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Mixture Design for Optimal Formulations*
The Recipe for Success
If you do product formulation, then standard factorial designs just don't work. You need the mixture designs taught in Mixture Design for Optimal Formulations to experiment most effectively.
Develop statistical models of product performance. Then use response surface methods to identify the "sweet spot" where all specifications can be achieved.
Ingredients for Efficient Experimentation
During the Mixture Design for Optimal Formulations workshop you will:
• Set up simplex designs
• Augment and evaluate design quality
• Select appropriate mixture models
• Generate contour plots in triangular space
• Design for constrained mixture variables
• Optimize product formulas
• Screen mixture components
The optional texts, "Experiments with Mixtures" by Cornell and "Experimental Design for Formulation" by Smith, provide valuable reference material.
"Very knowledgeable and helpful instructors."
—Rich Griffin, Research Chemist |
Produce Contour Maps in Mixture Space
Design-Expert ? software helps you practice designing and analyzing mixture experiments throughout the workshop. The software provides the power for generation of d-optimal designs, as well as sophisticated graphical outputs such as trace plots. You will learn how these methods work and what to look for.
*Prior Knowledge of DOE Recommended
Knowledge of elementary statistics and analysis of variance is recommended. Assess your abilities by taking the free Self-Assessment Questionnaire available at www.statease.net . If you aren't ready for the Mixture Design for Optimal Formulations workshop, take the online PreDOE course first for only $95.00 (takes 3-6 hours to complete, you can work at your own pace). Before attending class, please go through the One-Factor Tutorial, and then look at the One-Factor Self-Study presentation. You will find links to both below.
*Courses Outline:
Mixture Designs for Optimal Formulations (3 Days)
Section 1—Introduction to Mixtures
· What makes a mixture?
· Mixture (Scheffe) polynomials
· Mixture tutorial
· Gold simulation
Section 2—Simplex Design
· Simplex-Lattice designs Augmenting simplex designs Interpreting model coefficients Design evaluation
· Simplex-Centroid designs Model reduction
Section 3—Constrained Mixtures, Simplex
· Mixture tutorial revisited: actual–real–pseudo
· Optimization of multiple responses Propagation of error (POE) Cement optimization
· ABS Pipe: Model reduction & optimization
· Homework assignment
Section 4—Constrained Mixtures, Extreme Vertices
· Constrained mixtures, extreme vertices: shampoo
· Algorithmic point selection: flare
· Simulation of extreme vertices: fruit punch Ratio constraints: stability An additional equality constraint
Section 5—Combining Mixture and Process variables
· User defined cross designs: fish
· D-optimal crossed designs: fish and cure time
· Mixture amount experiments: Ibuprofen
· Mixtures with categorical factors: paint
Section 6—Screening Components · Simplex: Additive package
· Extreme vertices: Snee eight ingredients
· Blue Haze
Section 7—Application & Review
· Homework
· Optional review exercises
Section 8—Appendix
· Using ratios
· Slack variable polynomials
· Explanation of optimization techniques
· Piepel's vs. Cox's direction.
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MQIP Quality Management Intelligence Tank
Rm.1314, No.27 Shui Yin 2 nd Heng Rd. GuangZhou , China
p: 020-33689262,37597781, f: 020-37597782 |
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