Process Modeling, Optimization and Innovation
A.Y. 2023/2024
Learning objectives
The course aims to provide students with the knowledge related to the most used modeling and optimization methods in the food industry. The course also aims to provide students with the knowledge related to innovative technologies their applications.
Expected learning outcomes
At the end of the course, students will be able to construct, through the use of dedicated software, statistical models capable of describing a production process to optimize operating conditions and predict the properties of the finished product. Students will also be able to evaluate the potential applications in the various food processes of the most innovative technologies.
Lesson period: First semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
First semester
Course syllabus
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.
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.
Prerequisites for admission
Knowledge of the main processes applied in the food industry is required, as well as some basic notions of statistics.
Teaching methods
The course is divided into lectures (4 CFU) and exercises in the computer lab (2 CFU).
Teaching Resources
The files of the material used in class are supplied with an explanatory text (theory, exercises and exercises solved).
For the practical part of the computer lab, the texts of the exercises are provided with notes on the applied theoretical principles.
All teaching material is present on Ariel.
Recommended books:
- "Design and analysis of experiments", Douglas Montgomery, McGraw-Hill.
- "Engineering statistics" http://www.itl.nist.gov/div898/handbook/index.htm
- Response Surface Methodology. Process and product optimization using design experiments. Wiley series in probability and statistics. Raymond H. Myers, Douglas C. Montgomery, Christine M. Anderson-Cook
- "Formulation Simplified: Finding the Sweet Spot through Design and Analysis of Experiments with Mixtures", M.J. Anderson, P.J. Whitcomb, M.A. Bezener
For the practical part of the computer lab, the texts of the exercises are provided with notes on the applied theoretical principles.
All teaching material is present on Ariel.
Recommended books:
- "Design and analysis of experiments", Douglas Montgomery, McGraw-Hill.
- "Engineering statistics" http://www.itl.nist.gov/div898/handbook/index.htm
- Response Surface Methodology. Process and product optimization using design experiments. Wiley series in probability and statistics. Raymond H. Myers, Douglas C. Montgomery, Christine M. Anderson-Cook
- "Formulation Simplified: Finding the Sweet Spot through Design and Analysis of Experiments with Mixtures", M.J. Anderson, P.J. Whitcomb, M.A. Bezener
Assessment methods and Criteria
The exam will be written with theoretical questions and with exercises first with a calculator and statistical tables, then with the Design Expert software.
AGR/15 - FOOD SCIENCE AND TECHNOLOGY - University credits: 6
Computer room practicals: 32 hours
Lessons: 32 hours
Lessons: 32 hours
Professor:
Hidalgo Vidal Alyssa Mariel
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