Process Modeling, Optimization and Innovation

1. Introduction to process modeling. Comparison between the fundamental and the empirical approach. Principles, definitions, aims, examples of application.
2. The different statistical methods used for model building: linear, non linear and weighted regressions. The least square method to estimate model parameters.
3. Principles, aims and procedures of experimental design. The choosing criteria among the different experimental designs. The analysis of variance. The one factor comparative design and the use of blocking. The two-level full and fractional factorial designs. The principles of confounding and design resolution. Methods to improve the resolution of fractional design. The Plackett-Burman design. The addition of centre points to factorial design.
4. The response surface designs: Central Composite and Box-Behnken designs.
5. The optimization procedures for single or multiple responses. The steepest ascent method. The desirability function.
6. The mixture designs: simplex lattice and simplex centroids. Combining mixture and process factors: D-optimal design.
7. Thermal and non thermal novel food processing. High pressure, pulsed electric field, microwave, and ohmic heating processes.
- Computer practice for the modeling and optimization of food process and products by applying the different design methods. Softwares: Excel and Design Expert.