1st Edition
Experimental Design and Process Optimization
Experimental Design and Process Optimization delves deep into the design of experiments (DOE). The book includes Central Composite Rotational Design (CCRD), fractional factorial, and Plackett and Burman designs as a means to solve challenges in research and development as well as a tool for the improvement of the processes already implemented. Appropriate strategies for 2 to 32 factors are covered in detail in the book.
Initial Considerations
Topics of Elementary Statistics
Introductory Notions
General Ideas
Variables
Populations and Samples
Importance of the Form of the Population
First Ideas of Interference on a Normal Population
Parameters and Estimates
Notions on Testing Hypotheses
Inference of the Mean of a Normal Population
Inference of the Variance of a Normal Population
Inference of the Means of Two Normal Populations
Independent Samples
Paired Samples
Linear Relationship between Two Quantitative Variables
Quantification of a Simple Linear Relationship
Functional Relationship amongst Two Variables
Understanding Factorial Designs
Introductory Concepts
Completely Randomized Experimental Designs with a 2k Factorial Scheme
Factorial 22 with Non-Significant Interaction
The 22 Factorial without Repetitions
Factorial Fractions with Two Level
General Concepts
Half Factorials: ½ Fraction
Quarter Factorials: ¼ Fraction
Comparison of the Methodologies: Study of One Variable at a Time versus Factorial Design
Introduction
Case Study - Evaluation of the Effects of pH and Temperature on the Activity of an Enzyme
Experimental Strategy for Fractional Factorials and the Central Composite Rotational Design (CeRD)
Introduction
Case Study - Experimental Design for 2 Independent Variables
Case Study - Experimental Design for 3 Independent Variables
Case Study - Experimental Design for 4 Independent Variables
Case Study - Experimental Design for 5 Independent Variables
Case Study - Experimental Design for 6 Independent Variables
Case Study - Experimental Design for 7 Independent Variables
Case Study - Experimental Design for 8 Independent Variables
Selection of Variables
Fundamental Theory of the Plackett and Burman (PB) Designs
Locating the Problem
Hadamard Matrices
Some Properties of the Designs
PB Matrix Design
Final Considerations
Matrices of the PB Design
Recommendations
Matrices of the PB Design
Determination of the Main Effects and Calculation of the Deviations for PB Designs
Case Study using PB Design
Case Studies - Applications in Product Processes and Formulations
Case Study - Synthesis of Dextran - Analysis of the Model as from the Coded and Real Values
Case Study - Development of Bread with Substituted Ingredients
Case Study - Alkalization Process of Cocoa Nibs (Theobroma Cacao L.) and Evaluation of Quality
Case Study - Batch Distillation of the Natural Aroma of Cashew Fruit
Case Study - Evaluation of Curvature in Fractionated and/or Plackett and Burman (PB) Designs where the Central Point Responses are Lower or Higher than the Other Treatments
References
Tables
Biography
Maria Isabel Rodrigues is a professor at the University of Campinas in Brazil. She received her BS, MS, and PhD degrees in food engineering from the University of Campinas, Brazil. Dr. Rodrigues has taught courses of experimental design and process optimization at a postgraduate level at the University of Campinas, in private companies, and at other universities and institutions. She has worked as a consultant using this statistical tool in various specialty areas such as bioremediation, developments in microbial analytical methods, and fermentation and enzyme processes as well as in the automotive, chemical, petrochemical, cosmetic, pharmaceutical, and food industries.
Antonio Francisco Iemma has been a university-level teacher for more than 40 years. He has taught mathematics and biostatistics at the University of Ribeirão Preto, the Universidade Estadual Paulista, and the University of São Paulo. He received his master’s and doctoral degrees in statistics from the University of São Paulo, Brazil. He did his postdoctoral work at the Faculté Universitaire de Sciences Agronomiques de Gembloux in Belgium. Dr. Iemma has been a visiting lecturer at universities in Brazil and other countries such as Argentina, Belgium, Columbia, Cuba, and France, among others. He is also the former manager of biostatistics in the experiment optimization sector for GlaxoSmithKline Biological in Rixensart in Belgium.
