| Responsible Department | Department of Food Science | ||||||||||||||||||
| Earliest Possible Year | MSc. 1 year to MSc. 2 year | ||||||||||||||||||
| Duration | One block | ||||||||||||||||||
| Credits | 7.5 (ECTS) | ||||||||||||||||||
| Level of Course | MSc | ||||||||||||||||||
| Examination | Final 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 Teaching | Lectures (25%), exercises (25%), project work (50%) | ||||||||||||||||||
| Block Placement | Block 1 Week Structure: B | ||||||||||||||||||
| Language of Instruction | English | ||||||||||||||||||
| Optional Prerequisites | LLEK10246 | ||||||||||||||||||
| Restrictions | 50 | ||||||||||||||||||
| Course 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 MODDE and MATLAB 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 | |||||||||||||||||||
| The course introduces 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 MATLAB. 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 | |||||||||||||||||||
| Course Literature | |||||||||||||||||||
| Design and Analysis of Experiments, D.C. Montgomery, 6th edition, Wiley (2005) Supplemented by scientific papers, book chapters and course notes. | |||||||||||||||||||
| Course Coordinator | |||||||||||||||||||
| Franciscus Winfried J van der Berg, fb@life.ku.dk, Department of Food Science/Quality and Technology, Phone: 353-33545 | |||||||||||||||||||
| Study Board | |||||||||||||||||||
| Study Committee LSN | |||||||||||||||||||
| Work Load | |||||||||||||||||||
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