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optimizationtechnique-160421053209.pdf
1. OPTIMIZATION TECHNIQUES
Apr 21, 2016 sagar kishor savale 1
Mr. Sagar Kishor savale
Department of Pharmaceutics
avengersagar16@gmail.com
2015-2016
Mr. Sagar Kishor savale
Department of Pharmaceutics
avengersagar16@gmail.com
2015-2016
Department of Pharmacy (Pharmaceutics) | Sagar savale
3. For Formulation or Process
Enter around 1970’s
Search for the best result
Introduction1-2
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4. Definition1-2
It is defined as “To make as perfect, effective or
functional as possible”.
How we can make Formulation perfect ?
What should be characteristics?
What should be the conditions?
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6. CLASSIC OPTIMIZATION3
• Application to unconstrained problem
• Finding maximum or minimum of a function of independent variable
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Y= f(X) , where
Y- Response
X- Single independent variable
Y= f(X1, X2)
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8. APPLIED OPTIMIZATION METHODS3
A) EVALUTIONARY OPERATION
B) SIMPLEX METHOD
C) LAGRANGAIN METHOD
D) SEARCH METHOD
E) CANONICAL ANALYSIS
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9. Design Of Experiment4
(DOE)
It is a structured, organized method used to determine
relationship between the factor affecting a process and
output of that process.
Reduce experiment time
Reduce experimental cost
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10. Phases Of DOE4
Determine the goal
Identifying affecting factors
Selection of Experimental design
Generating a Design Matrix
Conducting an Experiment
Finding the optimum
Results 10
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11. Parsimony Principle4
This principle states that, some of the factors are
important while others are not.
80-20 rule- few variables more effects
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12. Types of Experimental Design1-4
Completely Randomised Design
Randomised block Design
Factorial Design
Response surface design
Three level full factorial design
Full Factorial Design
Fractional Factorial Design
Central Composite Design
Box- Behnken Design
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13. • Completely Randomized Design
Comparison of response values
Levels of factor are randomly assigned
• Randomized Block Design
To control nuisance factors
Block of non-significant factors
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14. Factorial Design
For evaluation of multiple factors simultaneously.
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means 2 is level while 3 is factor
Factorial Design is divided into two types-
- Full Factorial Design
- Fractional factorial design
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15. Full Factorial Design
– Simplest design to create, but extremely inefficient
– Each factor tested at each condition of the factor
– Number of runs (N)
N = yx
Where, y = number of levels,
x = number of factors
E.g.- 3 factors, 2 levels each,
N = 23
= 8 runs
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17. 2X
Design
2 = Level
X = Input Factors
Number of
factors
Number of runs
2 4
3 8
4 16
5 32
x1
x2
x3
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18. Fractional factorial design
– Means “less than full”
– Levels combinations are chosen to provide
sufficient information to determine the
factor effect
– More efficient
– Used for more than 5-factors
x1
x2
x3
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20. Response Surface Design
It is a model that describes a continuous curve or surface that
connects the measured data taken at strategically important
places in the experimental window.
Studying the response in the form of small surface.
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21. • It can be performed by two types
– Central Composite Design
– Box Behnken Design
Response Surface Design
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22. Central Composite Design
• Also called as Box Wilson Design
• It contains fractional factorial design with center points
surrounded by star points.
• Study of system by varying the parameters around a central
point.
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26. Three Level Full Factorial Design
Three levels
- Low
-Intermediate
- High
Prohibitive because high number of runs, cost and efforts.
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=27 runs
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28. Summary
Design Merits Limitations
Full Factorial Screening of factors Limited runs
Fractional Factorial
Design
For maximum
number of factors
Effects are not
uniquely estimated
Response surface
design
Curves of response
graphically
Become complex if
maximum number
of factors
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30. Advantages
Helps to determine important variables
Helps to measures interactions.
Allows extrapolations of the data and search for the
best possible product .
Allows plotting of graphs to depict how variables
are related.
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32. Conclusion
Immense potential in development of pharmaceutical product
and processes
Less involvement of men, material, machine and money
Improvement in formulation characteristics.
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33. REFERENCES
1) Bolton S, BonC.Pharmacutical statistics practical & clinical
application, 5th
ed. New York London ;informa healthcare
publishing ; 2010.p. (223- 39,424-51).
2) Jain NK,Pharmaceutical Product Development, New Delhi ;
CBS Publisher ; 2010. p. 295-340.
3) Schwartz JB,Connere RE,Schnaar RL,In: Banker GS & Rodes
CJ , editor . Modern Pharmaceutics, 4th
ed. informa healthcare
publishing ; 2010.p. 727-728.
4)Hirmanth RR,Vanjaka KI , Textbook of Industry Pharmacy
;Drug Delivery System and Cosmetics and Herbal Drug
Technology ; 2009.p.148-68.
5) Lewis GH, Mathieu DG, Pharmaceutical experimental design;
Dekker series publishing;Vol-92; 2008. p. 237-240.
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