If you have previously run experiments to identify the key factors and more appropriate ranges covering an optimum (i.e. there is evidence of curvature), then you can use knowledge previously gained to construct an optimisation design from new via the Response Surface tab. This will enable you to gain a detailed understanding of the experimental space you have arrived at; in particular to locate the most favourable settings/ranges.
You can also augment your existing experiments to improve the predictive power of your previous model for optimization purposes. You can either add axial (star) points to transform a screening design into a
Central composite design, or use the
RSM D-optimal option to select the additional runs needed to update your model to characterise optima (c.f. augmenting designs for optimization tipsheet).
To handle optimization designs within the Design Expert DX7 software tool, we have further tipsheets to help you:
Optimization Designs: Building the Design
Optimization Designs: Analysing your Results
Optimization Designs: Diagnosing your Results
Optimization Designs: Interpreting your Results
These tipsheets are linked to this case study
Granulation Process
This top-spray granulation process has four key process and ingredient parameters:
- Magnesium Stearate
- Granulation Paste Type
- Filler (Dibasic Sodium Phosphate)
- Granulation Blend Time
and three key properties of tablets of a drug product:
- Hardness
- Degradation Rate
- Dissolution
A screening study has established improved ranges for the four key factors known to affect the process. Two non-critical or qualitative factors have been set at preferred (optimal) settings. An optimisation design is needed to locate a sweet spot for the process and predict ranges for the factors to deliver a design space that both conforms to the specified targets (Goals) and is a workable solution on plant
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