Design Of Experiments For Mixtures

Mixture Designs to Optimize Formulations Using R: Simplex Lattice Designs, Simplex Centroid Designs, D-Optimal Designs.

What you'll learn

Understand the differences between factorial designs and mixture designs

Simplex lattice and simplex centroid designs

Simplex augmented designs

Build and analyze mixture designs for three components

Build and analyze mixture designs for four components

Interpret triangular contour plots

Build and analyze mixture design with constrains

Build and analyze D-optimal mixture designs

Requirements

The student must be familiar with the basic concepts of the design of experiments such as:

Analysis of variance (ANOVA)

Design of experiments for optimization (response surfaces)

Description

Welcome to "Design of Experiments for Mixtures"!Whether you're a scientist, an engineer, a researcher, or just someone interested in creating, perfecting, or innovating products with mixtures, this course will help you understand the principles of mixture designs.Mixtures are everywhere in our daily lives, from food recipes to pharmaceutical and chemical formulations, and material development. However, optimizing these mixtures is often a challenging task, as they involve multiple components, that interact among themselves to give the final properties of the product. Traditional experimental approaches may not be suitable for a clear understanding of these interactions, which is where the concept of "Design of Experiments" (DOE) specifically tailored to mixtures comes into play. This course will delve into the fundamental principles of mixture designs.We will start our journey by identifying when to use mixture designs instead of a traditional design of experiments approach and learning how to read and interpret plots in triangular coordinates. In the next step, we will learn the best approaches to distributing design points throughout a triangular surface using Simplex Designs. By then, we will be ready to dive into several real Case Studies from the food and pharmaceutical areas, covering different aspects of mixture designs and analysis. Finally, we will see Case Studies where the mixture variables have constraints and cannot vary over the whole mixture space.This is not a beginner course; it's essential to have some previous knowledge of DOE before enrolling on "Design of Experiments for Mixtures".The analysis of the data will be performed using R-Studio. This is not an R course; this way, it is desirable that students have some familiarity with R. The R codes and the data files used in the course can be downloaded, the functions will be briefly explained, and the codes can be easily adapted to analyse the student’s data.Any person who performs mixture experiments can benefit from this course, mainly researchers from the academy and the industry, Master and PhD students and engineers.Through a combination of theory and practical examples, you'll gain the skills and knowledge needed to design and analyse experiments with mixtures effectively.

Overview

Section 1: Introduction

Lecture 1 Course Presentation

Lecture 2 Installing R and R Studio

Section 2: Introduction to Mixture Designs

Lecture 3 Why Mixture Designs?

Lecture 4 Understanding Triangular Plots

Lecture 5 Designs for Triangular Plots: Simplex Lattice

Lecture 6 Designs for Triangular Plots: Simplex Centroid

Lecture 7 Models to Fit Mixtures

Lecture 8 Building Mixture Designs in R

Section 3: Mixture Designs with 3 Components

Lecture 9 Introduction to Case Study 1: Formulation of a Mixed Berry Fruit Juice

Lecture 10 Analyzing Mixture Designs in R

Lecture 11 Choosing Among Different Models and Checking Residuals Assumptions

Lecture 12 Building Contour Plots and Interpreting the Results

Lecture 13 Simplex Augmented Designs

Lecture 14 Introduction to Case Study 2: Drug Formulation

Lecture 15 Analyzing a Simplex Augmented Design

Section 4: Mixture Designs with Four Components

Lecture 16 Case Study 3: Optimizing Flavonoid-Rich Mixed Food Formulation

Lecture 17 Analysing a Mixture Design with Four Components

Section 5: Mixtures with Constrains

Lecture 18 Constrained Mixture Designs

Lecture 19 Case Study 4: Development of Omega-3 Loxoprofen-Loaded Nano-Emulsion

Lecture 20 Building a Simplex Lattice A[3,3] design with constrains in R

Lecture 21 Analysing a Simplex Lattice A[3,3] design with constrains in R

Section 6: Mixture Designs for Irregular Polyhedrons

Lecture 22 Introduction to Constrained Designs with Irregular Surfaces

Lecture 23 Building Constrained Designs with Irregular Surfaces in R

Lecture 24 Case Study 5: Analysing Constrained Designs with Irregular Surfaces in R

Section 7: Closing

Lecture 25 Closing Remarks

Researchers;,Graduate students;,Engineers;,Anyone who works with formulations and blends in the chemical, pharmaceutical, cosmetic, food and construction industries.