LLEK10294U Design of Experiments and Optimization

Volume 2019/2020
Education

MSc Programme in Food Science and Technology
MSc Programme in Food Innovation and Health
MSc Programme in Chemistry

Content

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 JMP are an integrated part of the course. The student will receive an introduction to both programs.

Learning Outcome

The course introduces the student to advanced design of experiment methods with focus on Process Analytical Technological (PAT) relevance. The software package used throughout the course are JMP.

After completing the course the student should be able to:

Knowledge

  • Summarize basic and advanced design of experiment methods
  • Summarize basic and advanced process optimization methods
  • Summarize basic and advanced statistical process control methods


Skills

  • Perform statistical inference
  • Use (fractional) factorial design, advanced design methods and computer generated designs
  • Analyze experimental design data


Competences

  • Use and perform Quality by Design
  • Use and perform process/product optimization methods
  • Use and perform measurement optimization
  • Use and perform optimization towards process robustness
Literature

See Absalon for a list of course literature

It is expected that the student have competences corresponding to a course in basic statistics

Academic qualifications equivalent to a BSc degree is recommended.
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.
  • Category
  • Hours
  • Exam
  • 1
  • Lectures
  • 40
  • Preparation
  • 55
  • Project work
  • 70
  • Theory exercises
  • 40
  • Total
  • 206
Written
Oral
Individual
Collective
Continuous feedback during the course of the semester
Credit
7,5 ECTS
Type of assessment
Oral examination, 20 min
The students will hand in a written group report on project work. At the individual oral examination the students discuss the results from their projects and the curriculum of the course with the examiners. No preparation time.

Weight: Oral examination in project report and curriculum 100%
Exam registration requirements

Hand in of written group report on project work. 

Aid
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners
Re-exam

Same as ordinary exam.

Possibility to re-submit project report two weeks before the date of the re-examination. 

Criteria for exam assesment

See Learning Outcome.