Example

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R&D - Example

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R&D - Example

• Semibatch Operation

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R&D - Example

Example Conclusion:

�Reagent B was not well distribute in the

reactor.

�Macromixing time was not enough to avoid

to malfunction in the production reactor.

�A semibatch operation was adopted and gave

positive results.

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Design Example

CRYSTALLIZER. SCALING-UP•The process rate and particle size distribution in crystallization and precipitation processes are dependent on chemical composition and physico-chemical properties of the system.

•In the same time they can be substantially dependent also on some phenomena that are functions of mixing conditions – for example, on primary and secondary nucleation, attrition and breakage of crystals, distribution of solid phase and liquid-solid mass transfer.

•The following example is related to a particular case when the crystallization is controlled mainly by these parameters, and scaling-up conditions include reproduction of these phenomena.

•The corresponding parameters selected from the list of VisiMix outputs and used below for crystallization scale-up are presented in the Table.

This example is based essentially on the article ‘Optimizing Crystallizer Scaleup’ by Wayne

J. Genk , Chem. Engng. Progress, June 2003, pp. 36-44

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For calculation of the Mass transfer coefficient, it is

necessary to enter a number of additional initial data,

including the Diffusivity of the solute. In our case the

problem consists not in prediction, but in reproduction

of the same value of the Mass transfer coefficient.

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Reactor Configurations

Pilot Production Design

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Results

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Methodology (J.M. Berty, CEP, 1979)(J.M. Berty, CEP, 1979)

LABORATORY(R&D)

PILOT(Pilot)

Demo – Simulation (Visimix, Dynochem,CFD)

PLANT (Production)

BENCH SCALE

(RC1,Mini Pilot)

LABORATORY(R&D)

PILOT(Mini Pilot)

PLANT (Pilot,Production)

BENCH SCALE

(RC1, HEL)

Scale Down

Final Disign

Build

11 י ולי 25

QbD

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Lab and Prod

Calculations

Moshe Bentolila, Roberto Novoa, and Wayne Genck, Michal Hasson, Efrat Manoff, "Computer Aided Process

Engineering at Chemagis" , PHARMACEUTICAL ENGINEERING July/August 2011. 30-38

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Non ideal stirring – non homogeneity• Before performance of scale up experiments

VisiMix simulation was used to check

suspension at different Mini Pilot Reactors:

Reactor 7603 7605 7605 7607 Volume, L 10 25 25 50 RPM 500 (Max) 400 500 (Max) 150 (Max)

Main Characteristic

Liquid – Solid Mixing

Solid suspension quality

Complete suspension is questionable.

Partial settling of solid phase may

occur.

Complete suspension is

expected.

Complete suspension is

expected.

Complete suspension is questionable.

Partial settling of solid phase may

occur. Max. degree of non uniformity of solid

distribution

AXIAL, % 22.3 10.3 29.1 132 RADIAL, % 65.7 34.3 76.3 90.8

Not all Mini Pilot reactor are capable of

full suspension of POCA.

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Process parameters vs. constraints

Case Target Function Yield EOR [hr] Stirrer [rpm] TEMP [0C]

AHigh demand to the product and the reactor. Yield →max, EOR≤8

98.1 8 546 26

BThe price of the product equals 10 times the value of reactor availability. (10·yield-EOR)→max

98.4 8.3 623 25

C

High demand of the product with low availability of reactors. One hour of available reactor equals 10 times yield. (1·yield-10·EOR)→max

95.2 1.5 483 39

DHigh availability of reactors, High cost of impurity purification. (10·yield-10·IMAM-1·EOR)→max

98.9 14.4 637 20

Bentolila, M., Kennet, R., “Scale-up Optimization Using Simulation Experiments,”

presented at the American Institute of Chemical Engineers (AIChE), Palm Springs

California, 11-16 June 2006.

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SECOND ORDER EXOTHERMAL REACTION IN A BATCH

REACTOR

•This example shows how to simulate 2nd order

exothermal reaction carried out in a stirred batch

reactor.

•VisiMix performs simulation of exothermal reaction

based on the analysis of the equipment and process

parameters, and helps avoid runaway reaction that

may take place when the energy generated by the

reaction is greater than the energy removed from the

reactor.

Process Safety

Example

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Problem description: A second order irreversible reaction is carried out in a stirred batch reactor. This reaction is run according to the stoichiometry A + B C → The equation for the reaction rate is: r = k CA CB where

r is reaction rate, moles of A/(L⋅sec);

k is reaction rate constant, L/(mole⋅sec); CA is concentration of reactant A, mole/L; CB is concentration of reactant B, mole/L. The reaction rate constant is a function of the system temperature and is given by k = k0 e -E/RT where

k0 is Arrhenius constant, L/(mole⋅sec); E is energy of activation, kJ/mole;

R is gas law constant, J/(mole⋅K);

T is absolute temperature, K.

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Results

Before Cooling Operation After Cooling Operation

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Conclusion

�� VisiMix Product and VisiMix Product and Data base allows you Data base allows you

to manage your equipment and projects to manage your equipment and projects

together with a relevant parameterstogether with a relevant parameters

�� Common language for all the company Common language for all the company

involves in the new process runninginvolves in the new process running

�� Present a wide view and platform for a Present a wide view and platform for a

well QbD (quality by Design) study.well QbD (quality by Design) study.