1. Design of Experiments (DOE)
Dr Subash Gopinath
School of Bioprocess Engineering,
UniMAP

2. Design of Experiments (DOE)
What is DOE?
Purpose of DOE?
Choose the design (Eg. Box-Behnhen)
Principle of selected design
How it works?
How do you calculate?
Conclusion

3. Design of Experiments
Factorial design
Regression analysis
Mathematical model
Statistical model
Response surface methodology
Central composite
Box-Behnhen design
Plackett Burmann model and etc.

4. Design of Experiments (DOE)
DOE is a formal mathematical method for systematically planning and conducting scientific studies that change experimental variables together in order to determine their effect of a given response.
DOE makes controlled changes to input variables in order to gain maximum amounts of information on cause and effect relationships with a minimum sample size.

5. Role of DOE in Process Improvement
DOE is more efficient that a standard approach of changing “one variable at a time” in order to observe the variable’s impact on a given response.
DOE generates information on the effect various factors have on a response variable and in some cases may be able to determine optimal settings for those factors.

8. BASIC STEPS IN DOE Four elements associated with DOE:
1. The design of the experiment,
2. The collection of the data,
3. The statistical analysis of the data, and
4. The conclusions reached and recommendations made as a result of the experiment.

9. Based on the results of the analysis, draw conclusions/inferences about the results, interpret the physical meaning of these results, determine the practical significance of the findings, and make recommendations for a course of action including further experiments

10. EXAMPLE: CONCLUSIONS
In statistical language, one would conclude that whether is not statistically significant at a 5% level of significance since the p-value is greater than 5% (0.05).

11. 2k DESIGNS (k > 2)
As the number of factors increase, the number of runs needed to complete a complete factorial experiment will increase dramatically. The following 2k design layout depict the number of runs needed for values of k from 2 to 5. For example, when k = 5, it will take 25 = 32 experimental runs for the complete factorial experiment.

12. Interactions for 2k Designs (k = 3)

13. 2k DESIGNS (k > 2)
For example, if there are no significant interactions present, you can estimate a response by the following formula. (for quantitative factors only)