Design of Experiments (DOEs) and Process Optimization in Bioproduction
This module will show students who previously obtained experience in laboratory experimental work how statistical design techniques can be used to rapidly understand and optimize the bioprocess performance.
It provides students with insights on how experimental design and small scall experimental models can underpin quality by design approaches. The module includes both the theoretical framework of statistics and hands on use of DoE software with the bioprocess case studies from industrial manufacturing of chemicals, materials, drugs, food, and textile.
General study skills as well
MAT1014 Mathematics or equivalent
STT1001 Statistics or equivalent
English level B1
Knowledge
The course will provide students with the knowledge about:
- Understand the practical bioprocess challenges, its implementation, and potential solutions using the industrial design of experiments
- Broad knowledge and understanding of the concepts and data interpretation used in the design of experiments, differences in solutions using optimal (good designs) from the less successful
- Choose the most optimal statistic concept (factorial, fractional factorial, response surface design concepts) for design and analysis of experiments, and optimization of the bioprocesses
- Delivery and communication of data analysis results from practical experiments using statistical software to broad auditorium
- Rapidly screen and optimise different media and operating conditions in chemical and biochemical processing
- Identify critical and non-critical process parameters
Skills
The course should enable students to:
- Use of JMP software for DoE and data visualization for the selected case studies on process optimization and quality control in bioproduction industries
- Analyse the experimental data with the confidence using the basic statistical concepts required for design of experiments (DoE)
- Interpret the output of experimental design software - for example, contour and residual analysis plots, ANOVA tables for factorial or surface response models, normal probability
- Data processing & visualization
General Competencies
After competition of this course, students will be able to:
- Ensure analytical methods can be validated and provide reliable data for process evaluation
- Understand the relationship between design of experiments and process quality development
- Present and discuss the results based on charts, contour plots, and tables
- Network with sector leaders and subject matter experts
Elective for students in the 6th semester of the BSc in Sustainable Economy and Bioproduction and Bachelor in Biology. Mandatory course in semesterpackage Biorefinary. The course can be offered to single course students, but be aware of the course's prerequisite.
Lectures will be in English.
Compound assessment:
- Obligatory participation (OD) in course activities (to get approved at least 80 % attendance), approved / not approved;
- 5 Class exercises (AK), (all exercises must be completed) approved / not approved
- 100 % - "Mappe" (assignment with the calculation, laboratory, literature recherche, report) - Topics will be given during the first lecture to solve individually or in groups. 100 %. Grades A-F
Practical experience with various processes, e.g., experimental work, laboratory, field studies, manufacturing or pilot plant-related research
Matematikk og statistikk: STT1001, MAT1014 or equivalent.