Skip to main content

Modern Mixture Experimentation

Download the Course Curriculum

NEW

Products and processes that involve mixtures of liquids, solids, or gases are pervasive and impact the sustainability of your organization. Mixture systems involve inherent constraints and complex kinetics, thereby making their behavior difficult to characterize and predict. This course focuses on designing and analyzing mixture experiments that enable you to predict and optimize system characteristics, which are critical to product and process design and quality.

This course introduces an innovative and cost-effective approach to designing and analyzing mixture experiments. You will design experiments that are much smaller and more cost-effective than traditional classical designs. You will apply a modern machine learning method, Self-Validating Ensemble Modeling (SVEM), to build accurate and stable predictive models of system behavior, using small data sets. A variety of design situations and numerous case studies will show you how to apply the methodology and demonstrate how well it works in practice.

Who Should Attend

Engineers, scientists, and researchers who work with mixtures, including chemical, specialty chemical, pharmaceutical, biotechnology, semiconductor, solar, consumer products, and automotive, and other that involve work with mixture designs and processes

Duration

2 days
Third day add-on option:  Special designs and analysis

Delivery methods

  • On-site classroom
  • Virtual live
  • Virtual on-demand

Prerequisites

No prior, formal training in design of experiments is assumed. Some familiarity with basic statistics is desirable. Options for gaining basic knowledge of statistics are available for participants without this background.

Key Outcome

You and your team will have the methodology and tools to construct cost-effective mixture experiments and fit accurate and stable models, thereby enabling you to leverage these models.

What You Will Learn

  • Mixture experiment terminology and applications
  • Shortcomings of classical mixture designs and approaches
  • Self-Validating Ensemble Modeling (SVEM) methodology for analyzing mixture experiments
  • Use of space-filling designs for a wide range of mixture situations
  • Optimization of the response surface over the design space
  • Optimization for multiple responses

Third day add-on option provides additional coverage:

  • Mixture and process factor experiments
  • Mixture of mixture experiments
  • Mixture experiments with functional responses

Other Add-on Options

  • Virtual office hours with instructors for questions, feedback, and discussion
  • Consulting services for project work and specific requirements

Request More Information about This Course

Please get in touch with us if you have questions or would like more information on how to register a group from your organization for this course.