Once influential factors are identified, the next goal consists of optimising their settings. This module covers the construction of experimental designs for optimisation. Data modeling is carried out with response surface methodology.
Optimization Designs & Response Surface Models
Course Details
Learning Objectives
Learning Objectives:
Upon completion of this module, participants will be able to:
- Select the factor and the levels to be tested
- Construct an optimisation design
- Construct a response surface model and interpret the results
- Assess the goodness-of-fit of the model
- Use the model to determine the optimal factor settings
- Plot the model using response surfaces and contour plots
- Make predictions and optimise the process using response surfaces
Target Audience
Target Audience:
This module is intended for researchers who conduct experiments or studies, who wish to optimize their model, process or formulation and who wish to determine optimal conditions using an appropriate optimization design.
Prerequisite
Prerequisite:
Participants should be familiar with the construction of basic factorial designs and the Analysis of Variance (ANOVA) method, or have followed the following courses or an equivalent:
Fundamental Tools in Statistics for Research
Introduction to the Design of Experiments DOE
Course Outline
Course Outline:
- Objectives and Context of Use of Optimisation Designs
- Issues involved in Optimisation
- Relevant Statistical Concepts
- The Need for an Alternative to ANOVA
- Construction of Optimization Designs
- Notion of Central Point and Repetitions
- Properties of Optimization Designs
- Central Composite Designs (CCD) and Box-Behnken Designs
- Use of Blocks
- Methodology of Response Surfaces
- Use of Models for Prediction and Optimization
- Advanced Methods
- Summary
Practical Info
Practical Info:
Course notes on statistical techniques
Sample datasets
Recommended Duration: 2 days
Course Materials:
