Design of Experiments
- https://www.uni-giessen.de/de/fbz/zentren/ggl/curriculum/Postdocs/workshops/2024/doe
- Design of Experiments
- 2024-09-17T08:30:00+02:00
- 2024-09-18T17:30:00+02:00
17.09.2024 08:30 bis 18.09.2024 17:30 (Europe/Berlin / UTC200)
online
Target group: Postdocs of the JLU
Dates
17 - 18 September, 2024
8:30 am - 5:30 pm
Location
online
Workshop Description
Have you ever wondered how to systematically plan and optimize your experiments to gain the most reliable and insightful results? Design of Experiments (DoE) is a powerful statistical tool used in research to identify and understand the relationship between different factors and their effects on responses. Whether you are new to the concept or looking to deepen your knowledge, this workshop will guide you through the essential principles and advanced techniques of DoE.
This intensive two-day course covers both fundamental principles and advanced methodologies of Design of Experiments (DoE). The workshop starts with an exploration of randomization, replicates and block building in the context of screening designs (e.g. two-level fractional factorial designs) that typically form the first step in optimization procedures to identify the significant factors from a large number of parameters. Participants will then delve into response surface methodologies, which are essential for the quantitative description and optimization of factors, enabling the prediction of optimal operation conditions. Approximately 50% of the course is dedicated to hands-on training with real life examples from biotechnological research as case studies.
Content & Objectives
The course is structured in these sections:
- Introduction one-factor-at-a-time (OFAT) vs DoE
- DoE – Terms and definitions
- Screening based on full and fractional factorial designs
- Application examples for screening with DoE
- Predictive models and response surfaces – part I
- Predictive models and response surfaces – part II
- Application examples for response surface methodologies
- Overview of additional design types – mixtures and combined designs
- Limits of DoE and descriptive models
- Q&A session with discussion
Typical learning outcomes are:
- You can select suitable design types for your research question
- You can set up designs with the necessary complexity and power
- You can identify corrupted data before model building
- You can analyze DoE data in a relevant software package
Trainer
Prof. Dr. Dr. Johannes Buyel
Johannes Buyel is a biotechnologist and bioprocess engineer by training. He has studied in Germany, Sweden and the USA with a focus on the production and purification of biopharmaceutical proteins. After 10 years as head of the Bioprocess Engineering department at Fraunhofer IME (Aachen, Germany), Johannes joined BOKU University in 2022 as Full Professor for Downstream Processing. He is using DoE since 2009 and providing trainings since 2013.
Registration
Sorry, Registration is already closed
Organizer
Postdoctoral Programme of the PCMO@GGL
Contact
Sabine Otto
Phone: 0641 99 47242
Email: postdocs