Process Modeling, Optimization and Innovation
A.Y. 2025/2026
Learning objectives
The course aims to provide students with knowledge of the most commonly used modeling and optimization methods in the food industry. It also aims to introduce students to innovative technologies and their applications.
Expected learning outcomes
By the end of the course, students will be able to build statistical models using dedicated software to describe a production process, optimize operating conditions, and predict the properties of the final product. Additionally, students will be able to assess the potential applications of the most innovative technologies in various food processes.
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
Introduction to process modeling: Comparison between fundamental and empirical approaches. Principles, definitions, objectives, and application examples.
Different statistical methods for model construction: Linear, nonlinear, and weighted regression. The least squares method for parameter estimation.
Principles, objectives, and procedures of experimental design: Different experimental designs and selection criteria. Analysis of variance. One-factor comparative design and block design. Full and fractional two-level factorial designs. The concept of confounding effects and resolution. Methods for improving the resolution of fractional designs. Plackett-Burman designs. The addition of central points to factorial designs.
Response surface designs: Central Composite Design, Face Centered Design, Box-Behnken.
Optimization procedures for one or more response variables and the steepest ascent method. The desirability function.
Mixture designs: Simultaneous treatment of process variables and mixtures using D-optimal designs.
Innovative thermal and non-thermal processes for the food industry: High-pressure processing, pulsed electric fields, microwaves, and ohmic heating.
Computer-based exercises in the IT lab with practical examples of process and product modeling and optimization using the covered methodologies.
Software used: Excel and Design Expert or equivalent.
Different statistical methods for model construction: Linear, nonlinear, and weighted regression. The least squares method for parameter estimation.
Principles, objectives, and procedures of experimental design: Different experimental designs and selection criteria. Analysis of variance. One-factor comparative design and block design. Full and fractional two-level factorial designs. The concept of confounding effects and resolution. Methods for improving the resolution of fractional designs. Plackett-Burman designs. The addition of central points to factorial designs.
Response surface designs: Central Composite Design, Face Centered Design, Box-Behnken.
Optimization procedures for one or more response variables and the steepest ascent method. The desirability function.
Mixture designs: Simultaneous treatment of process variables and mixtures using D-optimal designs.
Innovative thermal and non-thermal processes for the food industry: High-pressure processing, pulsed electric fields, microwaves, and ohmic heating.
Computer-based exercises in the IT lab with practical examples of process and product modeling and optimization using the covered methodologies.
Software used: Excel and Design Expert or equivalent.
Prerequisites for admission
Students are required to have knowledge of the main processes applied in the food industry and basic statistics concepts.
Teaching methods
The course will be delivered through:
Lectures (4 ECTS) with digital support (PowerPoint presentations, Excel spreadsheets, statistical software) to provide theoretical knowledge for understanding and applying the explained methods.
Laboratory exercises (2 ECTS) in the IT lab, aimed at familiarizing students with problem-solving techniques.
Lectures (4 ECTS) with digital support (PowerPoint presentations, Excel spreadsheets, statistical software) to provide theoretical knowledge for understanding and applying the explained methods.
Laboratory exercises (2 ECTS) in the IT lab, aimed at familiarizing students with problem-solving techniques.
Teaching Resources
Lecture slides, supplemented by explanatory texts, exercises with solutions, and sample exam questions, available on the myARIEL portal.
Douglas Montgomery - Progettazione e analisi degli esperimenti, McGraw-Hill.
NIST Engineering Statistics Handbook - Link.
Myers, Montgomery, Anderson-Cook - Response Surface Methodology: Process and Product Optimization Using Design Experiments, Wiley Series in Probability and Statistics.
Anderson, Whitcomb, Bezener - Formulation Simplified: Finding the Sweet Spot Through Design and Analysis of Experiments with Mixtures.
Douglas Montgomery - Progettazione e analisi degli esperimenti, McGraw-Hill.
NIST Engineering Statistics Handbook - Link.
Myers, Montgomery, Anderson-Cook - Response Surface Methodology: Process and Product Optimization Using Design Experiments, Wiley Series in Probability and Statistics.
Anderson, Whitcomb, Bezener - Formulation Simplified: Finding the Sweet Spot Through Design and Analysis of Experiments with Mixtures.
Assessment methods and Criteria
The assessment consists of a written exam divided into two parts:
Problem-solving section (use of calculator and statistical tables), open-ended questions, True/False statements, and text completion related to theoretical knowledge (80 minutes - 17 points).
Computer-based task using Design Expert or equivalent software, including a related commentary (40 minutes - 9 points).
The evaluation of Innovative Technologies will be conducted through a research project (4 points), with topics and presentation dates agreed upon with the instructor.
The final grade, expressed in thirtieths, will be communicated via email. There are six annual exam sessions: two during each break between semesters and one during academic breaks within each semester. Additional sessions may be scheduled upon student request.
Students with Specific Learning Disabilities (SLD) and/or disabilities should contact the instructor via email at least 15 days before the scheduled exam date to arrange individualized accommodations. The email should also CC the respective University Services: [email protected] (for students with SLD); [email protected] (for students with disabilities).
Problem-solving section (use of calculator and statistical tables), open-ended questions, True/False statements, and text completion related to theoretical knowledge (80 minutes - 17 points).
Computer-based task using Design Expert or equivalent software, including a related commentary (40 minutes - 9 points).
The evaluation of Innovative Technologies will be conducted through a research project (4 points), with topics and presentation dates agreed upon with the instructor.
The final grade, expressed in thirtieths, will be communicated via email. There are six annual exam sessions: two during each break between semesters and one during academic breaks within each semester. Additional sessions may be scheduled upon student request.
Students with Specific Learning Disabilities (SLD) and/or disabilities should contact the instructor via email at least 15 days before the scheduled exam date to arrange individualized accommodations. The email should also CC the respective University Services: [email protected] (for students with SLD); [email protected] (for students with disabilities).
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
Shifts:
Professor:
Hidalgo Vidal Alyssa Mariel
Turno 1
Professor:
Hidalgo Vidal Alyssa MarielTurno 2
Professor:
Hidalgo Vidal Alyssa MarielProfessor(s)
Reception:
By date