270059 Process Design of Experiments and Optimization

Details
Department of Food Science
Earliest Possible YearMSc. 1 year to MSc. 2 year
DurationOne block
 
Credits7.5 (ECTS)
Course LevelMSc
 
ExaminationFinal Examination

written examination and oral examination


All aids allowed

Description of Examination: The students will hand in a written group wise report on project work in due time before the individual oral examination. At the oral examination the students discusses the results from their projects and the theory taught in the course with the teachers and reviewer.

Weight: Oral examination in project report and curriculum 100%



7-point scale, internal examiner
 
Organisation of TeachingLectures (25%), exercises (25%), project work (50%)
 
Block PlacementBlock 1
Week Structure: A
 
Teaching LanguageEnglish
 
Mandatory Prerequisites270002 Advanced Chemometrics with MATLAB
 
Restrictions50
 
Areas of Competence the Course Will Address
The student will understand the basic and advanced methods on the design and analysis of experimental data, statistical inference and experimental domain optimization. The student will be able to apply principles on similar problems in research and the process industry.

The student will understand the application of MODDE and JMP for performing data analysis.

The student will learn to
- perform statistical inference,
- use (fractional) factorial design,
- computer generated design,
- do quality by Design,
- evolving operation,
- process/product optimization,
- measurement optimization, and
- optimization towards process robustness.

 
Course Objectives
The course will introduce the student to advanced design of experiment methods with focus on Process Analytical Technological (PAT) relevance. The software packages used throughout the course are MODDE and JMP.
 
Course Contents
Basic design of experiments is an essential part of any scientific investigation. As such statistical process design, monitoring and control are an integral part of the PAT concept. In this course the connection between theory and production process practice will be at the forefront.

The methods studied in this course will vary from year to year but each year the main topics are: statistical inference, (fractional) factorial design, computer generated design, Quality by Design, evolving operation, process/product optimization, measurement optimization, and optimization towards process robustness.

Computer exercises of simulated and real data using MODDE and JMP are an integrated part of the course. The student will receive an introduction to both programs.

It is expected that the student have competences corresponding to a course in basic statistics and the course Advanced Chemometrics with Matlab.

 
Teaching And Learning Methods
The students will be introduced to the theory through lectures. The students will work individually and in groups on a data analytical problem using the taught concepts and software to analyze a problem. The results are formulated in a written report which is orally presented at a seminar at the end of the course.
 
Learning Outcome
Stipulated in "Areas of Competence the Course Will Address"
 
Course Litterature
Design and Analysis of Experiments, D.C. Montgomery, 6th edition, Wiley (2005)
Supplemented by scientific papers, book chapters and course notes.
 
Course Coordinator
Frans W.J. van den Berg, fb@life.ku.dk, Department of Food Science/Quality and Technology, Phone: 35333545
 
Study Board
Study Committee LSN
 
Course Scope
lectures40
theoretical exercises40
Colloquia0
project work70
Excursions0
preparation48
examination8

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